Example 1:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Crucial problems in U.S. politics as of 2018
X_Axis['Response']: ['Immigration', 'Race_relations/_racism', 'Donald_Trump', 'Parties/_gridlock/_divisions_in_country', 'Economy_(general)_health_care/costs/accessibility', 'Unemployment/_lack_of_jobs', 'Distribution_of_wealth_/_Inequality', 'Education/_schools/_affording_education', 'Foreign_policy_(general)', 'Crime/violence/judicial_system', 'Morality/_ethics/_family_values', 'Gun_control/_too_many_guns', 'Lack_of_humanity/_respect', 'Environment/_global_warming', 'Dissatisfaction_with_government/_politics/_corruption', 'Defense/_national_security']
Y_Axis['Share', 'of', 'respondents']: ['12', '8', '8', '7', '6', '4', '4', '4', '4', '3', '3', '3', '3', '2', '2', '2']

gold: In this 2018 survey , eight percent of respondents stated they think the biggest problem for the United States today is Donald Trump . In fact , most of the important problems mentioned are somehow related to the current POTUS – the most mentioned one being immigration and racism . A country divided Since Trump 's inauguration in January 2017 , political camps in the United States are deeply divided and global politics is in turmoil ; Trump 's job approval ratings are notoriously low , and in fact , they are lower than Reagan 's or Nixon 's averages ever were .
gold_template: In this templateTitleDate[0] survey , templateYValue[1] templateScale of templateYLabel[1] stated they think the biggest problem for the templateTitle[2] today is templateXValue[2] . In fact , most of the important templateTitle[1] mentioned are somehow related to the current POTUS – the most mentioned one being templateXValue[0] and templateXValue[1] . A templateXValue[3] divided Since templateXValue[2] 's inauguration in 2017 , political camps in the templateTitle[2] are deeply divided and templateXValue[13] templateTitle[3] is in turmoil ; templateXValue[2] 's job approval ratings are notoriously low , and in fact , they are lower than Reagan 's or Nixon 's averages ever were .

generated_template: This statistic shows the results of a survey about the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] in templateTitleSubject[1] as of 2018 . The survey found that templateXValue[0] was templateTitleSubject[1] 's templateTitle[0] loved templateTitleSubject[0] princess with templateYValue[min] out of templateYValue[max] British adults choosing her as their favorite . templateXValue[1] and templateXValue[2] were second and third templateTitle[0] templateTitle[1] at templateYValue[2] and templateYValue[1] templateScale .
generated: This statistic shows the results of a survey about the Crucial problems U.S. politics in U.S. as of 2018 .  The survey found that Immigration was U.S. 's Crucial loved U.S. princess with 2 out of 12 British adults choosing her as their favorite .  Race_relations/_racism and Donald_Trump were second and third Crucial problems at 8 and % .

Example 2:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Survey on regretting tattoos in the U.S. 2015
X_Axis['Response']: ['Yes', 'No']
Y_Axis['Share', 'of', 'respondents']: ['23', '77']

gold: This statistic shows the results of a survey among adult Americans who have one or more tattoos . The survey was conducted in 2015 , asking the respondents whether they ever regret getting any of their tattoos , or not . 77 percent of respondents stated they do not regret getting any of their tattoos .
gold_template: This statistic shows the results of a templateTitle[0] among adult Americans who have one or more templateTitle[2] . The templateTitle[0] was conducted in templateTitleDate[0] , asking the templateYLabel[1] whether they ever regret getting any of their templateTitle[2] , or not . templateYValue[max] templateScale of templateYLabel[1] stated they do not regret getting any of their templateTitle[2] .

generated_template: In templateTitleDate[0] , some templateYValue[0] templateScale of templateYLabel[1] stated they will probably templateTitle[4] templateTitle[5] on templateTitle[6] templateTitle[7] templateTitle[8] that year , while almost half of all templateYLabel[1] stated they probably wo n't . Is romance really dead ? templateTitle[6] templateTitle[7] templateTitle[8] is generally considered the most romantic templateTitle[8] of the year , which people use to give flowers and tokens of appreciation to loved ones . About half of the American population celebrate templateTitle[6] templateTitle[7] templateTitle[8] , but this fact does templateXValue[last] infer that the same templateYLabel[0] of the population is taken or in love ; in fact , a certain templateYLabel[0] of templateYLabel[1] rather bought templateTitle[6] templateTitle[7] presents for their pets last year rather than a significant other , and another significant templateYLabel[0] of templateYLabel[1] spent the templateTitle[8] alone , with colleagues , friends , or with family , rather than with a spouse or a partner .
generated: In 2015 , some 23 % of respondents stated they will probably 2015 on that year , while almost half of all respondents stated they probably wo n't .  Is romance really dead ? 2015 is generally considered the most romantic 2015 of the year , which people use to give flowers and tokens of appreciation to loved ones .  About half of the American population celebrate 2015 , but this fact does No infer that the same Share of the population is taken or in love ; in fact , a certain Share of respondents rather bought 2015 presents for their pets last year rather than a significant other , and another significant Share of respondents spent the 2015 alone , with colleagues , friends or with family , rather than with a spouse or a partner .

Example 3:
titleEntities: {'Subject': ['Russia'], 'Date': ['2018', '2018']}
title: Average player height of participating national teams at the 2018 World Cup in Russia
X_Axis['Country']: ['Serbia', 'Sweden', 'Iceland', 'Denmark', 'Croatia', 'Russia', 'Tunisia', 'Germany', 'Belgium', 'Senegal', 'Switzerland', 'Iran', 'Poland', 'Morocco', 'South_Korea', 'England', 'Costa_Rica', 'Australia', 'Nigeria', 'Panama', 'Portugal', 'France', 'Brazil', 'Colombia', 'Egypt', 'Uruguay', 'Mexico', 'Spain', 'Argentina', 'Peru', 'Japan', 'Saudi_Arabia']
Y_Axis['Average', 'player', 'height', 'in', 'centimeters']: ['185.6', '185.2', '185.0', '185.0', '184.9', '184.3', '184.0', '183.8', '183.8', '183.7', '183.5', '183.4', '183.1', '182.4', '182.2', '182.1', '181.6', '181.3', '181.2', '181.1', '180.5', '180.5', '180.4', '180.2', '180.1', '179.8', '179.5', '179.5', '179.4', '178.3', '178.1', '176.2']

gold: While they may not have made it out of the group stages of the 2018 World Cup , the Serbian national team were top of the table when it came to the average height of their players – their squad boasted an average height of 185.6 centimeters per player . The first-choice goalkeeper for Serbia , Vladimir Stojković , was one of the tallest members of the team at 195 centimeters . At the other end of the scale , the squad of Saudi Arabia came in at an average of just 176.2 centimeters , making them the shortest squad at the 2018 World Cup .
gold_template: While they may not have made it out of the group stages of the templateTitleDate[0] templateTitle[7] templateTitle[8] , the Serbian templateTitle[4] team were top of the table when it came to the templateYLabel[0] templateYLabel[2] of their players – their squad boasted an templateYLabel[0] templateYLabel[2] of templateYValue[max] templateYLabel[3] per templateYLabel[1] . The first-choice goalkeeper for templateXValue[0] , Vladimir Stojković , was one of the tallest members of the team at 195 templateYLabel[3] . At the other end of the scale , the squad of templateXValue[last] came in at an templateYLabel[0] of just templateYValue[min] templateYLabel[3] , making them the shortest squad at the templateTitleDate[0] templateTitle[7] templateTitle[8] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] in templateTitle[3] as of 2018 , sorted by templateXLabel[0] . During that period of templateTitleDate[0] , templateXValue[1] had a templateYLabel[0] of templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Average of Russia player in participating as of 2018 , sorted by Country .  During that period of 2018 , Sweden had a Average of 185.2 player height .

Example 4:
titleEntities: {'Subject': ['Christian'], 'Date': ['2010']}
title: Largest U.S. Christian denominations 2010 , by number of adherents
X_Axis['Christian', 'grouping']: ['Catholic', 'Evangelical_and_Conservative_Protestant', 'Mainline_Protestant', 'Latter-day_Saints', 'Black_Protestant', 'Orthodox_Christian', 'Other_Faiths', 'Total']
Y_Axis['Number', 'of', 'followers']: ['58928987', '50013803', '22655826', '6267771', '4877067', '1056535', '6667542', '150686156']

gold: This graph shows the largest Christian denominations in the United States in 2010 , by number of adherents . In 2010 , the Lattar-day Saints were among the largest Christian groups with about 6.3 million adherents in the United States .
gold_template: This graph shows the templateTitle[0] templateXValue[5] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[5] templateYLabel[0] of templateTitle[7] . In templateTitleDate[0] , the Lattar-day templateXValue[3] were among the templateTitle[0] templateXValue[5] groups with about templateYValue[3] templateScale templateTitle[7] in the templateTitle[1] .

generated_template: The statistic presents the templateTitle[0] templateTitleSubject[0] templateYLabel[0] templateTitle[3] templateTitle[4] templateTitle[6] templateTitle[7] of 2017 . As of the fourth quarter of templateTitleDate[0] , it was found that templateYValue[max] templateScale of the templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] .
generated: The statistic presents the Largest Christian Number denominations 2010 number adherents of 2017 .  As of the fourth quarter of 2010 , it was found that 150686156 % of the number adherents .

Example 5:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016', '2016']}
title: Most googled holiday keywords in the United Kingdom ( UK ) in January 2016
X_Axis['Keyword']: ['Cheap_holidays', 'Holidays', 'City_breaks', 'All_inclusive_holidays', 'Sun_holidays', 'Weekend_breaks', 'Last_minute_holdiays', 'Holiday', 'Last_minute_holiday_deals', 'Ski_holdiays', 'Other_keywords']
Y_Axis['Searches', 'in', 'thousands']: ['550.0', '450.0', '465.0', '165.0', '135.0', '110.0', '110.0', '90.5', '74.0', '74.0', '5633.69']

gold: This statistic displays the most googled holiday-related keywords in the United Kingdom during January 2016 , ranked by search volume . That month `` cheap holidays '' was searched 550 thousand times on Google UK .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] holiday-related templateXValue[last] in the templateTitleSubject[0] during templateTitle[7] templateTitleDate[0] , ranked by search volume . That month `` templateXValue[0] '' was searched templateYValue[0] thousand times on Google templateTitleSubject[1] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of time .
generated: This statistic shows the Searches of adults in the United Kingdom who were using Most as of 2019 , sorted UK Keyword .  In 2016 , around 5633.69 thousands of time .

Example 6:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. fire statistics : number of false alarms , by type 2018
X_Axis['Type', 'of', 'call']: ['Unintentional_call', 'System_malfunction', 'Other_false_alarms_(bomb_scares_etc.)', 'Malicious_mischievous_false_call']
Y_Axis['Number', 'of', 'calls', 'responded', '(in', 'thousands)']: ['1378.5', '888.5', '450.5', '171.5']

gold: This statistic shows total number of false alarms responded by fire departments in the United States in 2018 . In 2018 , U.S. fire departments responded to a total of 2,889,000 false alarms . Malicious false calls increased by 22 % from 2017 , accounting for 171,500 of all false calls .
gold_template: This statistic shows total templateYLabel[0] of templateXValue[2] responded templateTitle[6] templateTitle[1] departments in the templateTitle[0] in templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] templateTitle[1] departments templateYLabel[2] to a total of 2,889,000 templateXValue[2] . templateXValue[last] templateXValue[2] templateYLabel[1] templatePositiveTrend templateTitle[6] 22 templateScale from 2017 , accounting for templateYValue[min] of all templateXValue[2] templateYLabel[1] .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[6] templateTitle[7] in templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] templateXValue[4] departments templateYLabel[2] to templateYValue[2] templateXValue[2] . This was an templatePositiveTrend of 13 templateScale from the previous year .
generated: This statistic shows the total Number of calls responded (in in the by type in 2018 .  In 2018 , U.S. Malicious_mischievous_false_call departments responded to 450.5 Other_false_alarms_(bomb_scares_etc.) This was an increase of 13 % from the previous year .

Example 7:
titleEntities: {'Subject': ['Washington'], 'Date': ['2016']}
title: Number of registered automobiles in Washington 2016
X_Axis['Autmobile', 'type']: ['Publicly_owned', 'Private_and_commercial_(including_taxicabs)', 'Total']
Y_Axis['Number', 'of', 'registered', 'automobiles']: ['72003', '2863653', '2935656']

gold: This statistic represents the number of registered automobiles in Washington in 2016 . In that year , there were around 2.86 million private and commercial automobiles ( including taxicabs ) registered in Washington .
gold_template: This statistic represents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateTitleDate[0] . In that year , there were around templateYValue[1] templateScale templateXValue[1] and templateXValue[1] templateYLabel[2] ( including taxicabs ) templateYLabel[1] in templateTitleSubject[0] .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who were using templateTitleSubject[0] as of templateTitleDate[0] . During a survey period of time , it was found that templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic presents the percentage of online consumers in the Number who were using Washington as of 2016 .  During a survey period of time , it was found that 2935656 % automobiles .

Example 8:
titleEntities: {'Subject': ['EIFS', 'STUCCO'], 'Date': ['2018']}
title: Most used EIFS and STUCCO brands in the U.S. 2018
X_Axis['Brand']: ['Dryvit', 'STO', 'Omega_Products', 'Senergy', 'Simplex_(Finestone)', 'Parex/La_Habra', 'TEC', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['26.9', '11.5', '6.7', '5.8', '5.8', '4.8', '3.8', '34.6']

gold: This statistic depicts the most used EIFS and STUCCO siding brands by U.S. construction firms in 2018 . The survey revealed that 26.9 percent of the respondents used Dryvit brand the most .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitleSubject[0] and templateTitleSubject[1] siding templateTitle[4] by templateTitle[5] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[0] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic displays a rankings of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of 2018 . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] named watching templateXValue[0] ( DuPont ) .
generated: This statistic displays a rankings of the used EIFS STUCCO brands U.S. 2018 as of 2018 .  The survey revealed that 34.6 % of the respondents named watching Dryvit ( DuPont ) .

Example 9:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2018', '2018']}
title: Leading video gaming brands on Twitter 2018 , by followers
X_Axis['Brand']: ['PlayStation', 'Xbox', 'Nintendo_of_America', 'Rockstar_Games', 'Ubisoft', 'EA_SPORTS_FIFA', 'Sonic_the_Hedgehog', 'Fortnite', 'Electronic_Arts', 'League_of_Legends']
Y_Axis['Brand', 'fans', 'in', 'millions']: ['15.63', '12.87', '9.44', '9.31', '7.11', '6.4', '5.76', '5.64', '5.19', '4.36']

gold: This statistic gives information on the most popular gaming brands on Twitter , ranked by number of followers on the social network . As of September 2018 , Sony 's PlayStation was ranked first with 15.63 million Twitter followers . Ubisoft was ranked fifth with 7.1 million followers .
gold_template: This statistic gives information on the most popular templateTitle[2] templateTitle[3] on templateTitleSubject[0] , ranked templateTitle[6] number of templateTitle[7] on the social network . As of 2018 , Sony 's templateXValue[0] was ranked first with templateYValue[max] templateScale templateTitleSubject[0] templateTitle[7] . templateXValue[4] was ranked fifth with templateYValue[4] templateScale templateTitle[7] .

generated_template: This statistic gives information on the most popular templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) as of templateTitleDate[0] . As of templateTitleDate[0] , templateXValue[0] was ranked by around templateYValue[max] templateScale templateYLabel[2] .
generated: This statistic gives information on the most popular video gaming brands in the United Kingdom ( Twitter ) as of 2018 .  As of 2018 , PlayStation was ranked by around 15.63 millions .

Example 10:
titleEntities: {'Subject': ['Dublin'], 'Date': ['2019']}
title: Prime office rental prices in Dublin Q1 2015-Q3 2019
X_Axis['Quarter']: ["Q1_'15", "Q2_'15", "Q3_'15", "Q4_'15", "Q1_'16", "Q2_'16", "Q3_'16", "Q4_'16", "Q1_'17", "Q2_'17", "Q3_'17", "Q4_'17", "Q1_'18_", "Q2_'18", "Q3_'18", "Q4_'18", "Q1_'19", "Q2_'19", "Q3_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['538', '552', '592', '619', '619', '619', '646', '646', '646', '646', '673', '673', '-', '673', '673', '-', '673', '673', '673']

gold: The statistic displays the rental costs per square meter of prime office spaces in Dublin , Ireland , from the first quarter of 2015 to the third quarter of 2019 . It can be seen that the price of prime office properties in Dublin grew over time , reaching 673 euros per square meter per year as of the 3rd quarter of 2017 and remaining constant up until the third quarter 2019 .
gold_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] , Ireland , from the first templateXLabel[0] of 2015 to the third templateXLabel[0] of templateTitleDate[0] . It can be seen that the price of templateTitle[0] templateTitle[1] properties in templateTitleSubject[0] templatePositiveTrend over time , reaching templateYValue[last] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year as of the 3rd templateXLabel[0] of 2017 and remaining constant up until the third templateXLabel[0] templateTitleDate[0] .

generated_template: This statistic gives information on the templateTitle[2] number of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] 's templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[min] to the first templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , the website 's templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic gives information on the rental number of per square meter euros in Dublin 's per from the first Quarter of 2019 to the first Quarter of 2019 .  In the fourth Quarter of 2019 , the website 's per amounted to 538 million meter per square meter of euros per square meter .

Example 11:
titleEntities: {'Subject': ['Ultra'], 'Date': ['2019']}
title: Ultra high net worth individuals - distribution by region 2019
X_Axis['Region']: ['North_America', 'Europe', 'Asia-Pacific', 'China', 'Latin_America', 'India', 'Africa']
Y_Axis['Number', 'of', 'UHNW', 'individuals']: ['84054', '33551', '22657', '18132', '4460', '4376', '804']

gold: This statistic shows the regional distribution of ultra high net worth individuals in 2019 . About 84,054 individuals with net assets of at least 50 million U.S. dollars were residing in North America in 2019 . That is about 50 percent of the total number of UHNW individuals worldwide .
gold_template: This statistic shows the regional templateTitle[5] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitleDate[0] . About templateYValue[max] templateYLabel[2] with templateTitle[2] assets of at least 50 templateScale U.S. dollars were residing in templateXValue[0] in templateTitleDate[0] . That is about 50 templateScale of the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2018 , sorted by templateXLabel[0] . During that period of time , templateXValue[0] was ranked first with an average templateYLabel[0] of templateYValue[max] templateScale of the app templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of Ultra high net worth as of 2018 , sorted by Region .  During that period of time , North_America was ranked first with an average Number of 84054 % of the app individuals .

Example 12:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. consumers who have personally experienced hacking 2018
X_Axis['Response']: ['Yes_more_than_once', 'Yes_but_only_once', 'No_never', "Don't_know_/_can't_recall"]
Y_Axis['Share', 'of', 'respondents']: ['14', '22', '51', '12']

gold: This statistic presents the share of internet users in the United States who have ever had any of their online accounts hacked . During the October 2018 survey , 14 percent of respondents stated that their online accounts had been hacked more than once .
gold_template: This statistic presents the templateYLabel[0] of internet users in the templateTitle[0] templateTitle[2] templateTitle[3] ever had any of their online accounts hacked . During the October templateTitleDate[0] survey , templateYValue[0] templateScale of templateYLabel[1] stated that their online accounts had been hacked templateXValue[0] once .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] templateScale of female students in the templateTitle[1] were templateXValue[0] with templateXValue[2] during templateXValue[last] .
generated: This statistic shows the results of a survey among female U.S. high school students have personally experienced hacking .  According to the source , 51 % of female students in the consumers were Yes_more_than_once with No_never during Don't_know_/_can't_recall .

Example 13:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. exports - top trading partners 2018
X_Axis['Country']: ['Canada', 'Mexico', 'China', 'Japan', 'United_Kingdom', 'Germany', 'Korea_South', 'Netherlands', 'Brazil', 'Hong_Kong', 'France', 'Singapore', 'India', 'Belgium', 'Taiwan']
Y_Axis['Export', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['298.7', '265.0', '120.3', '75.0', '66.2', '57.7', '56.3', '49.4', '39.5', '37.5', '36.3', '33.1', '33.1', '31.4', '30.2']

gold: This graph shows the largest trading partners for the United States in 2018 , by value of exported trade goods . In 2018 , the United States exported trade goods worth about 39.5 billion U.S. dollars to Brazil .
gold_template: This graph shows the largest templateTitle[3] templateTitle[4] for the templateXValue[4] States in templateTitleDate[0] , by templateYLabel[1] of exported trade goods . In templateTitleDate[0] , the templateXValue[4] States exported trade goods worth about templateYValue[8] templateScale templateYLabel[3] templateYLabel[4] to templateXValue[8] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , broken down templateTitle[4] templateXLabel[0] . According to the source , templateXValue[4] had a templateYLabel[0] of templateYValue[4] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Export of the U.S. exports top trading in 2018 , broken down partners Country .  According to the source , United_Kingdom had a Export of 66.2 value billion .

Example 14:
titleEntities: {'Subject': ['Economic'], 'Date': ['2016']}
title: Economic loss due to major droughts worldwide up to 2016
X_Axis['Drought']: ['United_States_June_2012', 'China_P_Rep_January_1994', 'United_States_January_2011', 'Australia_1981', 'Brazil_January_2014', 'Spain_September_1990', 'China_P_Rep_October_2009', 'United_States_July_2002', 'Iran_Islam_Rep_April_1999', 'Spain_April_1999']
Y_Axis['Economic', 'loss', 'in', 'billion', 'U.S.', 'dollars']: ['20.0', '13.8', '8.0', '6.0', '5.0', '4.5', '3.6', '3.3', '3.3', '3.2']

gold: This statistic shows the economic harm as a result of droughts in certain countries in the period from 1900 to 2016  . The drought in China in 1994 caused an economic loss of almost 13.8 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] harm as a result of templateTitle[4] in certain countries in the period from 1900 to templateTitleDate[0] . The templateXLabel[0] in templateXValue[1] in templateXValue[1] caused an templateYLabel[0] templateYLabel[1] of almost templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[6] as of October templateTitleDate[0] . As of the fourth templateXLabel[0] of templateTitleDate[0] , templateXValue[0] had an average of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Economic loss of due major droughts up as of October 2016 .  As of the fourth Drought of 2016 , United_States_June_2012 had an average of 20.0 billion U.S. dollars .

Example 15:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2010', '2019']}
title: Twitter : number of monthly active users 2010 to 2019
X_Axis['Quarter']: ["Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['330.0', '321.0', '326.0', '335.0', '336.0', '330.0', '330.0', '326.0', '327.0', '318.0', '317.0', '313.0', '310.0', '305.0', '307.0', '304.0', '302.0', '288.0', '284.0', '271.0', '255.0', '241.0', '231.7', '218.0', '204.0', '185.0', '167.0', '151.0', '138.0', '117.0', '101.0', '85.0', '68.0', '54.0', '49.0', '40.0', '30.0']

gold: How many people use Twitter ? As of the first quarter of 2019 , Twitter averaged 330 million monthly active users , a decline from its all-time high of 336 MAU in the first quarter of 2018 . As of the first quarter of 2019 , the company switched its user reporting metric to monetizable daily active users ( mDAU ) . Twitter Twitter is a social networking and microblogging service , enabling registered users to read and post short messages called tweets .
gold_template: How many people use templateTitleSubject[0] ? As of the first templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] averaged templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] , a decline from its all-time high of templateYValue[max] MAU in the first templateXLabel[0] of 2018 . As of the first templateXLabel[0] of templateTitleDate[max] , the company switched its user reporting metric to monetizable daily templateYLabel[2] templateYLabel[3] ( mDAU ) . templateTitleSubject[0] is a social networking and microblogging service , enabling registered templateYLabel[3] to read and post short messages called tweets .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] as of the first templateXLabel[0] of templateTitleDate[max] . Excluding the country , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] amounted to templateYValue[0] templateScale as of the most recent templateXLabel[0] . In total , templateTitleSubject[0] had 330 templateScale global templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows a timeline with the amount of monthly active users Twitter as of the first Quarter of 2019 .  Excluding the country , the Number of monthly active users Twitter amounted to 330.0 millions as of the most recent Quarter .  In total , Twitter had 330 millions global monthly active users .

Example 16:
titleEntities: {'Subject': ['Percentage'], 'Date': ['2007']}
title: Percentage of population with a university degree , by country 2007
X_Axis['Country']: ['Canada', 'New_Zealand', 'Japan', 'United_States', 'Korea_Republic_of', 'Norway', 'Australia', 'Ireland', 'United_Kingdom', 'Denmark', 'Sweden', 'Netherlands', 'Spain', 'France', 'Germany', 'Austria', 'Mexico', 'Portugal', 'Italy']
Y_Axis['Percent', 'with', 'a', 'university', 'degree']: ['48', '41', '41', '40', '35', '34', '34', '32', '32', '32', '31', '31', '29', '27', '24', '18', '15', '14', '13']

gold: In 2007 , Canada had the highest share of adults with a university degree , at 48 percent of those between the ages of 25 and 64 . Italy had the least amount of people with a university degree , at 13 percent of people between the ages of 25 and 64 . University around the world Deciding which university to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .
gold_template: In templateTitleDate[0] , templateXValue[0] had the highest share of adults templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[max] templateScale of those between the ages of 25 and 64 . templateXValue[last] had the least amount of people templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[min] templateScale of people between the ages of 25 and 64 . templateYLabel[2] around the world Deciding which templateYLabel[2] to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] worldwide as of 2019 , sorted templateTitle[6] templateXLabel[0] . During the survey period , templateXValue[1] had the highest templateYLabel[0] of templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .
generated: This statistic shows the Percent of Percentage university degree worldwide as of 2019 , sorted 2007 Country .  During the survey period , New_Zealand had the highest Percent of 35 university degree worldwide .

Example 17:
titleEntities: {'Subject': ['ITV', 'UK'], 'Date': ['2019']}
title: ITV viewers reached quarterly in the UK Q1 2012-Q3 2019
X_Axis['Quarter']: ['Q1_2012', 'Q2_2012', 'Q3_2012', 'Q4_2012', 'Q1_2013', 'Q2_2013', 'Q3_2013', 'Q4_2013', 'Q1_2014', 'Q2_2014', 'Q3_2014', 'Q4_2014', 'Q1_2015', 'Q2_2015', 'Q3_2015', 'Q4_2015', 'Q1_2016', 'Q2_2016', 'Q3_2016', 'Q4_2016', 'Q1_2017', 'Q2_2017', 'Q3_2017', 'Q4_2017', 'Q1_2018', 'Q2_2018', 'Q3_2018', 'Q4_2018', 'Q1_2019', 'Q2_2019', 'Q3_2019']
Y_Axis['Viewers', 'in', 'thousands']: ['55196', '55302', '54443', '55327', '55277', '55014', '54013', '55559', '55443', '55628', '54636', '55195', '53997', '53716', '53457', '54227', '53928', '54201', '52843', '54150', '53038', '51936', '51546', '53303', '52906', '51628', '47762', '48375', '48236', '46376', '45308']

gold: This statistic shows the quarterly reach of the ITV television channel in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the quarter ending September 2014 , ITV reached 54.64 million viewers . The number of viewers fell to roughly 45.3 million in the last period in consideration .
gold_template: This statistic shows the templateTitle[3] reach of the templateTitleSubject[0] television channel in the United Kingdom ( templateTitleSubject[1] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the templateXLabel[0] ending 2014 , templateTitleSubject[0] templateTitle[2] templateYValue[10] templateScale templateYLabel[0] . The number of templateYLabel[0] templateNegativeTrend to roughly templateYValue[min] templateScale in the last period in consideration .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitle[0] 's templateTitle[1] templateYLabel[2] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[2] .
generated: This statistic shows the ITV viewers reached of ITV from the first Quarter of 2019 to the fourth Quarter of Q1_2012 .  In the fourth Quarter of 2019 , ITV 's viewers thousands amounted to 55628 thousands .

Example 18:
titleEntities: {'Subject': ['San Jose'], 'Date': ['2019']}
title: Player expenses ( payroll ) of San Jose Earthquakes 2019
X_Axis['Month']: ['Valeri_"Vako"_Qazaishvili', 'Chris_Wondolowski', 'Florian_Jungwirth', 'Guram_Kashia', 'Cristian_Espinoza', 'Danny_Hoesen', 'Anibal_Godoy', 'Magnus_Eriksson', 'Marcos_Lopez_Lanfranco', 'Harold_Cummings', 'Judson', 'Francois_Affolter', 'Shea_Salinas', 'Andrew_Tarbell', 'Nick_Lima', 'Daniel_Vega', 'Jackson_Yueill', 'Tommy_Thompson', 'JT_Marcinkowski', 'Eric_Calvillo', 'Siad_Haji', 'Gilbert_Fuentes', 'Matt_Bersano', 'Luis_Felipe', 'Jimmy_Ockford', 'Cade_Cowell', 'Jacob_Akanyirige', 'Kevin_Partida', 'Paul_Marie']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['1604.04', '800.0', '616.68', '590.0', '550.0', '549.67', '498.13', '450.0', '387.75', '320.67', '305.0', '273.0', '250.0', '235.0', '218.44', '210.0', '190.0', '175.0', '147.0', '135.0', '104.0', '92.0', '71.67', '71.63', '70.88', '67.23', '64.23', '57.23', '57.23']

gold: The statistic shows the player expenses ( payroll ) of the San Jose Earthquakes club of Major League Soccer by player in 2019 . Valeri `` Vako '' Qazaishvili received a salary of 1.6 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] Earthquakes club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] `` Vako '' templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[1] Santos received a salary of templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in that year .
generated: The statistic shows the Player expenses ( Payroll ) of the San Jose club of Major League Soccer by Player in 2019 .  Chris_Wondolowski Santos received a salary of 800.0 thousand U.S. dollars in that year .

Example 19:
titleEntities: {'Subject': ['Oakland Athletics'], 'Date': ['2019']}
title: Oakland Athletics all-time home run leaders 2019
X_Axis['Month']: ['Mark_McGwire', 'Jimmie_Foxx', 'Reggie_Jackson', 'Jose_Canseco', 'Bob_Johnson', 'Eric_Chavez', 'Al_Simmons', 'Jason_Giambi', 'Sal_Bando', 'Gus_Zernial']
Y_Axis['Number', 'of', 'home', 'runs']: ['363', '302', '269', '254', '252', '230', '209', '198', '192', '191']

gold: This statistic shows the Oakland Athletics all-time home run leaders as of October 15 , 2019 . Mark McGwire has hit the most home runs in Oakland Athletics franchise history with 363 home runs .
gold_template: This statistic shows the templateTitleSubject[0] all-time templateYLabel[1] templateTitle[4] templateTitle[5] as of October 15 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitleSubject[0] all-time templateYLabel[1] templateTitle[4] templateTitle[5] as of October 14 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Oakland Athletics all-time home run leaders as of October 14 , 2019 .  Mark_McGwire has hit the most home runs in Oakland Athletics franchise history with 363 home runs .

Example 20:
titleEntities: {'Subject': ['BuzzFeed'], 'Date': ['2015', '2016']}
title: BuzzFeed : monthly content views 2015 to 2016
X_Axis['Month']: ["Apr_'15", "Oct_'15", "May_'16"]
Y_Axis['Number', 'of', 'monthly', 'video', 'viewers', 'in', 'millions']: ['1000', '5000', '7000']

gold: This statistic presents the number of monthly BuzzFeed video viewers in 2013 and 2014 and as of May 2016 . In the most recent reported period , 7 billion viewers accessed content on the social sharing platform per month .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitleSubject[0] templateYLabel[2] templateYLabel[3] in 2013 and 2014 and as of templateXValue[last] templateTitleDate[max] . In the most recent reported period , templateYValue[max] templateScale templateYLabel[3] accessed templateTitle[2] on the social sharing platform per templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] who were using templateTitle[5] templateTitle[6] as of 2019 . As of that templateXLabel[0] , the mobile messaging app had been templateYLabel[1] templateYLabel[2] worldwide .
generated: This statistic shows the Number of monthly video in the BuzzFeed who were using 2016 as of 2019 .  As of that Month , the mobile messaging app had been monthly video worldwide .

Example 21:
titleEntities: {'Subject': ['Google'], 'Date': ['2003']}
title: Google : quarterly net income 2003 to 2015
X_Axis['Financial', 'Quarter/Year']: ["Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09", "Q4_'08", "Q3_'08", "Q2_'08", "Q1_'08", "Q4_'07", "Q3_'07", "Q2_'07", "Q1_'07", "Q4_'06", "Q3_'06", "Q2_'06", "Q1_'06", "Q4_'05", "Q3_'05", "Q2_'05", "Q1_'05", "Q4_'04", "Q3_'04", "Q2_'04", "Q1_'04", "Q4_'03", "Q3_'03", "Q2_'03", "Q1_'03"]
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['3979.0', '3931.0', '3515.0', '4675.0', '2739.0', '3351.0', '3371.0', '3324.0', '2921.0', '3376.0', '2970.0', '2886.0', '2176.0', '2785.0', '2890.0', '2705.0', '2729.0', '2505.0', '1798.0', '2543.0', '2167.0', '1840.0', '1955.0', '1974.0', '1439.0', '1485.0', '1423.0', '382.4', '1289.9', '1247.5', '1307.1', '1206.4', '1070.0', '925.1', '1002.2', '1030.72', '733.36', '721.08', '592.29', '372.21', '381.18', '342.81', '369.19', '204.1', '51.98', '79.06', '63.97', '27.25', '20.43', '32.17', '25.8']

gold: This timeline shows Google 's quarterly net income from 2003 to 2015 . In the third quarter of 2015 , the California-based web company earned 3.98 billion US dollars , an increase from the 3.93 billion in the previous quarter . Due to restructuring under new parent company Alphabet , Google net income is not longer reported separately .
gold_template: This timeline shows templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] from templateTitleDate[min] to templateTitleDate[max] . In the third quarter of templateTitleDate[max] , the California-based web company earned templateYValue[0] templateScale US templateYLabel[4] , an templatePositiveTrend from the templateYValue[1] templateScale in the previous quarter . Due to restructuring under new parent company Alphabet , templateTitleSubject[0] templateYLabel[0] templateYLabel[1] is not longer reported separately .

generated_template: The statistic presents the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] of the templateTitle[7] of the fourth quarter of templateTitleDate[0] . In the fourth quarter of templateTitleDate[0] , templateTitleSubject[0] 's templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the fiscal year .
generated: The statistic presents the Google quarterly net income 2003 2015 of the 2015 of the fourth quarter of 2003 .  In the fourth quarter of 2003 , Google 's Net amounted to 4675.0 million U.S. dollars in the fiscal year .

Example 22:
titleEntities: {'Subject': ['Case Shiller National Home Price Index'], 'Date': ['2017', '2019']}
title: U.S. housing : Case Shiller National Home Price Index 2017 to 2019
X_Axis['Month']: ['Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17']
Y_Axis['Index', 'value']: ['212.06', '211.63', '210.87', '209.65', '207.96', '206.06', '204.72', '204.46', '204.94', '205.34', '205.59', '205.6', '205.55', '205.18', '204.27', '202.66', '200.8', '198.76', '197.08', '196.29', '196.02', '195.61', '195.24', '194.97', '194.5', '193.66', '192.4', '190.65', '188.65', '186.64', '185.14']

gold: This statistic presents the values of S & P Case Shiller U.S. National Home Price Index from February 2017 to August 2019 . The index value was equal to 100 as of January 2000 , so if the index value is equal to 130 in a given month , for example , it means that the house prices increased by 30 percent since 2000 . The value of the S & P Case Shiller National Home Price Index amounted to 212.06 in August 2019 .
gold_template: This statistic presents the values of S & P templateTitleSubject[0] U.S. templateTitleSubject[0] Price templateYLabel[0] from 2017 to 2019 . The templateYLabel[0] templateYLabel[1] was equal to 100 as of 2000 , so if the templateYLabel[0] templateYLabel[1] is equal to 130 in a given templateXLabel[0] , for example , it means that the house prices templatePositiveTrend by 30 templateScale since 2000 . The templateYLabel[1] of the S & P templateTitleSubject[0] National templateTitleSubject[0] Index amounted to templateYValue[max] in 2019 .

generated_template: The templateYLabel[1] of the DJIA templateYLabel[0] amounted to templateYValue[0] on 31 , templateTitleDate[max] . templateTitleSubject[0] Industrial templateTitleSubject[0] templateYLabel[0] – additional information The templateTitleSubject[0] Industrial templateTitleSubject[0] templateYLabel[0] is a price-weighted templateTitleSubject[0] of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM .
generated: The value of the DJIA Index amounted to 212.06 on 31 , 2019 .  Case Shiller National Home Price Index Industrial Case Shiller National Home Price Index Index – additional information The Case Shiller National Home Price Index Industrial Index is a price-weighted Case Shiller National Home Price Index of 30 the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM .

Example 23:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Cities with the highest spending on parks and recreation in the U.S. 2018
X_Axis['State']: ['Minneapolis', 'Seattle', 'San_Francisco', 'Portland', 'Arlington', 'Irvine', 'Washington_D.C.', 'St._Paul', 'Plano', 'Boise', 'New_York', 'Long_Beach', 'Chicago', 'Cincinnati', 'Aurora']
Y_Axis['Spending', 'per', 'resident', 'U.S.', 'dollars']: ['346.97', '268.42', '259.59', '233.99', '232.59', '206.12', '203.21', '201.96', '201.02', '182.04', '176.0', '171.14', '162.87', '153.37', '146.33']

gold: This statistic shows the cities with the highest spending per resident on parks and recreation in the United States in 2018 . Seattle , Washington , spent around 268.42 U.S. dollars per resident on parks and recreation in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[4] and templateTitle[5] in the templateTitle[6] in templateTitleDate[0] . templateXValue[1] , templateXValue[6] , spent around templateYValue[1] templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] on templateTitle[4] and templateTitle[5] in templateTitleDate[0] .

generated_template: This graph shows the templateTitle[0] templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[6] templateTitle[7] in the templateTitle[5] templateTitle[6] as of 2019 . As of templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of templateTitleSubject[0] population templateTitle[6] templateTitle[7] templateXLabel[0] .
generated: This graph shows the Cities U.S. highest spending parks of 2018 in the U.S. 2018 as of 2019 .  As of 2018 , Minneapolis had the highest Spending of U.S. population 2018 State .

Example 24:
titleEntities: {'Subject': ['U.S.from'], 'Date': ['1900', '2016']}
title: Fatality numbers from heat waves and cold waves in the U.S.from 1900 to 2016
X_Axis['Type,', 'Year,', 'Location']: ['Heat_wave_1980_Kansas_City_Missouri_St_Loius', 'Heat_wave_1936_Illinois', 'Heat_wave_1995_Missouri_Oklahoma_Illinois', 'Heat_wave_1999_Illinois_Missouri_Wisconsin', 'Heat_wave_1983', 'Heat_wave_1966_St_Louis_Missouri', 'Heat_wave_2006_California', 'Cold_wave_1963', 'Heat_wave_1998_Arizona_Florida_Colorado', 'Heat_wave_2012_Washington_DC_Iowa_Virginia_North_Carolina']
Y_Axis['Number', 'of', 'fatalities']: ['1260', '1193', '670', '257', '188', '182', '164', '150', '130', '107']

gold: This statistic shows a list of heat waves and cold waves experienced in the United States from 1900 to 2016 , by the number of fatalities that occurred as a result . In the deadliest event on record , around 1,260 people lost their lives as a result of a heat wave in 1980 in Missouri and Tennessee .
gold_template: This statistic shows a list of templateXValue[0] templateTitle[4] and templateXValue[7] templateTitle[4] experienced in the country templateTitleSubject[0] templateTitleDate[min] to templateTitleDate[max] , by the templateYLabel[0] of templateYLabel[1] that occurred as a result . In the deadliest event on record , around templateYValue[max] people lost their lives as a result of a templateXValue[0] in templateXValue[0] in templateXValue[0] and Tennessee .

generated_template: In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of the templateYLabel[2] templateYLabel[2] templateTitle[1] , at approximately templateYValue[max] templateScale . templateXValue[1] had the second largest templateYLabel[0] . This was followed by templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: In 1900 , Heat_wave_1980_Kansas_City_Missouri_St_Loius had the highest Number of the fatalities numbers , at approximately 1260 % .  Heat_wave_1936_Illinois had the second largest Number .  This was followed by 1193 million fatalities .

Example 25:
titleEntities: {'Subject': ['UK'], 'Date': ['2017', '2019']}
title: Amounts outstanding of notes and coin in circulation in the UK 2017 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17"]
Y_Axis['Amounts', 'outstanding', 'in', 'million', 'GBP']: ['82648', '82820', '82980', '82933', '82920', '82806', '82764', '82649', '82546', '82536', '82471', '82318', '82117', '81945', '81817', '81812', '81704', '81698', '81637', '81561', '81503', '81345', '81610', '81804', '81938', '82069', '82139', '82114', '82088', '82132', '82156', '82170', '82037', '81627', '81526', '81210']

gold: As of December 2019 , the value of outstanding notes and coins in circulation in the United Kingdom reached approximately 82.65 billion British pounds . This was an increase of over 1.4 billion British pounds as compared to January 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of notes in circulation .
gold_template: As of 2019 , the value of templateYLabel[1] templateTitle[2] and coins in templateTitle[4] in the United Kingdom reached approximately templateYValue[0] templateScale British pounds . This was an templatePositiveTrend of over 1.4 templateScale British pounds as compared to 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of templateTitle[2] in templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2019 , templateTitle[6] . As of 2020 , had an templateYLabel[0] of templateYValue[max] templateScale of active templateYLabel[2] .
generated: This statistic shows the Amounts of the Amounts outstanding notes coin in the circulation as of 2019 , 2017 .  As of 2020 , had an Amounts of 82980 million of active million .

Example 26:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: U.S. mobile payment app usage 2016
X_Axis['Payment', 'Method']: ['Android_Pay', 'Retailer_mobile_app', 'Apple_Pay', 'Samsung_Pay', 'MasterPass', 'CurrenC', 'Windows_Phone_Wallet']
Y_Axis['Share', 'of', 'respondents']: ['18', '12', '11', '3', '2', '1', '1']

gold: This statistic presents the most commonly used mobile payment apps according to a survey of internet users carried out in February 2016 in the United States . During the survey period , 11 percent of respondents reported having used Apple Pay in the previous year .
gold_template: This statistic presents the most commonly used templateXValue[1] templateXLabel[0] apps according to a survey of internet users carried out in 2016 in the templateTitle[0] . During the survey period , templateYValue[2] templateScale of templateYLabel[1] reported having used templateXValue[2] templateXValue[0] in the previous year .

generated_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[3] of internet users in the templateTitle[0] as of 2017 , sorted by templateXLabel[0] . During the survey period of time , templateYValue[max] templateScale of templateYLabel[1] were templateXValue[0] .
generated: This statistic presents the mobile payment app of internet users in the U.S. as of 2017 , sorted by Payment .  During the survey period of time , 18 % of respondents were Android_Pay .

Example 27:
titleEntities: {'Subject': ['MTV', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: MTV viewers reached quarterly in the United Kingdom ( UK ) Q1 2012-Q3 2019
X_Axis['Quarter']: ['Q1_2012', 'Q2_2012', 'Q3_2012', 'Q4_2012', 'Q1_2013', 'Q2_2013', 'Q3_2013', 'Q4_2013', 'Q1_2014', 'Q2_2014', 'Q3_2014', 'Q4_2014', 'Q1_2015', 'Q2_2015', 'Q3_2015', 'Q4_2015', 'Q1_2016', 'Q2_2016', 'Q3_2016', 'Q4_2016', 'Q1_2017', 'Q2_2017', 'Q3_2017', 'Q4_2017', 'Q1_2018', 'Q2_2018', 'Q3_2018', 'Q4_2018', 'Q1_2019', 'Q2_2019', 'Q3_2019']
Y_Axis['Viewers', 'in', 'thousands']: ['10610', '9638', '10353', '9702', '9677', '10047', '10609', '11487', '9967', '10947', '9674', '9917', '10647', '9856', '9509', '8351', '10631', '10279', '10201', '9717', '8838', '8761', '8738', '8188', '8011', '7629', '8344', '6833', '6482', '5960', '6890']

gold: This statistic shows the quarterly reach of MTV in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the first quarter of 2016 , MTV reached 10.63 million viewers . In the second quarter of 2019 , MTV had its lowest reach during the period in consideration at nearly six million viewers .
gold_template: This statistic shows the templateTitle[3] reach of templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the first templateXLabel[0] of templateXValue[16] , templateTitleSubject[0] templateTitle[2] templateYValue[16] templateScale templateYLabel[0] . In the second templateXLabel[0] of templateXValue[28] , templateTitleSubject[0] had its lowest reach during the period in consideration at nearly templateYValue[28] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitle[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] templateYValue[max] each year .
generated: This statistic shows the MTV viewers reached of MTV from the first Quarter of 2019 to the fourth Quarter of Q1_2012 .  In the fourth Quarter of 2019 , MTV 's viewers thousands 11487 each year .

Example 28:
titleEntities: {'Subject': ['Americans'], 'Date': ['2019']}
title: Church attendance of Americans 2019
X_Axis['Frequency']: ['Every_week', 'Almost_every_week', 'About_once_a_month', 'Seldom', 'Never', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['23', '10', '12', '24', '29', '3']

gold: According to a 2019 survey , 29 percent of Americans never attend church or synagogue , compared to 23 percent of Americans who attend every week . Religiosity in the United States Despite only about a fifth of Americans attending church or synagogue on a weekly basis , almost 40 percent consider themselves to be very religious . Additionally , states in the Deep South such as Mississippi , Alabama , and Louisiana had the most residents identifying as very religious .
gold_template: According to a templateTitleDate[0] survey , templateYValue[max] templateScale of templateTitleSubject[0] templateXValue[4] attend templateTitle[0] or synagogue , compared to templateYValue[0] templateScale of templateTitleSubject[0] who attend templateXValue[0] . Religiosity in the country Despite only templateXValue[2] a fifth of templateTitleSubject[0] attending templateTitle[0] or synagogue on a weekly basis , templateXValue[1] 40 templateScale consider themselves to be very religious . Additionally , states in the Deep South such as Mississippi , Alabama , and Louisiana had the most residents identifying as very religious .

generated_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[3] of internet users in the templateTitle[0] as of 2017 . During the most recent survey period , templateYValue[0] templateScale of templateYLabel[1] stated they purchased goods templateTitle[1] at templateXValue[0] a templateXValue[0] .
generated: This statistic presents the attendance Americans 2019 of internet users in the Church as of 2017 .  During the most recent survey period , 23 % of respondents stated they purchased goods attendance at Every_week a .

Example 29:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2016']}
title: Number of mobile-only monthly active Facebook users 2011 to 2016
X_Axis['Quarter']: ["Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11"]
Y_Axis['Number', 'of', 'mobile-only', 'users', 'in', 'millions']: ['1149', '1055', '967', '894', '823', '727', '655', '581', '526', '456', '399', '341', '296', '254', '219', '189', '157', '126', '102', '83', '58']

gold: The timeline shows the number of mobile-only monthly active Facebook users . As of the fourth quarter of 2016 , 1,149 million Facebook users accessed the social media site exclusively via mobile device every month , up from 823 million users in the corresponding quarter of the previous year .
gold_template: The timeline shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] templateYLabel[2] . As of the fourth templateXLabel[0] of templateTitleDate[max] , templateYValue[max] templateScale templateTitleSubject[0] templateYLabel[2] accessed the social media site exclusively via mobile device every month , up from templateYValue[4] templateScale templateYLabel[2] in the corresponding templateXLabel[0] of the previous year .

generated_template: The timeline shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , templateTitle[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] was a 3.88 templateScale of growth .
generated: The timeline shows the Number mobile-only of the Facebook from the first Quarter of 2011 to the fourth Quarter of 2016 .  As of the last reported Quarter , Number 's mobile-only users was a 3.88 millions of growth .

Example 30:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Most popular games with casino visitors in the U.S. in 2014
X_Axis['Response']: ['Slot_machines', 'Black_Jack', 'Poker', 'Roulette', 'Video_poker', 'Craps', 'Sports_book', 'Baccarat', 'No_favorite']
Y_Axis['Share', 'of', 'respondents']: ['48', '16', '6', '5', '4', '4', '2', '1', '14']

gold: This statistic shows the most popular games with casino visitors in the United States as of May 2014 . During the survey , six percent of respondents said that poker was their favorite game to play at casinos .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2014 . During the survey , templateYValue[2] templateScale of templateYLabel[1] said that templateXValue[2] was their templateXValue[last] game to play at casinos .

generated_template: As of early templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the most of templateTitleSubject[0] adults found that templateXValue[2] and templateXValue[2] . Some templateYValue[max] templateScale of templateYLabel[1] stated that they used templateXValue[0] social or templateXValue[0] and templateYValue[1] templateScale chose a templateXValue[1] .
generated: As of early 2014 , Slot_machines and Black_Jack were the most of U.S. adults found that Poker and .  Some 48 % of respondents stated that they used Slot_machines social or Slot_machines and 16 % chose a Black_Jack .

Example 31:
titleEntities: {'Subject': ['HPE research development'], 'Date': ['2013', '2019']}
title: HPE : research and development spending 2013 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['1842', '1667', '1486', '1714', '2338', '2197', '1956']

gold: This statistic shows Hewlett Packard Enterprise 's ( previously a part of Hewlett-Packard Company ) expenditure on research and development for each fiscal year from 2013 to 2019 . In 2019 , HPE 's R & D expenditure came to 1.84 billion U.S. dollars . This represented a small portion of HPE 's net revenue , which reached 29.1 billion U.S. dollars .
gold_template: This statistic shows Hewlett Packard Enterprise 's ( previously a part of Hewlett-Packard Company ) templateYLabel[0] on templateTitleSubject[0] and templateTitleSubject[0] for each templateXLabel[0] templateXLabel[1] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateTitleSubject[0] 's R & D templateYLabel[0] came to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . This represented a small portion of templateTitleSubject[0] 's net revenue , which reached 29.1 templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the world from templateXValue[last] to templateXValue[0] . In templateXValue[0] , there were templateYValue[0] templateScale of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] .
generated: This statistic shows HPE research development spending in the world from 2013 to 2019 .  In 2019 , there were 1842 million of HPE research development research development spending .

Example 32:
titleEntities: {'Subject': ['Americans'], 'Date': ['2012']}
title: Share of Americans who have had a one-night-stand , as of 2012
X_Axis['Response']: ['No', 'Yes']
Y_Axis['Share', 'of', 'respondents']: ['41.9', '58.1']

gold: This statistic shows the results of a survey in the United States in 2012 on one-night-stands . 58.1 percent of respondents in the United States stated they have had a one-night-stand before .
gold_template: This statistic shows the results of a survey in the country in templateTitleDate[0] on one-night-stands . templateYValue[max] templateScale of templateYLabel[1] in the country stated they templateTitle[3] templateTitle[4] a templateTitle[5] before .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[7] as of 2015 , sorted templateTitle[6] templateTitle[7] templateTitle[8] templateXLabel[0] . During the survey period , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated they used templateXValue[0] .
generated: This statistic presents the Share Americans who have had one-night-stand 2012 as of 2015 , sorted 2012 Response .  During the survey period , it was found that 58.1 % of the respondents stated they used No .

Example 33:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading coupon website awareness according to U.S. users 2016
X_Axis['Response']: ['groupon.com', 'coupons.com', 'retailmenot.com', 'livingsocial.com', 'redplum.com', 'mycoupons.com', 'couponcabin.com', 'swagbugs.com', 'savingstar.com', 'slickdeals.net', 'fatwallet.com', 'us.toluna.com', 'travelzoo.com', 'shopathome.com', 'woot.com', 'hip2save.com', 'couponmountain.com', 'freeshipping.org', 'couponchief.com', 'yipit.com', 'shesaved.com', 'passionforsavings.com', 'fyvor.com', 'other']
Y_Axis['Share', 'of', 'respondents']: ['75', '64', '62', '48', '32', '31', '25', '23', '22', '19', '18', '14', '14', '13', '12', '11', '9', '6', '5', '4', '3', '3', '2', '2']

gold: This statistic gives information on the most popular discount and promo code websites according to users in the United States . During the August 2016 survey , 75 percent of respondents stated that they were aware of groupon.com . Coupons.com was ranked second with 64 percent awareness rate and retailmenot.com was in third place with 62 percent of respondents stating that they knew the platform .
gold_template: This statistic gives information on the most popular discount and promo code websites templateTitle[4] to templateTitle[6] in the templateTitle[5] . During the 2016 survey , templateYValue[max] templateScale of templateYLabel[1] stated that they were aware of templateXValue[idxmax(Y)] . templateXValue[1] was ranked second with templateYValue[1] templateScale templateTitle[3] rate and templateXValue[2] was in third place with templateYValue[2] templateScale of templateYLabel[1] stating that they knew the platform .

generated_template: According to a templateTitleDate[0] Statista survey , templateYValue[max] templateScale of the templateTitle[6] templateYLabel[1] use their templateTitle[1] to templateXValue[0] to templateXValue[0] . templateXValue[last] common uses of those belonging to the website templateXValue[1] and templateXValue[2] . templateXValue[last] had a civil templateTitle[3] .
generated: According to a 2016 Statista survey , 75 % of the users respondents use their coupon to groupon.com .  other common uses of those belonging to the website coupons.com and retailmenot.com .  other had a civil awareness .

Example 34:
titleEntities: {'Subject': ['Change'], 'Date': ['2018', '2019']}
title: Change of domestic heating oil price in selected countries 2018 to 2019
X_Axis['Country']: ['Canada', 'Italy', 'Spain', 'France', 'United_Kingdom', 'Japan', 'Germany', 'United_States']
Y_Axis['Change', 'in', 'heating', 'oil', 'price']: ['7', '5.9', '5.9', '4', '0.8', '-0.1', '-4.4', '-']

gold: This statistic shows the change in the price of domestic heating oil per liter in selected countries for the period between December 2018 and December 2019 . In December 2019 , the domestic heating oil price in Germany was some -4.4 percent lower than in December 2018 . Domestic heating oil The price of domestic heating oil per liter has decreased in many countries .
gold_template: This statistic shows the templateYLabel[0] in the templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter in templateTitle[5] templateTitle[6] for the period between 2018 and 2019 . In 2019 , the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] was some templateYValue[6] templateScale lower than in 2018 . templateTitle[1] templateYLabel[1] templateYLabel[2] The templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter has templateNegativeTrend in many templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2019 , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , there were templateYValue[2] templateScale of all templateYLabel[2] .
generated: This statistic shows the Change of the Change domestic heating oil in the price as of 2019 , countries Country .  In 2018 , there were 5.9 % of all oil .

Example 35:
titleEntities: {'Subject': ['European Union'], 'Date': ['2018', '2018']}
title: Number of dogs in the European Union 2018 , by country
X_Axis['Country']: ['Germany', 'United_Kingdom', 'Poland', 'Italy', 'France', 'Spain', 'Romania', 'Portugal', 'Czechia', 'Netherlands', 'Belgium', 'Hungary', 'Slovakia', 'Sweden', 'Austria', 'Finland', 'Bulgaria', 'Greece', 'Denmark', 'Lithuania', 'Ireland', 'Slovenia', 'Latvia', 'Estonia']
Y_Axis['Number', 'of', 'dogs', 'in', 'thousands']: ['9400', '9000', '7600', '7002', '6950', '6270', '4000', '2100', '2000', '1520', '1315', '1180', '900', '880', '827', '810', '740', '660', '595', '550', '450', '290', '260', '210']

gold: This statistic presents the number of pet dogs in the European Union by country , as of 2018 . Germany ranked highest with a dog population of approximately 9.4 million in 2018 , followed by the United Kingdom ( UK ) with nine million . The number of dogs in Europe has seen a notable increase since 2010 , with the number of dogs significantly increasing by more than eleven million from 2010 to 2018 .
gold_template: This statistic presents the templateYLabel[0] of pet templateYLabel[1] in the templateTitleSubject[0] by templateXLabel[0] , as of templateTitleDate[0] . templateXValue[0] ranked highest with a dog population of approximately templateYValue[max] templateScale in templateTitleDate[0] , followed templateTitle[5] the templateXValue[1] ( UK ) with templateYValue[max] templateScale . The templateYLabel[0] of templateYLabel[1] in Europe has seen a notable templatePositiveTrend since 2010 , with the templateYLabel[0] of templateYLabel[1] significantly templatePositiveTrend templateTitle[5] more than eleven templateScale from 2010 to templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2019 , sorted by templateXLabel[0] . In templateTitleDate[0] , templateTitle[0] templateTitle[1] templateYLabel[2] was the templateXValue[2] with templateYValue[4] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of European Union dogs European Union as of 2019 , sorted by Country .  In 2018 , Number dogs thousands was the Poland with 6950 dogs thousands .

Example 36:
titleEntities: {'Subject': ['A U.S.'], 'Date': ['2018']}
title: People with hemophilia A in the U.S. 2018 , by age group
X_Axis['Age', 'group']: ['0-4_years', '5-13_years', '14-18_years', '19-44_years', 'Above_45_years']
Y_Axis['Percentage', 'of', 'people']: ['9', '24', '13', '34', '21']

gold: This statistic displays the percentage of people in the United States diagnosed with Hemophilia A , sorted by age group , as of 2018 . In that year , nine percent of all Americans diagnosed with hemophilia A were between 0 and 4 years of age .
gold_template: This statistic displays the templateScale of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitleDate[0] . In that year , templateYValue[min] templateScale of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Percentage of adults in the A U.S. who were using People as of 2019 , sorted group .  During that period of time , 34 percentage of female people stated that they used the social networking site .

Example 37:
titleEntities: {'Subject': ['Number M A', 'Europe'], 'Date': ['2014', '2015']}
title: Number of M & A deals in Europe 2014 to 2015
X_Axis['Month']: ['Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14']
Y_Axis['Number', 'of', 'deals']: ['1222', '1030', '1107', '1166', '1097', '1076', '1235', '1152', '1167', '1222', '912', '1253', '1171', '1070', '1185', '1062']

gold: The statistic presents the number of merger and acquisition transactions in Europe from March 2014 to June 2015 . There were 1,222 M & A deals in Europe in June 2015 . The number of merger and acquisition transactions in Europe remained fairly steady between March 2014 and June 2015 .
gold_template: The statistic presents the templateYLabel[0] of merger and acquisition transactions in templateTitleSubject[1] from 2014 to 2015 . There were templateYValue[0] templateTitleSubject[0] templateTitle[2] A templateYLabel[1] in templateTitleSubject[1] in 2015 . The templateYLabel[0] of merger and acquisition transactions in templateTitleSubject[1] remained fairly steady between 2014 and 2015 .

generated_template: The statistic presents the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] templateTitle[2] in the templateTitle[4] as of 2014 . As of that templateXLabel[0] , the mobile messenger had an overall templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] , up from templateYValue[4] templateScale templateYLabel[2] in the country .
generated: The statistic presents the Number of Number M A & in the Europe as of 2014 .  As of that Month , the mobile messenger had an overall Number of 1253 million deals , up from 1097 million deals in the country .

Example 38:
titleEntities: {'Subject': ['Employees'], 'Date': ['2011']}
title: Employees ' average working hours per week worldwide 2011
X_Axis['Country']: ['Singapore', 'India', 'Brazil', 'Mexico', 'U.S.', 'Worldwide', 'Japan', 'Argentina', 'Spain', 'Sweden', 'Germany', 'U.K.', 'France', 'Italy', 'Netherlands']
Y_Axis['Average', 'working', 'hours', 'per', 'week']: ['44', '42', '40', '40', '40', '40', '40', '40', '40', '40', '39', '37', '37', '36', '36']

gold: The statistic depicts how many hours employees work per week on average worldwide . Respondents from India work 42 hours per week on average .
gold_template: The statistic depicts how many templateYLabel[2] templateTitleSubject[0] work templateYLabel[3] templateYLabel[4] on templateYLabel[0] templateXValue[5] . Respondents from templateXValue[1] work templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] on templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , broken down templateTitle[4] templateXLabel[0] . According to the source , with a total of templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Average of the Employees ' average working in 2011 , broken down hours Country .  According to the source , with a total of 42 working hours .

Example 39:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2019']}
title: Amazon 's Q4 income including seasonal sales 2009 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q4_'18", "Q4_'17", "Q4_'16", "Q4_'15", "Q4_'14", "Q4_'13", "Q4_'12", "Q4_'11", "Q4_'10", "Q4_'09"]
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['3268', '3000', '1900', '749', '482', '214', '239', '97', '177', '416', '384']

gold: This statistic shows Amazon.com 's net income in the fourth quarters from 2009 to 2019 , a comparison of the company 's quarterly income including the yearly Christmas sales . In 2019 , Amazon 's net income of that quarter amounted to 3.27 billion U.S. dollars .
gold_template: This statistic shows Amazon.com templateTitle[1] templateYLabel[0] templateYLabel[1] in the fourth quarters from templateTitleDate[min] to templateTitleDate[max] , a comparison of the company templateTitle[1] quarterly templateYLabel[1] templateTitle[4] the yearly Christmas templateTitle[6] . In templateTitleDate[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of that templateXLabel[0] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] , up from the results of the preceding templateXLabel[0] with templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's templateYLabel[0] sales amounted to 87.44 templateScale templateYLabel[3] templateYLabel[4] during the same fiscal period . Shopping via templateTitleSubject[0] Online product sales are the biggest revenue segment of the e-tailer , followed by retail third-party seller services .
generated: In the fourth Quarter of 2019 , Amazon 's Net income amounted to 3268 million U.S. dollars , up from the results of the preceding Quarter with 3000 million U.S. dollars .  Amazon 's Net sales amounted to 87.44 million U.S. dollars during the same fiscal period .  Shopping via Amazon Online product sales are the biggest revenue segment of the e-tailer , followed by retail third-party seller services .

Example 40:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: Fast food restaurant visit frequency in the United Kingdom ( UK ) 2015
X_Axis['Response']: ['Less_than_once_per_week', 'One_to_three_times_per_week', 'Four_to_six_times_per_week', 'Seven_to_nine_times_per_week', 'Ten_times_or_more_per_week', "I_don't_eat_at_fast_food_restaurants"]
Y_Axis['Share', 'of', 'respondents']: ['44', '27', '5', '2', '1', '24']

gold: This statistic shows the frequency of fast food ( any quick service ) restaurant visits in any given fast food restaurant in 2015 . In 2015 , 44 percent of respondents visited fast food restaurants less than once per week .
gold_template: This statistic shows the templateTitle[4] of templateXValue[last] ( any quick service ) templateTitle[2] visits in any given templateXValue[last] restaurant in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[max] templateScale of templateYLabel[1] visited templateXValue[last] restaurants templateXValue[0] once templateXValue[0] .

generated_template: The statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books . templateYValue[max] templateScale of templateYLabel[1] stated that they followed by the templateXValue[2] days .
generated: The statistic shows the results of a survey about the restaurant visit of several individual Fast I_don't_eat_at_fast_food_restaurants in United Kingdom 2015 , phrased One_to_three_times_per_week the question : `` except reading books .  44 % of respondents stated that they followed by the Four_to_six_times_per_week days .

Example 41:
titleEntities: {'Subject': ['Snapchat'], 'Date': ['2020', '2020']}
title: Countries with the most Snapchat users 2020
X_Axis['Country']: ['United_States', 'India', 'France', 'United_Kingdom', 'Saudi_Arabia', 'Mexico', 'Brazil', 'Germany', 'Canada', 'Russia']
Y_Axis['Audience', 'size', 'in', 'millions']: ['101.25', '22.95', '21.25', '18.7', '16.1', '14.8', '13.95', '12.15', '8.15', '7.75']

gold: As of January 2020 , the United States had the biggest Snapchat user base in the world , with an audience of 101.25 million users . India ranked in second place with a Snapchat audience base of 22.95 million users . The photo-sharing platform is projected to surpass 365 million global users in 2023 .
gold_template: As of 2020 , the templateXValue[0] had the biggest templateTitleSubject[0] user base in the world , templateTitle[1] an templateYLabel[0] of templateYValue[max] templateScale templateTitle[4] . templateXValue[1] ranked in second place templateTitle[1] a templateTitleSubject[0] templateYLabel[0] base of templateYValue[1] templateScale templateTitle[4] . The photo-sharing platform is projected to surpass 365 templateScale global templateTitle[4] in 2023 .

generated_template: This statistic gives a ranking of the templateTitle[0] templateTitle[1] the largest templateTitleSubject[0] audiences worldwide as of 2020 . During the measured period , the templateXValue[0] were ranked first templateTitle[1] an templateYLabel[0] of templateYValue[max] templateScale templateTitle[4] .
generated: This statistic gives a ranking of the Countries most the largest Snapchat audiences worldwide as of 2020 .  During the measured period , the United_States were ranked first most an Audience of 101.25 millions 2020 .

Example 42:
titleEntities: {'Subject': ['Americans'], 'Date': ['2017']}
title: Americans on the concept of the American Dream in 2017
X_Axis['Month']: ['Personal_freedom', 'Religious_freedom', 'Equality', 'Security', 'The_pursuit_of_happiness', 'Economic_freedom', 'Freedom_of_justice', 'Political_freedom', 'Common_good', 'Diversity', 'Freedom_of_the_press', 'Progress_and_change', 'Patriotism', 'Scientific_progress', 'Separation_of_powers', 'Individualism', 'Action_and_achievement', 'Competition', 'Capitalism', 'Solidarity', 'Volunteerism', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['66', '56', '55', '54', '53', '51', '48', '45', '44', '42', '40', '39', '37', '36', '36', '34', '34', '30', '28', '21', '19', '4']

gold: This statistic shows the results of a 2017 survey among Americans on the concepts essential for the American Dream . During the survey , 34 percent of respondents stated that action and achievement is essential for the American Dream .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] on the concepts essential for the templateTitleSubject[0] templateTitle[3] . During the survey , templateYValue[15] templateScale of templateYLabel[1] stated that templateXValue[16] and templateXValue[16] is essential for the templateTitleSubject[0] templateTitle[3] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] . As of templateTitleDate[0] , templateYValue[max] templateScale of templateYLabel[1] stated templateXValue[2] was currently templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Americans nation in Americans .  As of 2017 , 66 % of respondents stated Equality was currently Personal_freedom .

Example 43:
titleEntities: {'Subject': ['British Telecommunications BT', 'ARPU'], 'Date': []}
title: British Telecommunications ( BT ) : consumer ARPU Q1 2014/15-Q1 2019/20
X_Axis['Quarter']: ['Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014']
Y_Axis['Average', 'revenue', 'per', 'user', 'in', 'GBP', 'per', 'month']: ['37.9', '38.8', '39.6', '38.3', '37.9', '38.9', '37.7', '37.8', '37.6', '36.7', '37.3', '37.4', '35.0', '37.1', '36.6', '35.6', '34.9', '34.6', '34.2', '33.6', '33.2']

gold: This statistic shows the Consumer ARPU of British Telecommunications ( BT Consumer ) from the first quarter of 2014/15 to the first quarter of 2019/20 . In the first quarter of 2019/20 ending June 30 , the ARPU was 37.9 British pounds per month .
gold_template: This statistic shows the templateTitle[3] templateTitleSubject[1] of templateTitleSubject[0] ( templateTitleSubject[0] templateTitle[3] ) from the first templateXLabel[0] of 2014/15 to the first templateXLabel[0] of templateTitle[7] . In the first templateXLabel[0] of templateTitle[7] ending 30 , the templateTitleSubject[1] was templateYValue[0] templateTitleSubject[0] pounds templateYLabel[2] templateYLabel[6] .

generated_template: The timeline shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitleDate[max] , the photo sharing app had templateYValue[max] templateScale templateTitle[1] subscriber base has been consistent over the last few years .
generated: The timeline shows the British Telecommunications of the British Telecommunications BT from the first Quarter of to the fourth Quarter of Q1_2019 .  In the fourth Quarter of , the photo sharing app had 39.6 million Telecommunications subscriber base has been consistent over the last few years .

Example 44:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used garage door openers in the U.S. 2018
X_Axis['Brand']: ['Liftmaster', 'Chamberlain', 'Overhead_Door', 'Genie', 'Craftsmen', 'Wayne-Dalton', 'Raynor', 'Linear', 'Access_Master', 'Marantec', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['50', '14.9', '9.6', '7.9', '5.3', '3.5', '3.5', '0.9', '0.9', '0.9', '2.6']

gold: This statistic depicts garage door openers brands used the most by U.S. construction firms in 2018 . The survey revealed that 50 percent of the respondents used LiftMaster garage door openers brand the most .
gold_template: This statistic depicts templateTitle[2] templateXValue[2] templateTitle[4] brands templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateTitle[2] templateXValue[2] templateTitle[4] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic displays templateTitle[2] templateTitle[3] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] ( DuPont ) templateXLabel[0] templateTitle[2] the templateTitle[0] .
generated: This statistic displays garage door used the Most by U.S. construction firms in 2018 .  The survey revealed that 50 % of the respondents used Liftmaster ( DuPont ) Brand garage the Most .

Example 45:
titleEntities: {'Subject': ['Tourism'], 'Date': ['2018']}
title: Tourism industry growth expectations according to travel experts 2018
X_Axis['Response']: ['Significantly_decline', 'Slightly_decline', 'No_change', 'Slightly_grow', 'Significantly_grow']
Y_Axis['Share', 'of', 'respondents']: ['1', '11', '14', '56', '18']

gold: This statistic shows the results of a survey asking travel industry representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they expect the tourism industry to grow or decline in 2018 . Of respondents , 56 percent believed that the tourism industry will grow slightly in 2018 .
gold_template: This statistic shows the results of a survey asking templateTitle[5] templateTitle[1] representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they expect the templateTitleSubject[0] templateTitle[1] to templateXValue[3] or templateXValue[0] in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[max] templateScale believed that the templateTitleSubject[0] templateTitle[1] will templateXValue[3] templateXValue[1] in templateTitleDate[0] .

generated_template: As of templateTitleDate[0] , templateYValue[max] templateScale of U.S.-based templateYLabel[1] stated that their favorite moving watching location was at templateXValue[1] . Only around templateYValue[0] templateScale of templateYLabel[1] stated that they templateTitle[0] templateXValue[last] or templateXValue[0] and templateXValue[0] . The search of lodging has consolidated as the past nine years thanks to templateXValue[0] .
generated: As of 2018 , 56 % of U.S.-based respondents stated that their favorite moving watching location was at Slightly_decline .  Only around 1 % of respondents stated that they Tourism Significantly_grow or Significantly_decline and .  The search of lodging has consolidated as the past nine years thanks to Significantly_decline .

Example 46:
titleEntities: {'Subject': ['Chile'], 'Date': ['2018']}
title: Chile : public perception on the country 's main problems in 2018
X_Axis['Response']: ['Crime', 'Unemployment', 'Corruption', 'Health', 'Education', 'Low_salaries', 'Violation_of_human_rights', 'Immigrants', 'Income_distribution', 'Lack_of_care_for_the_elderly', 'Political_situation', 'The_economy', 'Poverty', 'Drug_consumption']
Y_Axis['Share', 'of', 'respondents']: ['38.2', '8.8', '6.2', '6', '5.8', '4.4', '3.9', '3.2', '2.8', '2.8', '2.7', '2.3', '1.8', '1.6']

gold: During a survey conducted in 2018 , over 38 percent of respondents in Chile mentioned criminality as one of the most important problems affecting the South American country . Furthermore , 3.2 percent of the people interviewed quoted immigrants as one of the main problems in their country , leaving behind income distribution , the political situation and the economy.Up to 45 percent of the people surveyed in Chile claim that most religious leaders are involved in acts of corruption .
gold_template: During a survey conducted in templateTitleDate[0] , over templateYValue[max] templateScale of templateYLabel[1] in templateTitleSubject[0] mentioned criminality as one of the most important templateTitle[6] affecting the South American templateTitle[3] . Furthermore , templateYValue[7] templateScale of the people interviewed quoted templateXValue[7] as one of the templateTitle[5] templateTitle[6] in their templateTitle[3] , leaving behind templateXValue[8] , the templateXValue[10] and the economy.Up to 45 templateScale of the people surveyed in templateTitleSubject[0] claim that most religious leaders are involved in acts of templateXValue[2] .

generated_template: As of early templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] with templateYValue[max] templateScale of templateYLabel[1] stated that templateTitle[1] . Another templateYValue[1] templateScale of templateYLabel[1] stated that they templateXValue[6] for the templateXValue[2] during templateXValue[last] .
generated: As of early 2018 , Crime and Unemployment were the most perception country 's main with 38.2 % of respondents stated that public .  Another 8.8 % of respondents stated that they Violation_of_human_rights for the Corruption during Drug_consumption .

Example 47:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Video game systems : U.S. installed base in 2017 , by platform
X_Axis['Console']: ['Xbox_One_S', 'Xbox_One', 'Xbox_360', 'Xbox', 'PlayStation_4', 'PlayStation_3', 'PlayStation_2', 'Wii_U', 'Wii']
Y_Axis['Installed', 'base', 'in', 'millions']: ['9', '12', '21', '5', '10', '13', '14', '6', '16']

gold: The statistic depicts the installed base of video game systems in the United States in 2017 , by platform . The installed base of Microsoft 's Xbox 360 was 21 million .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The templateYLabel[0] templateYLabel[1] of Microsoft 's templateXValue[0] templateXValue[2] was templateYValue[max] templateScale .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[0] as of 2019 , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , there were templateTitle[0] templateTitle[1] templateTitle[2] time .
generated: The statistic shows the Installed of Video game systems U.S. installed in the Video as of 2019 , 2017 Console .  In 2017 , there were Video game systems time .

Example 48:
titleEntities: {'Subject': ['Number'], 'Date': ['2006', '2010']}
title: Number of poker players who play for money
X_Axis['Month']: ['September_2006', 'January_2007', 'Spring_2008', 'Spring_2009', 'Spring_2010']
Y_Axis['Number', 'of', 'players', '(in', 'millions)']: ['17.8', '15.2', '20.0', '20.8', '22.2']

gold: This statistic shows the number of poker players worldwide who play for money , from 2006 to 2010 ( in millions ) . In Spring 2008 , 20 million people played poker online for money .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] worldwide templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] , from templateXValue[0] to templateXValue[last] ( in templateScale ) . In templateXValue[2] , templateYValue[2] templateScale people played templateTitle[1] online templateTitle[5] templateTitle[6] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] as of October templateTitleDate[0] . As of the last reported period , the templateYLabel[1] of time , templateTitle[1] were templateXValue[0] has been templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the poker players who play for in Number as of October 2006 .  As of the last reported period , the players of time , poker were September_2006 has been players (in .

Example 49:
titleEntities: {'Subject': ['Most'], 'Date': ['2019']}
title: Most popular global mobile messaging apps 2019
X_Axis['Platform']: ['WhatsApp', 'Facebook_Messenger', 'WeChat', 'QQ_Mobile', 'Snapchat', 'Telegram']
Y_Axis['Monthly', 'active', 'users', 'in', 'millions']: ['1600', '1300', '1133', '808', '314', '200']

gold: As of October 2019 , 1.6 billion users were accessing the WhatsApp messenger on a monthly basis . The app 's reach is usage penetration is particularly strong in markets outside of the United States and it is one of the most popular mobile social apps worldwide . In February 2014 , social network Facebook acquired the mobile app for 19 billion U.S. dollars .
gold_template: As of October templateTitleDate[0] , templateYValue[max] templateScale templateYLabel[2] were accessing the templateXValue[0] templateXValue[1] on a templateYLabel[0] basis . The app 's reach is usage penetration is particularly strong in markets outside of the country and it is one of the templateTitleSubject[0] templateTitle[1] templateXValue[3] social templateTitle[5] worldwide . In 2014 , social network templateXValue[1] acquired the templateXValue[3] app for 19 templateScale U.S. dollars .

generated_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) . During the survey period , it was found that templateXValue[0] had templateYValue[max] templateScale templateYLabel[2] .
generated: This statistic presents a ranking of the Most popular global mobile in the United Kingdom ( Most ) .  During the survey period , it was found that WhatsApp had 1600 millions users .

Example 50:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2017', '2019']}
title: Inflation rate ( CPI ) in the United Kingdom ( UK ) 2017 to 2019
X_Axis['Month']: ["Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17"]
Y_Axis['Inflation', 'rate']: ['1.5', '1.5', '1.7', '1.7', '2.1', '2', '2', '2.1', '1.9', '1.9', '1.8', '2.1', '2.3', '2.4', '2.4', '2.7', '2.5', '2.4', '2.4', '2.4', '2.5', '2.7', '3', '3', '3.1']

gold: The Consumer Price Index ( CPI ) rate of the United Kingdom in November 2019 was 1.5 percent , which together with the previous month , was the lowest rate recorded in this two year period . Between November 2017 and November 2019 the CPI rate was at it 's highest in November of 2017 , when an inflation rate of 3.1 percent was recorded .
gold_template: The Consumer Price Index ( templateTitle[2] ) templateYLabel[1] of the templateTitleSubject[0] in 2019 was templateYValue[min] templateScale , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[min] year period . Between 2017 and 2019 the templateTitle[2] templateYLabel[1] was at it 's highest in of templateTitleDate[min] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale was recorded .

generated_template: Between 2019 and 2020 , templateTitle[3] prices in the templateTitleSubject[0] templatePositiveTrend by templateYValue[0] templateScale . A period of continuous deflation between 2015 and 2017 preceded a return to a sustained rise of the cost of templateTitle[3] from 2017 onwards . templateYLabel[0] templateYLabel[1] and consumer price index templateYLabel[0] is commonly measured via the consumer price index , which illustrates changes to prices paid by consumers templateTitle[2] a representative basket of goods and services .
generated: Between 2019 and 2020 , United prices in the United Kingdom increased by 1.5 % .  A period of continuous deflation between 2015 and 2017 preceded a return to a sustained rise of the cost of United from 2017 onwards .  Inflation rate and consumer price index Inflation is commonly measured via the consumer price index , which illustrates changes to prices paid by consumers CPI a representative basket of goods and services .

Example 51:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Origin of illegal immigrants in the U.S. 2015
X_Axis['Country']: ['Mexico', 'El_Salvador', 'Guatemala', 'India', 'Honduras', 'Philippines', 'China', 'Korea', 'Vietnam', 'Ecuador', 'Other']
Y_Axis['Illegal', 'immigrants', 'in', 'thousands']: ['6580', '750', '620', '470', '440', '370', '320', '230', '170', '150', '1870']

gold: The statistic shows the estimated number of illegal immigrants in the U.S. in 2015 , by country of origin . As of January 2015 , about 6.58 million illegal immigrants from Mexico were living in the United States .
gold_template: The statistic shows the estimated number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] of templateTitle[0] . As of 2015 , about templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] from templateXValue[0] were living in the templateTitle[3] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the world from templateTitleDate[min] to templateTitleDate[max] . In that year , templateXValue[2] had a total of templateYValue[2] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Illegal of the Origin illegal immigrants U.S. in the world from 2015 to .  In that year , Guatemala had a total of 620 immigrants thousands .

Example 52:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Number of women murdered by men in the U.S. by state 2017
X_Axis['U.S.', 'states']: ['California', 'Texas', 'Georgia', 'Pennsylvania', 'Ohio', 'New_York', 'Virginia', 'Tennessee', 'Arizona', 'North_Carolina', 'Louisiana', 'Michigan', 'South_Carolina', 'Missouri', 'Illinois', 'Washington', 'New_Jersey', 'Kentucky', 'Maryland', 'Indiana', 'Arkansas', 'Colorado', 'Nevada', 'Wisconsin', 'Oklahoma', 'Massachusetts', 'Mississippi', 'Oregon', 'Minnesota', 'Kansas', 'West_Virginia', 'New_Mexico', 'Connecticut', 'Alaska', 'Idaho', 'Iowa', 'Utah', 'Montana', 'Delaware', 'Nebraska', 'Hawaii', 'Vermont', 'South_Dakota', 'Rhode_Island', 'Maine', 'North_Dakoda', 'New_Hampshire', 'Wyoming']
Y_Axis['Number', 'of', 'homicide', 'victims']: ['232', '221', '96', '82', '78', '74', '73', '69', '68', '65', '63', '58', '52', '51', '49', '42', '41', '40', '39', '38', '34', '32', '30', '30', '28', '23', '22', '21', '21', '16', '15', '15', '15', '14', '13', '12', '12', '10', '7', '7', '6', '6', '6', '5', '5', '4', '4', '4']

gold: This statistic shows the number of female homicide victims in single offender homicides per state in the United States in 2017 . In 2017 , there were 221 women killed by male single offenders in the state of Texas . Texas was the state with the highest number of women murdered by men in single offender homicides .
gold_template: This statistic shows the templateYLabel[0] of female templateYLabel[1] templateYLabel[2] in single offender homicides per templateTitle[7] in the templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , there were templateYValue[1] templateTitle[1] killed templateTitle[3] male single offenders in the templateTitle[7] of templateXValue[1] . templateXValue[1] was the templateTitle[7] with the highest templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in single offender homicides .

generated_template: This statistic shows the templateYLabel[0] of internet users in the templateTitle[0] who were using templateTitleSubject[0] as of 2020 . During the survey period , it was found that just templateYValue[4] templateScale of internet users had been accessed here .
generated: This statistic shows the Number of internet users in the Number who were using U.S. as of 2020 .  During the survey period , it was found that just 78 % of internet users had been accessed here .

Example 53:
titleEntities: {'Subject': ['Current'], 'Date': []}
title: Current year in various historical and world calendars 2020
X_Axis['Month']: ['Assyrian', 'Hebrew', 'Chinese', 'Julian', 'Buddhist', 'Gregorian', 'Hindu', 'Islamic', 'Iranian', 'French_Revolutionary']
Y_Axis['Current', 'year', '(as', 'of', 'January', '25,', '2020)']: ['6770', '5780', '4718', '2773', '2563', '2020', '1941', '1441', '1398', '228']

gold: Today , the vast majority of the world uses what is known as the Gregorian calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The Gregorian calendar replaced the Julian calendar , which had been the most used calendar in Europe until this point . The Gregorian calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth year , unless the year is divisible by 100 but not 400 ( for example , the year 2000 was a leap year , whereas 2100 and 2200 will not be ) .
gold_template: Today , the vast majority of the templateTitle[4] uses what is known as the templateXValue[5] calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The templateXValue[5] calendar replaced the templateXValue[3] calendar , which had been the most used calendar in Europe until this point . The templateXValue[5] calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth templateYLabel[1] , unless the templateYLabel[1] is divisible by 100 but not 400 ( for example , the templateYLabel[1] 2000 was a leap templateYLabel[1] , whereas 2100 and 2200 will not be ) .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of 2018 . As of that templateXLabel[0] , templateXValue[0] had a total of templateYValue[max] templateScale of the app templateYLabel[2] .
generated: This statistic shows the Current year various historical world Current 2020 as of 2018 .  As of that Month , Assyrian had a total of 6770 % of the app (as .

Example 54:
titleEntities: {'Subject': ['Physicians'], 'Date': ['2013']}
title: Physicians density worldwide by region 2013
X_Axis['Country']: ['Europe', 'Americas', 'Western_Pacific', 'World', 'Eastern_Mediterranean', 'Southeast_Asia', 'Africa']
Y_Axis['Physicians', 'per', '10,000', 'population']: ['32.1', '21.5', '15.5', '13.9', '12.7', '5.9', '2.7']

gold: This statistic depicts the physicians density worldwide as of 2013 , by WHO region . As of that year , the average density in Southeast Asia stood at 5.9 physicians per 10,000 inhabitants . The world average was around 14 physicians per 10,000 inhabitants .
gold_template: This statistic depicts the templateYLabel[0] templateTitle[1] templateTitle[2] as of templateTitleDate[0] , templateTitle[3] WHO templateTitle[4] . As of that year , the average templateTitle[1] in templateXValue[5] stood at templateYValue[5] templateYLabel[0] templateYLabel[1] 10,000 inhabitants . The templateXValue[3] average was around templateYValue[3] templateYLabel[0] templateYLabel[1] 10,000 inhabitants .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] worldwide as of 2019 , sorted templateTitle[5] templateXLabel[0] . As of that year , the templateXValue[3] had a templateYLabel[0] of templateYValue[4] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Physicians of the per 10,000 worldwide as of 2019 , sorted 2013 Country .  As of that year , the World had a Physicians of 12.7 per 10,000 .

Example 55:
titleEntities: {'Subject': ['Italians'], 'Date': ['2017', '2017']}
title: Persons with whom Italians cheat on their partners 2017
X_Axis['Response']: ['Friend', 'Colleague_from_work', 'Stranger_met_in_a_particular_context_(disco_gym_holidays_etc.)', 'Neighbor', 'Does_not_reply', 'Partner_of_a_friend_of_mine', 'Stranger_met_by_chance', 'Escort', 'Family_member']
Y_Axis['Share', 'of', 'respondents']: ['25.4', '22.6', '17.5', '10.7', '9.3', '5.3', '4.4', '3.5', '1.3']

gold: A survey conducted in 2017 reveals that the largest groups of Italian respondents cheated on their partner either with a friend or with a colleague . In particular , 25.4 percent of interviewees in Italy stated that they betrayed their partner with a friend , while 22.6 percent did so with a colleague . However , it was common to cheat with strangers as well .
gold_template: A survey conducted in templateTitleDate[0] reveals that the largest groups of Italian templateYLabel[1] cheated on templateTitle[5] templateXValue[5] either templateTitle[1] a templateXValue[0] or templateTitle[1] a templateXValue[1] . In templateXValue[2] , templateYValue[max] templateScale of interviewees in Italy stated that they betrayed templateTitle[5] templateXValue[5] templateTitle[1] a templateXValue[0] , while templateYValue[1] templateScale did so templateTitle[1] a templateXValue[1] . However , it was common to templateTitle[4] templateTitle[1] strangers as well .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] templateTitle[3] as of 2018 . At that time , templateYValue[max] templateScale of templateYLabel[1] stated that they used templateXValue[last] passwords for templateXValue[0] and templateXValue[1] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Persons nation in Italians cheat as of 2018 .  At that time , 25.4 % of respondents stated that they used Family_member passwords for Friend and Colleague_from_work .

Example 56:
titleEntities: {'Subject': ['Russia'], 'Date': ['2015']}
title: Largest cities in Russia 2015
X_Axis['Month']: ['Moscow', 'St._Petersburg', 'Novosibirsk', 'Jekaterinburg', 'Nižnij_Novgorod', "Kazan'", "Čel'abinsk", 'Samara', 'Omsk', 'Rostov-na-Donu']
Y_Axis['Residents', 'in', 'million']: ['12.05', '5.19', '1.57', '1.43', '1.27', '1.21', '1.18', '1.17', '1.17', '1.11']

gold: The statistic depicts the ten largest cities in Russia in 2015 . In 2015 , Moscow had around 12.1 million residents , making it the largest city in Russia . Population and workforce of Russia Russia is the largest country in the world ; with a total population of around 144 million people ( 263767 ) , it is the ninth most populous nation worldwide .
gold_template: The statistic depicts the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had around templateYValue[max] templateScale templateYLabel[0] , making it the templateTitle[0] city in templateTitleSubject[0] . Population and workforce of templateTitleSubject[0] is the templateTitle[0] country in the world ; with a total population of around 144 templateScale people ( 263767 ) , it is the ninth most populous nation worldwide .

generated_template: This statistic shows the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateScale people lived in templateXValue[0] , making it the templateTitle[0] city in templateTitleSubject[0] .
generated: This statistic shows the ten Largest cities in Russia as of 2015 .  In 2015 , around 12.05 million people lived in Moscow , making it the Largest city in Russia .

Example 57:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Car sharing users in Europe 2014 , by country
X_Axis['Country']: ['Germany', 'United_Kingdom', 'France', 'Italy', 'Switzerland', 'Austria', 'Netherlands', 'Sweden', 'Spain', 'Belgium', 'Norway', 'Denmark']
Y_Axis['Number', 'of', 'users']: ['757000', '163000', '153000', '130000', '105000', '75000', '51000', '21000', '20000', '16000', '8500', '7800']

gold: This statistic displays the number of car sharing users in selected countries in Europe in 2014 . Germany had by far the highest number of people using car sharing companies in Europe and almost five times as many as France . In Italy there were an estimated 130,000 car share users .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in selected countries in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had templateTitle[5] far the highest templateYLabel[0] of people using templateTitle[0] templateTitle[1] companies in templateTitleSubject[0] and almost five times as many as templateXValue[2] . In templateXValue[3] there were an estimated templateYValue[3] templateTitle[0] share templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[1] have templateYLabel[2] of any templateXLabel[0] of templateTitle[6] .
generated: This statistic shows the Number of Europe sharing users Europe in 2014 .  In that year , Germany was the Car sharing have users of any Country of .

Example 58:
titleEntities: {'Subject': ['Share'], 'Date': ['2015']}
title: Share of the global agricultural machinery market , by region 2015
X_Axis['Country']: ['European_Union', 'NAFTA', 'China', 'South_America', 'India', 'Commonwealth_of_Independent_States', 'Japan', 'Turkey', 'Rest_of_World']
Y_Axis['Share', 'of', 'market']: ['26', '22', '15', '8', '6', '6', '4', '3', '10']

gold: This statistic shows the share of the global farming machinery market in 2015 , by region . The European Union and NAFTA accounted for 48 percent of the agricultural machinery market in this year , though China in third place is one of the fastest growing markets .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] farming templateTitle[3] templateYLabel[1] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . The templateXValue[0] and templateXValue[1] accounted for 48 templateScale of the templateTitle[2] templateTitle[3] templateYLabel[1] in this year , though templateXValue[2] in third place is one of the fastest templatePositiveTrend markets .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] , broken down templateTitle[4] templateXLabel[0] . According to the source , there were templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[0] .
generated: This statistic shows the Share of the Share market global agricultural in 2015 , broken down market Country .  According to the source , there were 15 market in European_Union .

Example 59:
titleEntities: {'Subject': ['Major'], 'Date': ['2017']}
title: Major species groups in global aquaculture production worldwide 2017
X_Axis['Month']: ['Carps_barbels_and_other_cyprinids', 'Miscellaneous_freshwater_fishes', 'Tilapias_and_other_cichlids', 'Oysters', 'Clams_cockles_arkshells', 'Shrimps_prawns', 'Salmons_trouts_smelts', 'Freshwater_crustaceans', 'Scallops_pectens', 'Mussels', 'Marine_fishes_not_identified']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['28345', '10433', '5881', '5711', '5658', '5512', '3477', '2526', '2185', '2164', '990']

gold: This statistic shows the top 10 species groups for aquaculture production worldwide in 2017 . In that year , with over 3.5 million metric tons , salmons , trouts and smelts represented one of the world 's most produced species groups in aquaculture .
gold_template: This statistic shows the top 10 templateTitle[1] templateTitle[2] for templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitleDate[0] . In that year , with over templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] , templateXValue[6] , templateXValue[6] and templateXValue[6] represented one of the world 's most produced templateTitle[1] templateTitle[2] in templateTitle[4] .

generated_template: This statistic shows the annual templateYLabel[0] of templateTitle[3] templateTitle[4] in selected templateTitleSubject[0] templateTitle[6] as of 2013 . As of the third templateXLabel[0] of templateTitleDate[0] , around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the annual Production of global aquaculture in selected Major worldwide as of 2013 .  As of the third Month of 2017 , around 28345 thousand metric tons .

Example 60:
titleEntities: {'Subject': ['Messenger'], 'Date': ['2014', '2017']}
title: Facebook Messenger : number of monthly active users 2014 to 2017
X_Axis['Month']: ["Sep_'17", "Apr_'17", "Jul_'16", "Apr_'16", "Dec_'15", "Jun_'15", "Mar_'15", "Nov_'14", "Apr_'14"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['1300', '1200', '1000', '900', '800', '700', '600', '500', '200']

gold: This statistic presents the number of monthly active Facebook Messenger chat app users from April 2014 to September 2017 . As of the last reported period , the mobile messenger had 1.3 billion monthly active users worldwide , ranking second among mobile chat apps worldwide . Facebook messenger users – additional information Mobile messenger apps are on the rise , with optimistic projections for this market in the coming years .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[0] templateTitleSubject[0] chat app templateYLabel[3] from 2014 to 2017 . As of the last reported period , the mobile templateTitleSubject[0] had templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide , ranking second among mobile chat apps worldwide . templateTitle[0] templateTitleSubject[0] templateYLabel[3] – additional information Mobile templateTitleSubject[0] apps are on the rise , with optimistic projections for this market in the coming years .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitleSubject[0] templateYLabel[3] worldwide as of 2017 . As of that templateXLabel[0] , the mobile messaging app announced more than templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from over templateYValue[1] templateScale MAU in 2016 . The service is one of the most popular mobile apps worldwide .
generated: This statistic shows a timeline with the amount of monthly active Messenger users worldwide as of 2017 .  As of that Month , the mobile messaging app announced more than 1300 millions monthly active users , up from over 1200 millions MAU in 2016 .  The service is one of the most popular mobile apps worldwide .

Example 61:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : Light vehicle sales by manufacturer 2019
X_Axis['Automaker']: ['Nissan', 'General_Motors', 'Volkswagen', 'Toyota', 'KIA', 'Honda', 'FCA_Mexico', 'Ford', 'Mazda', 'Hyundai', 'Baic', 'Renault', 'Suzuki', 'BMW_Group', 'Mercedes_Benz', 'Mitsubishi', 'Peugeot', 'Volvo', 'ISUZU', 'Acura', 'Lincoln', 'Infiniti', 'Land_Rover', 'Subaru', 'Jaguar', 'Smart']
Y_Axis['Number', 'of', 'units', 'sold']: ['174706', '133823', '117045', '67670', '62762', '48937', '41413', '39731', '39040', '29018', '21147', '20308', '19880', '15859', '13627', '11134', '7097', '1334', '1224', '1224', '1082', '951', '908', '790', '207', '30']

gold: The Japanese multinational Nissan was the automobile manufacturer with the highest number of light vehicles sold in Mexico , with more than 174 thousand units sold as of August 2019 . The American multinational General Motors ranked second with almost 134 thousand light vehicle units sold in 2019 .
gold_template: The Japanese multinational templateXValue[0] was the automobile templateTitle[5] with the highest templateYLabel[0] of templateTitle[1] vehicles templateYLabel[2] in templateXValue[6] , with more than 174 thousand templateYLabel[1] templateYLabel[2] as of 2019 . The American multinational templateXValue[1] ranked second with almost templateYValue[1] thousand templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[6] templateTitle[7] in templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] 's templateTitleSubject[0] 's templateTitleSubject[0] 's templateTitleSubject[0] had a company of templateYValue[max] templateScale templateYLabel[2] .
generated: The statistic shows the Mexico Light vehicle sales by manufacturer 2019 in .  In 2019 , Mexico 's had a company of 174706 % sold .

Example 62:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2008', '2019']}
title: Twitter : number of employees 2008 to 2019
X_Axis['Month']: ["Dec_'19", "Dec_'18", "Dec_'17", "Dec_'16", "Dec_'15", "Dec_'14", "Dec_'13", "Jan_'11", "Jan_'10", "Jan_'09", "Jan_'08"]
Y_Axis['Number', 'of', 'employees']: ['4900', '3920', '3372', '3583', '3900', '3638', '2712', '350', '130', '29', '8']

gold: The statistic provides the number of employees of Twitter from 2008 to 2019 . At the end of the most recently reported year , the microblogging company employed 4,900 people , up from 3,920 people in the previous year . Twitter 's corporate demography In 2018 , the majority of Twitter 's employees were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent .
gold_template: The statistic provides the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . At the end of the most recently reported year , the microblogging company employed templateYValue[max] people , up from templateYValue[1] people in the previous year . templateTitleSubject[0] 's corporate demography In 2018 , the majority of templateTitleSubject[0] 's templateYLabel[1] were male with a share of 59.8 templateScale and of a white ethnicity with 42.3 templateScale .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] in templateTitleSubject[0] as of 2019 . In 2018 , templateXValue[0] was the most popular templateTitle[1] templateYLabel[0] of templateYLabel[1] .
generated: The statistic shows the Twitter of number in Twitter as of 2019 .  In 2018 , Dec_'19 was the most popular number of employees .

Example 63:
titleEntities: {'Subject': ['Americans', 'American'], 'Date': ['2018']}
title: Americans ' level of pride to be an American 2018
X_Axis['Response']: ['Extremely', 'Very', 'Moderately', 'Only_a_little', 'Not_at_all', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['47', '25', '16', '7', '3', '1']

gold: This statistic shows the results of a 2018 survey regarding patriotism in the United States . The respondents were asked how proud they are to be an American . In 2018 , some 47 percent of survey respondents stated they were extremely proud to be an American .
gold_template: This statistic shows the results of a templateTitleDate[0] survey regarding patriotism in the templateTitle[4] . The templateYLabel[1] were asked how proud they are to be an templateTitleSubject[0] . In templateTitleDate[0] , some templateYValue[max] templateScale of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitleSubject[0] .

generated_template: This statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in templateTitle[3] as of 2018 . During the survey , templateYValue[max] templateScale of templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the results of a survey about the level pride of several individual Americans No_opinion in Americans pride as of 2018 .  During the survey , 47 % of respondents stated that they used the social networking site .

Example 64:
titleEntities: {'Subject': ['Obamacare U.S.'], 'Date': ['2019']}
title: Number of sign-ups during 2019 Obamacare open enrollment , by U.S. state
X_Axis['State']: ['Florida', 'California', 'Texas', 'North_Carolina', 'Georgia', 'Pennsylvania', 'Virginia', 'Illinois', 'Massachusetts', 'Michigan', 'New_York', 'New_Jersey', 'Washington', 'Tennessee', 'Missouri', 'South_Carolina', 'Ohio', 'Wisconsin', 'Utah', 'Colorado', 'Alabama', 'Arizona', 'Maryland', 'Oklahoma', 'Indiana', 'Oregon', 'Minnesota', 'Connecticut', 'Idaho', 'Louisiana', 'Kansas', 'Mississippi', 'Nebraska', 'Kentucky', 'Nevada', 'Maine', 'Arkansas', 'Iowa', 'Montana', 'New_Mexico', 'New_Hampshire', 'Rhode_Island', 'South_Dakota', 'Vermont', 'Wyoming', 'West_Virginia', 'Delaware', 'North_Dakota', 'District_of_Columbia', 'Hawaii', 'Alaska']
Y_Axis['Number', 'of', 'signups']: ['1783304', '1513883', '1087240', '501271', '458437', '365888', '328020', '312280', '300085', '274058', '271873', '255246', '222636', '221533', '220461', '214956', '206871', '205118', '194570', '169672', '166128', '160456', '156963', '150759', '148404', '148180', '123731', '111066', '103154', '92948', '89993', '88542', '87416', '84620', '83449', '70987', '67413', '49210', '45374', '45001', '44581', '34533', '29069', '25223', '24852', '22599', '22562', '21820', '20894', '20193', '17805']

gold: This statistic displays the number of Affordable Care Act ( Obamacare ) sign-ups during the 2019 open enrollment period as of February 2019 , by U.S. state . Until February 2019 , there were around 1.51 million sign-ups in California . Open enrollment allows U.S. citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .
gold_template: This statistic displays the templateYLabel[0] of Affordable Care Act ( templateTitleSubject[0] ) templateTitle[1] templateTitle[2] the templateTitleDate[0] templateTitle[5] templateTitle[6] period as of 2019 , templateTitle[7] templateTitleSubject[0] templateXLabel[0] . Until 2019 , there were around templateYValue[1] templateScale templateTitle[1] in templateXValue[1] . templateTitle[5] templateTitle[6] allows templateTitleSubject[0] citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitleDate[0] . As of this time , there were about templateYValue[1] templateYLabel[0] of templateYLabel[0] templateTitle[6] templateTitle[7] templateXLabel[0] .
generated: This statistic shows the Number of Obamacare U.S. sign-ups during 2019 in the Obamacare as of 2019 .  As of this time , there were about 1513883 Number of enrollment by State .

Example 65:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2018']}
title: Real GDP of Michigan 2018 , by industry
X_Axis['Industry']: ['Manufacturing', 'Finance_insurance_real_estate_rental_and_leasing', 'Professional_and_business_services', 'Government_and_government_enterprises', 'Educational_services_health_care_and_social_assistance', 'Wholesale_trade', 'Retail_trade', 'Construction', 'Arts_entertainment_recreation_accommodation_and_food_services', 'Information', 'Transportation_and_warehousing', 'Other_services_(except_government_and_government_enterprises)', 'Utilities', 'Agriculture_forestry_fishing_and_hunting', 'Mining_quarrying_and_oil_and_gas_extraction']
Y_Axis['Real', 'value', 'added', 'in', 'billion', 'chained', '(2012)', 'U.S.', 'dollars']: ['88.79', '76.55', '65.72', '47.6', '44.72', '32.33', '31.09', '16.71', '15.81', '14.24', '11.63', '10.14', '9.24', '4.23', '2.36']

gold: This graph shows the real value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by industry . In 2018 , the construction industry added 16.71 billion chained 2012 U.S. dollars of value to the total state GDP .
gold_template: This graph shows the templateXValue[1] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[7] templateXLabel[0] templateYLabel[2] templateYValue[7] templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the total state templateTitle[1] .

generated_template: This graph shows the templateXValue[0] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[last] templateXLabel[0] templateYLabel[2] templateYValue[min] templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] .
generated: This graph shows the Manufacturing value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , Industry .  In 2018 , the Mining_quarrying_and_oil_and_gas_extraction Industry added 2.36 billion chained 2012 U.S. dollars of value to the state GDP .

Example 66:
titleEntities: {'Subject': ['American'], 'Date': ['2012']}
title: Influence of friends and family on American teenagers ' decisions regarding sex in 2012
X_Axis['Response']: ['Parents', 'Friends', 'The_media', 'Religious_leaders', 'Siblings', 'Teachers_and_educators', 'Someone_else']
Y_Axis['Share', 'of', 'respondents']: ['38', '22', '9', '6', '6', '4', '10']

gold: This statistic shows the results of a 2012 survey among American teenagers aged 12 to 19 regarding the influence of their social environment on their decisions about sex . Some 38 percent of surveyed teenagers stated that parents most influenced their decisions about sex .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[4] aged 12 to 19 templateTitle[7] the templateTitle[0] of their social environment on their templateTitle[6] about templateTitle[8] . Some templateYValue[max] templateScale of surveyed templateTitle[4] stated that templateXValue[0] most influenced their templateTitle[6] about templateTitle[8] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateXValue[0] as of October templateTitleDate[0] . At that time , templateYValue[min] templateScale of templateYLabel[1] stated that they used templateXValue[last] passwords for templateXValue[0] to templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Influence nation in Parents as of October 2012 .  At that time , 4 % of respondents stated that they used Someone_else passwords for Parents to .

Example 67:
titleEntities: {'Subject': ['IT'], 'Date': ['2017']}
title: IT functions outsourced worldwide 2017
X_Axis['Response']: ['Software_application_development', 'Software_application_maintenance', 'Data_centers', 'IT_infrastructure', 'Service_desk_/_help_desk', 'Networks', 'Systems_integration', 'HR_BPO', 'IT_department', 'IT_BPO', 'KPO']
Y_Axis['Share', 'of', 'respondents']: ['64', '51', '40', '32', '32', '29', '29', '12', '12', '12', '6']

gold: The statistic reveals the IT functions most commonly outsourced by IT leaders worldwide , as of 2017 . In 2017 , software application development was outsourced by 64 percent of respondents .
gold_template: The statistic reveals the IT templateTitle[0] most commonly templateTitle[1] by IT leaders templateTitle[2] , as of templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] development was templateTitle[1] by templateYValue[max] templateScale of templateYLabel[1] .

generated_template: This graph shows the results of a survey among 559 industrial enterprises on their opinion on templateXValue[1] templateTitle[3] as of 2018 . Some templateYValue[max] templateScale of templateYLabel[1] stated they would manage to templateXValue[0] ( templateXValue[1] of templateYValue[1] templateScale ) . templateTitle[3] templateXValue[1] basis .
generated: This graph shows the results of a survey among 559 industrial enterprises on their opinion on Software_application_maintenance 2017 as of 2018 .  Some 64 % of respondents stated they would manage to Software_application_development ( Software_application_maintenance of 51 % ) .  2017 Software_application_maintenance basis .

Example 68:
titleEntities: {'Subject': ['APAC'], 'Date': ['2018', '2018']}
title: Corruption perception index APAC 2018 , by country
X_Axis['Country']: ['New_Zealand', 'Singapore', 'Australia', 'Hong_Kong', 'Japan', 'Bhutan', 'Taiwan', 'South_Korea', 'Malaysia', 'India', 'China', 'Sri_Lanka', 'Indonesia', 'Mongolia', 'Philippines', 'Thailand', 'Timor-Leste', 'Vietnam', 'Pakistan', 'Nepal', 'Myanmar', 'Laos', 'Papua_New_Guinea', 'Bangladesh', 'Cambodia', 'Afghanistan', 'North_Korea']
Y_Axis['Index', 'score']: ['87', '85', '77', '76', '73', '68', '63', '57', '47', '41', '39', '38', '38', '37', '36', '36', '35', '33', '33', '31', '29', '29', '28', '26', '20', '16', '14']

gold: This statistic shows the perceived corruption of the countries in the Asia Pacific region , as calculated by the Corruption Perception Index in 2018 . In that year , New Zealand was perceived as the least corrupt country with an index score of 87 out of 100 .
gold_template: This statistic shows the perceived templateTitle[0] of the countries in the Asia Pacific region , as calculated templateTitle[5] the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitleDate[0] . In that year , templateXValue[0] was perceived as the least corrupt templateXLabel[0] with an templateYLabel[0] templateYLabel[1] of templateYValue[max] out of 100 .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] from 1900 to templateTitleDate[max] . In 2019 , templateXValue[0] had the most templateTitleSubject[0] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] , with templateYValue[max] points .
generated: The statistic shows the APAC perception index APAC 2018 by country from 1900 to 2018 .  In 2019 , New_Zealand had the most APAC Index score in 2018 , with 87 points .

Example 69:
titleEntities: {'Subject': ['Metro Group'], 'Date': []}
title: Metro Group 's sales worldwide 2018/2019 , by region
X_Axis['Country']: ['Western_Europe_(excluding_Germany)', 'Eastern_Europe_(exluding_Russia)', 'Germany', 'Russia', 'Asia']
Y_Axis['Sales', 'in', 'million', 'euros']: ['8885', '5986', '4075', '2406', '1097']

gold: This statistic shows the global sales of the Metro Group in 2018/2019 , by region . In that year , the sales of the Metro Group in Germany amounted to approximately 2.25 billion euros . The company discontinued its hypermarket sales and its operations in China .
gold_template: This statistic shows the global templateYLabel[0] of the templateTitleSubject[0] in templateTitle[5] , templateTitle[6] templateTitle[7] . In that year , the templateYLabel[0] of the templateTitleSubject[0] in templateXValue[2] amounted to approximately 2.25 templateScale templateYLabel[2] . The company discontinued its hypermarket templateYLabel[0] and its operations in China .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateXValue[2] had the largest templateTitle[1] templateYLabel[0] of about templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Sales of the Group 's sales worldwide in .  In that year , the Germany had the largest Group Sales of about 5986 million euros .

Example 70:
titleEntities: {'Subject': ['Terrorism'], 'Date': []}
title: Terrorism - number of hostages taken by region
X_Axis['Country']: ['Africa', 'South_Asia', 'Near_East', 'East_Asia_and_Pacific', 'Western_Hemisphere', 'Europe_and_Eurasia']
Y_Axis['Number', 'of', 'hostages', 'taken']: ['2651', '1748', '1206', '246', '190', '9']

gold: The statistic shows the number of hostages taken by terrorists in 2010 by region . 2.651 people were taken hostage by terrorists in Africa .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] terrorists in 2010 templateTitle[4] templateTitle[5] . 2.651 people were templateYLabel[2] hostage templateTitle[4] terrorists in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] in templateTitle[3] as of 2020 , broken down by world templateTitle[6] . As of that period , templateYValue[max] templateScale of the templateXValue[0] were followed templateTitle[6] templateXValue[1] .
generated: This statistic shows the Number of the hostages taken in as of 2020 , broken down by world region .  As of that period , 2651 % of the Africa were followed region South_Asia .

Example 71:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2017']}
title: Luxury brand social media engagement generated by top influencers 2017
X_Axis['Brand', '(top', 'influencer)']: ['Valentino_(Demi_Lovato)', 'Tory_Burch_(Shay_Mitchell)', "Tod's_(Naomi_Campbell)", 'Tiffany_&_Co._(Reese_Witherspoon)', 'Salvatore_Ferragamo_(Nina_Dobrev)', 'Saint_Laurent_(J_Balvin)', 'Prada_(Chiara_Ferragni)', 'Michael_Kors_(Blake_Lively)', 'Louis_Vuitton_(Kevin_Ma)', 'Hugo_Boss_(Mariano_Di_Vaio)', 'Hermes_(Xenia_Tchoumi)', 'Gucci_(Nina_Dobrev)', 'Givenchy_(Nicki_Minaj)', 'Fendi_(Gigi_Hadid)', 'Dolce_&_Gabbana_(Cameron_Dallas)', 'Dior_(Rihanna)', 'Chanel_(Cara_Delevigne)', 'Celine_(Kim_Kardashian)', 'Cartier_(Nikkie_Tutorials)', 'Burberry_(Dove_Cameron)', 'Bulgari_(Bella_Hadid)', 'Bottega_Veneta_(Kris_Jenner)', 'Balenciaga_(Nicki_Minaj)', 'Average']
Y_Axis['Number', 'of', 'social', 'media', 'actions', 'per', 'post']: ['1385467', '134751', '40647', '77643', '601316', '181475', '116169', '759670', '26689', '91041', '27692', '458444', '629753', '653272', '742342', '629179', '662894', '1182087', '290287', '447287', '591423', '102457', '341862', '442341']

gold: This statistic presents the number of social media actions generated per post by top luxury brand influencer . During the first quarter of 2017 , an average social media post by Kim Kardashian generated 1.2 million social media actions . Kardashian is the top influencer for luxury brand Celine .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] templateTitle[7] templateTitleSubject[0] templateXLabel[0] influencer . During the first quarter of templateTitleDate[0] , an templateXValue[last] templateYLabel[1] templateYLabel[2] templateYLabel[5] templateTitle[6] Kim Kardashian templateTitle[5] templateYValue[17] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] . Kardashian is the templateTitle[7] influencer for templateTitleSubject[0] templateXLabel[0] templateXValue[17] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] as of 2017 . As of the fourth templateXLabel[0] of templateTitleDate[0] , templateXValue[0] had an active templateYLabel[1] of templateYValue[max] templateScale templateYLabel[5] templateYLabel[6] .
generated: This statistic shows the Luxury brand social media engagement generated as of 2017 .  As of the fourth Brand of 2017 , Valentino_(Demi_Lovato) had an active social of 1385467 million post .

Example 72:
titleEntities: {'Subject': ['Euro'], 'Date': ['2019', '2019']}
title: Monthly inflation rate in Euro area countries June 2019
X_Axis['Country']: ['Latvia', 'Slowakia', 'Netherlands', 'Estonia', 'Lithuania', 'Slovenia', 'Malta', 'Austria', 'Luxembourg', 'Germany', 'France', 'Belgium', 'Euro_area', 'Ireland', 'Finland', 'Italy', 'Portugal', 'Spain', 'Cyprus', 'Greece']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3.1', '2.7', '2.7', '2.6', '2.4', '1.9', '1.8', '1.6', '1.5', '1.5', '1.4', '1.3', '1.3', '1.1', '1.1', '0.8', '0.7', '0.6', '0.3', '0.2']

gold: The statistic shows the inflation rate in the Euro area countries in June 2019 . Inflation or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the index 's current level of inflation .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[12] countries in templateTitle[6] templateTitleDate[0] . templateYLabel[0] or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an templatePositiveTrend in the index 's current level of templateYLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[14] templateTitle[4] in templateTitle[5] templateTitleDate[0] . templateYLabel[0] or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an templatePositiveTrend in the indexes current level of templateYLabel[0] .
generated: The statistic shows the Inflation rate in the Finland area in countries 2019 .  Inflation or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) .  The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the indexes current level of Inflation .

Example 73:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['16', '24']}
title: Most important things for young people ( 16 - 24 ) in Great Britain 2013
X_Axis['Response']: ['Family', 'Friends', 'Having_a_good_education', 'Living_a_healthy_active_lifestyle', 'Having_money', 'Doing_good_things_for_the_community', 'The_environment', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['51', '14', '11', '8', '6', '3', '1', '5']

gold: This statistic shows the result of a survey question designed to find out what is most important to young people ( 16 - 24 ) in Great Britain , as of November 2013 . The majority of respondents said that their family is the most important thing to them .
gold_template: This statistic shows the result of a survey question designed to find out what is templateTitle[0] templateTitle[1] to templateTitle[4] templateTitle[5] ( templateTitle[6] - templateTitle[7] ) in templateTitleSubject[0] , as of 2013 . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateXValue[0] templateTitle[3] as of 2018 . At that time , templateYValue[max] templateScale of templateYLabel[1] stated that they used templateXValue[last] passwords for templateXValue[0] to templateXValue[0] to templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Most nation in Family for as of 2018 .  At that time , 51 % of respondents stated that they used None_of_the_above passwords for Family to .

Example 74:
titleEntities: {'Subject': ['Value'], 'Date': ['2018']}
title: Value of the leading 10 textile importers worldwide 2018 , by country
X_Axis['Country']: ['European_Union_(28)', 'United_States', 'China', 'Viet_Nam', 'Bangladesh', 'Japan', 'Hong_Kong_China', 'Indonesia', 'Mexico_', 'Turkey']
Y_Axis['Import', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['77', '30', '18', '18', '11', '9', '7', '7', '7', '6']

gold: The European Union ( EU28 ) was the leading importer of textiles worldwide , with textile imports valuing approximately 77 billion U.S. dollars in 2018 . That year , the United States and China were the next two largest importers of textiles , with imports of 30 billion U.S. dollars and 18 billion U.S. dollars respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .
gold_template: The templateXValue[0] ( EU28 ) was the templateTitle[1] importer of textiles templateTitle[5] , with templateTitle[3] imports valuing approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] . That year , the templateXValue[1] and templateXValue[2] were the next two largest templateTitle[4] of textiles , with imports of templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] and templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .

generated_template: This graph shows the largest templateTitle[3] templateTitle[4] of the templateXValue[6] States regarding trade goods in templateTitleDate[0] , by templateYLabel[0] templateYLabel[1] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of trade goods from templateXValue[2] amounted to templateYValue[2] templateScale .
generated: This graph shows the largest textile importers of the Hong_Kong_China States regarding trade goods in 2018 , by Import value .  In 2018 , the Import value of trade goods from China amounted to 18 billion .

Example 75:
titleEntities: {'Subject': ['Search'], 'Date': ['2013']}
title: Search engines : regular usage penetration 2013 , by country
X_Axis['Country']: ['Belgium', 'Norway', 'South_Africa', 'Turkey', 'Germany', 'Great_Britain', 'Australia', 'Poland', 'Sweden', 'France', 'Canada', 'Argentina', 'Italy', 'Total', 'Spain', 'Brazil', 'South_Korea', 'India', 'Mexico', 'Saudi_Arabia', 'China', 'United_States', 'Hungary', 'Russia', 'Japan', 'Indonesia']
Y_Axis['Share', 'of', 'respondents']: ['85', '85', '85', '83', '82', '82', '82', '80', '79', '78', '78', '78', '76', '74', '74', '73', '71', '70', '69', '69', '69', '68', '67', '57', '56', '40']

gold: This statistic displays the share of internet users in selected countries visiting search engines regularly as of August 2013 . Penetration was 85 percent in Belgium , Norway and South Africa .
gold_template: This statistic displays the templateYLabel[0] of internet users in selected countries visiting templateTitleSubject[0] templateTitle[1] regularly as of 2013 . templateTitle[4] was templateYValue[max] templateScale in templateXValue[0] , templateXValue[1] and templateXValue[2] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] templateTitle[3] as of templateTitleDate[0] . templateYValue[2] templateScale of templateYLabel[1] stated templateXValue[2] was currently the templateTitle[0] templateXLabel[0] in templateTitleSubject[0] templateTitle[3] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Search nation in Search usage as of 2013 .  85 % of respondents stated South_Africa was currently the Search Country in Search usage .

Example 76:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: U.S. parental digital monitoring on teen online behavior 2015
X_Axis['Response']: ['What_sites_he/she_can_access', 'What_online_accounts_he/she_can_have', 'What_he/she_can_post_online_for_others_to_see', 'The_time_of_day_he/she_can_use_technology', 'May_only_download_apps_with_age_ratings', 'Amount_of_time_he/she_can_use_technology_per_day_or_per_week', 'Must_check_devices_with_parents/leave_in_common_area_before_going_to_bed', 'When_in_home_he/she_can_use_or_be_online']
Y_Axis['Share', 'of', 'respondents']: ['79', '77', '75', '74', '67', '65', '60', '59']

gold: This statistic presents the most popular rules of parents in the United States to monitor their teen 's activities online . As of October 2015 , 77 percent of responding parents stated that they had rules about what online accounts their children were allowed to have .
gold_template: This statistic presents the most popular rules of parents in the templateTitle[0] to monitor their templateTitle[4] 's activities templateXValue[1] . As of October templateTitleDate[0] , templateYValue[1] templateScale of responding parents stated that they had rules about templateXValue[0] templateXValue[1] their children were allowed to templateXValue[1] .

generated_template: This statistic shows the results of a survey among Indian templateTitle[1] regarding their opinion on templateTitle[5] templateTitle[6] , as of 2018 . As of the survey period , templateYValue[max] templateScale of the templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the results of a survey among Indian parental regarding their opinion on online behavior , as of 2018 .  As of the survey period , 79 % of the respondents stated that they used the social networking site .

Example 77:
titleEntities: {'Subject': ['Chained'], 'Date': ['2000', '2019']}
title: Chained consumer price index of all urban consumers 2000 to 2019
X_Axis['December', 'value']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Chained', 'Consumer', 'Price', 'Index', '(1999=100)']: ['144.73', '141.7', '139.55', '137.22', '134.79', '134.21', '133.51', '131.77', '129.84', '126.14', '124.54', '121.56', '121.3', '117.0', '114.4', '111.2', '107.8', '106.0', '103.9', '102.6']

gold: This statistic shows the unadjusted chained Consumer Price Index in the United States from 2000 to 2019 , at year-end . In December 2019 , the chained consumer price index stood at 144.73 , reflecting a 44.73 percent increase from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .
gold_template: This statistic shows the unadjusted templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the country from templateXValue[last] to templateXValue[0] , at year-end . In templateXLabel[0] templateXValue[0] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] stood at templateYValue[max] , reflecting a 44.73 templateScale templatePositiveTrend from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitle[6] as of 2019 , sorted templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the Chained of consumer price index in the Chained consumers as of 2019 , sorted 2000 December .  In 2000 , around 144.73 % of all Price .

Example 78:
titleEntities: {'Subject': ['U.S. UHNW'], 'Date': ['2014']}
title: Wealth in the U.S. - UHNW ( super rich ) population in 2014 , by city
X_Axis['State']: ['New_York', 'San_Francisco', 'Los_Angeles', 'Chicago', 'Washington', 'Houston', 'Dallas', 'Toronto', 'Atlanta', 'Seattle']
Y_Axis['Number', 'of', 'Ultra-High-Net-Worth', 'people']: ['8655', '5460', '5135', '2885', '2730', '2545', '2330', '1840', '1230', '1095']

gold: This statistic shows the number of the super-rich , or Ultra-High-Net-Worth , persons in the United States in 2014 , sorted by city . New York has the largest concentration of super-rich individuals ; about 8,655 UHNW ( Ultra High Net Worth ) people are living in the metro area .
gold_template: This statistic shows the templateYLabel[0] of the super-rich , or templateYLabel[1] , persons in the templateTitle[1] in templateTitleDate[0] , sorted templateTitle[7] templateTitle[8] . templateXValue[0] has the largest concentration of super-rich individuals ; about templateYValue[max] templateTitleSubject[0] ( Ultra High Net Worth ) templateYLabel[2] are living in the metro area .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2019 , templateTitle[7] templateXLabel[0] . During that time , around templateYValue[max] templateScale of all templateTitle[2] templateTitle[3] were templateXValue[0] .
generated: This statistic shows the Number of the Wealth U.S. UHNW super in the rich as of 2019 , by State .  During that time , around 8655 % of all UHNW super were New_York .

Example 79:
titleEntities: {'Subject': ['North America'], 'Date': ['2019']}
title: The most successful music tours in North America in 2019
X_Axis['Artist', '/', 'Band']: ['The_Rolling_Stones', 'Elton_John', 'Bob_Seger_&_The_Silver_Bullet_Band', 'Pink', 'Ariana_Grande', 'Jonas_Brothers', 'Kiss', 'Fleetwood_Mac', 'Garth_Brooks', 'Justin_Timberlake', 'Billy_Joel', 'Dead_&_Company', 'Eric_Church', 'Michael_Buble', 'Trans-Siberian_Orchesta']
Y_Axis['Gross', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['177.8', '157.4', '97.0', '87.8', '82.6', '81.7', '81.6', '77.5', '76.1', '75.6', '70.4', '68.6', '68.6', '66.7', '65.7']

gold: In 2019 , the most successful music tour in North America based on gross revenue was that of The Rolling Stones , which generated 177.8 million U.S. dollars . Following closely behind was Elton John 's tour , which made 157.4 million dollars in revenue and sold over 1.15 million tickets .
gold_template: In templateTitleDate[0] , the templateTitle[0] templateTitle[1] templateTitle[2] tour in templateTitleSubject[0] based on templateYLabel[0] templateYLabel[1] was that of The templateXValue[0] , which generated templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . Following closely behind was templateXValue[1] 's tour , which made templateYValue[1] templateScale templateYLabel[4] in templateYLabel[1] and sold over 1.15 templateScale tickets .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] . In that year , there were almost templateYValue[max] templateScale of internet users .
generated: This statistic shows the Gross of adults in the most who were using most as of 2019 , sorted 2019 Artist .  In that year , there were almost 177.8 million of internet users .

Example 80:
titleEntities: {'Subject': ['H M'], 'Date': ['2019']}
title: Number of H & M store openings worldwide by country in 2019
X_Axis['Country']: ['Total', 'Franchise', 'USA', 'China', 'Japan', 'UK', 'Sweden', 'Germany', 'Italy', 'Russia', 'France', 'South_Korea', 'India', 'Mexico', 'Philippines', 'Netherlands', 'Belgium', 'Canada', 'Australia', 'Finland', 'Luxembourg', 'Poland', 'New_Zealand', 'Spain', 'South_Africa', 'Norway', 'Iceland', 'Switzerland', 'Colombia', 'Kazakhstan', 'Austria', 'Belarus', 'Uruguay', 'Malaysia', 'Chile', 'Peru', 'Hong_Kong', 'Vietnam', 'Singapore', 'Slovakia', 'Latvia', 'Lithuania', 'Denmark', 'Georgia', 'Bosnia-Herzegovina', 'Turkey', 'Romania', 'Estonia', 'Serbia', 'Ukraine', 'Portugal', 'Greece', 'Puerto_Rico', 'Slovenia', 'Hungary', 'Taiwan', 'Bulgaria', 'Cyprus', 'Macau', 'Croatia', 'Ireland', 'Czech_Republic']
Y_Axis['Number', 'of', 'H&M', 'store', 'openings']: ['375', '28', '28', '21', '15', '13', '12', '11', '10', '10', '10', '8', '8', '7', '6', '6', '6', '5', '5', '5', '5', '4', '4', '4', '4', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0']

gold: This statistic shows the number of new openings of the H & M Group stores in 2019 , by country . In 2019 , 28 new H & M stores were opened in the United States . H & M is a Swedish clothing manufacturer and retailer , based in Stockholm , Sweden .
gold_template: This statistic shows the templateYLabel[0] of templateXValue[22] templateYLabel[3] of the templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] Group stores in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , templateYValue[1] templateXValue[22] templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] stores were opened in the country . templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] is a Swedish clothing manufacturer and retailer , based in Stockholm , templateXValue[6] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , there were templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[0] .
generated: This statistic shows the Number of the H & H&M store in M store openings worldwide .  According to the source , there were 28 H&M store in Total .

Example 81:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Number of mobile internet users in the United Kingdom ( UK ) Q3 2013-Q2 2016
X_Axis['Quarter']: ['Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014', 'Q4_2013', 'Q3_2013']
Y_Axis['Users', 'in', 'millions']: ['42.77', '42.03', '41.27', '40.36', '39.56', '38.7', '37.82', '36.73', '35.78', '34.75', '33.69', '32.57']

gold: This statistic displays a forecast of the number of mobile internet users in the UK from third quarter 2013 to second quarter 2016 . It is forecast that there will be 43 million mobile internet users as of quarter two 2016 .
gold_template: This statistic displays a forecast of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[1] from third templateXLabel[0] templateXValue[10] to second templateXLabel[0] templateXValue[0] . It is forecast that there will be templateYValue[max] templateScale templateTitle[1] templateTitle[2] templateYLabel[0] as of templateXLabel[0] two templateXValue[0] .

generated_template: The timeline presents the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from the first templateXLabel[0] of templateXValue[0] . As of the fourth templateXLabel[0] of templateTitleDate[0] , there were approximately templateYValue[max] templateScale of templateXValue[0] .
generated: The timeline presents the Number mobile of the United Kingdom from the first Quarter of Q2_2016 .  As of the fourth Quarter of 2016 , there were approximately 42.77 millions of Q2_2016 .

Example 82:
titleEntities: {'Subject': ['Number'], 'Date': ['2016']}
title: Number of deaths caused by majors droughts worldwide up to 2016
X_Axis['Country', 'and', 'Year']: ['China_(1928)', 'Bangladesh_(1943)', 'India_(1942)', 'India_(1965)', 'India_(1900)', 'Soviet_Union_(1921)', 'China_(1920)', 'Ethiopia_(May_1983)', 'Sudan_(April_1983)', 'Ethiopia_(December_1973)']
Y_Axis['Number', 'of', 'deaths']: ['3000000', '1900000', '1500000', '1500000', '1250000', '1200000', '500000', '300000', '150000', '100000']

gold: This statistic illustrates deaths due to drought worldwide from 1900 to 2016  . The dry period of April 1983 in Sudan caused around 150,000 deaths . Deaths due to drought worldwide The 1928 drought in the People 's Republic of China was the deadliest drought during the period between 1900 and 2016 , causing the death of an estimated three million people .
gold_template: This statistic illustrates templateYLabel[1] due to drought templateTitle[6] from 1900 to templateTitleDate[0] . The dry period of 1983 in templateXValue[8] templateTitle[2] around templateYValue[8] templateYLabel[1] . templateYLabel[1] due to drought templateTitle[6] The 1928 drought in the People 's Republic of templateXValue[0] was the deadliest drought during the period between 1900 and templateTitleDate[0] , causing the death of an estimated templateYValue[max] templateScale people .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[6] in the templateTitle[0] from 1900 to templateTitleDate[0] . Between templateXValue[0] , templateYValue[max] accounted for templateYValue[1] of all time .
generated: The statistic shows the Number of by majors worldwide in the Number from 1900 to 2016 .  Between China_(1928) , 3000000 accounted for 1900000 of all time .

Example 83:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2016']}
title: Alibaba : mobile share of gross merchandise volume Q2 2012-Q2 2016
X_Axis['Quarter']: ["Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12"]
Y_Axis['Percentage', 'of', 'mobile', 'GMV']: ['75', '73', '68', '62', '55', '51', '42', '36', '33', '27', '20', '15', '12', '10.7', '7.4', '5.6', '4.6']

gold: This statistic gives information on the mobile share of Alibaba 's gross merchandise volume from the second quarter of 2012 to the second quarter of 2016 . Last reported quarter , mobile sales accounted for 75 percent of the group 's China retail marketplaces GMV .
gold_template: This statistic gives information on the templateYLabel[1] templateTitle[2] of templateTitleSubject[0] 's templateTitle[3] templateTitle[4] templateTitle[5] from the second templateXLabel[0] of 2012 to the second templateXLabel[0] of templateTitleDate[0] . Last reported templateXLabel[0] , templateYLabel[1] sales accounted for templateYValue[max] templateScale of the group 's China retail marketplaces templateYLabel[2] .

generated_template: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] of templateTitleDate[max] , the social network had a total of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic illustrates Alibaba 's mobile share Percentage from the first Quarter of 2016 to the fourth Quarter of 2016 .  As of the last reported Quarter of 2016 , the social network had a total of 75 percentage GMV .

Example 84:
titleEntities: {'Subject': ['Walmart'], 'Date': ['2006', '2019']}
title: Walmart 's operating income worldwide 2006 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Operating', 'income', 'in', 'billion', 'U.S.', 'dollars']: ['21.96', '20.44', '22.76', '24.11', '27.15', '26.87', '27.73', '26.49', '25.51', '23.97', '22.77', '21.92', '20.5', '18.69']

gold: The timeline shows Walmart 's operating income worldwide from 2006 to 2019 . In 2016 , Walmart 's global operating income amounted to about 24.11 billion U.S. dollars . Walmart , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores .
gold_template: The timeline shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[4] from templateXValue[last] to templateXValue[0] . In templateXValue[3] , templateTitleSubject[0] templateTitle[1] global templateYLabel[0] templateYLabel[1] amounted to about templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateXValue[last] as of 2019 . In templateTitleDate[0] , there were templateYValue[0] templateScale of the templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] .
generated: This statistic shows Walmart 's operating income in the 2006 as of 2019 .  In 2006 , there were 21.96 billion of the income 's operating income .

Example 85:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Marijuana and cannabis consumption in the past three months Canada by gender 2019
X_Axis['Sex']: ['Male', 'Female']
Y_Axis['Share', 'of', 'respondents']: ['18.4', '15.1']

gold: As of December 2019 , some 18.4 percent of surveyed males had consumed marijuana or cannabis in the past three months in Canada . In comparison , 15.1 percent of female respondents had used the recently legalized drug in the same time period . The legalization of cannabis in Canada Following a similar trend in the United States , the legalization of recreational marijuana in Canada has become a hot topic in politics and in the public realm .
gold_template: As of 2019 , some templateYValue[max] templateScale of surveyed males had consumed templateTitle[0] or templateTitle[1] in the templateTitle[3] three templateTitle[5] in templateTitleSubject[0] . In comparison , templateYValue[min] templateScale of templateXValue[last] templateYLabel[1] had used the recently legalized drug in the same time period . The legalization of templateTitle[1] in templateTitleSubject[0] Following a similar trend in the country , the legalization of recreational templateTitle[0] in templateTitleSubject[0] has become a hot topic in politics and in the public realm .

generated_template: This statistic presents the templateTitle[1] of templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . According to cases handled templateTitle[5] templateTitleSubject[0] harassment support group WHOA , templateYValue[max] templateScale of reporting templateTitle[3] were templateXValue[0] .
generated: This statistic presents the cannabis of Canada cannabis past in 2019 , months Canada .  According to cases handled months Canada harassment support group WHOA , 18.4 % of reporting past were Male .

Example 86:
titleEntities: {'Subject': ['Latin American'], 'Date': ['2018', '2018']}
title: 2018 FIFA World Cup : most valued Latin American teams
X_Axis['Country']: ['Brazil', 'Argentina', 'Uruguay', 'Colombia', 'Mexico', 'Costa_Rica', 'Peru', 'Panama']
Y_Axis['Value', 'in', 'million', 'U.S.', 'dollars']: ['981.0', '699.0', '373.0', '251.1', '154.6', '40.15', '38.53', '8.23']

gold: The statistic presents the market value of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . The Brazilian squad was the Latin American team with the highest market value , amounting to 981 million euros . Argentina ranked second , with a market value of 699 million euros .
gold_template: The statistic presents the market templateYLabel[0] of all templateTitleSubject[0] soccer templateTitle[8] participating in the templateTitleDate[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . The Brazilian squad was the templateTitleSubject[0] team with the highest market templateYLabel[0] , amounting to templateYValue[max] templateScale euros . templateXValue[1] ranked second , with a market templateYLabel[0] of templateYValue[1] templateScale euros .

generated_template: This statistic depicts the largest templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] . In that year , templateXValue[0] was the second largest templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] a total of templateYValue[max] thousand templateYLabel[2] .
generated: This statistic depicts the largest Latin American FIFA in 2018 .  In that year , Brazil was the second largest Value of Cup most million U.S. a total of 981.0 thousand U.S. .

Example 87:
titleEntities: {'Subject': ['Iran'], 'Date': ['2017']}
title: Main export partners of Iran 2017
X_Axis['Country']: ['Japan', 'Italy', 'Turkey', 'South_Korea', 'India', 'China']
Y_Axis['Share', 'in', 'total', 'exports']: ['5.3', '5.7', '11.1', '11.4', '15.1', '27.5']

gold: This statistic shows Iran 's main export partners in 2017 , sorted by their share in total exports . In 2017 , Iran 's main export partner was China with a share of 27.5 percent in all exports .
gold_template: This statistic shows templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] , sorted by their templateYLabel[0] in templateYLabel[1] templateYLabel[2] . In templateTitleDate[0] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] partner was templateXValue[last] with a templateYLabel[0] of templateYValue[max] templateScale in all templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the Main export partners Iran in 2017 .  In 2017 , the most important export partner Iran was Japan , accounting Iran 27.5 % of all exports .

Example 88:
titleEntities: {'Subject': ['Reddit.com'], 'Date': ['2019']}
title: Distribution of Reddit.com traffic 2019 , by country
X_Axis['Country']: ['United_States', 'United_Kingdom', 'Canada', 'Australia', 'Germany']
Y_Axis['Share', 'of', 'traffic']: ['49.57', '7.79', '7.75', '4.28', '3.27']

gold: Reddit is one of the world 's most popular websites and as of October 2019 , the United States generated 49.57 percent of desktop traffic to the forum site . Reddit in the United States In August 2019 , Reddit accounted for over less than one percent of social media website traffic in the United States , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news by posting links and let other users vote and comment on them .
gold_template: Reddit is one of the world 's most popular websites and as of October templateTitleDate[0] , the templateXValue[0] generated templateYValue[max] templateScale of desktop templateYLabel[1] to the forum site . Reddit in the templateXValue[0] In 2019 , Reddit accounted for over less than one templateScale of social media website templateYLabel[1] in the templateXValue[0] , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news templateTitle[4] posting links and let other users vote and comment on them .

generated_template: This statistic represents the regional templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last templateYValue[min] months . As of October templateTitleDate[0] , the templateXValue[0] accounted for templateYValue[max] templateScale of desktop templateYLabel[1] to the visual blogging site during this period of time .
generated: This statistic represents the regional Distribution of Reddit.com traffic in the last 3.27 months .  As of October 2019 , the United_States accounted for 49.57 % of desktop traffic to the visual blogging site during this period of time .

Example 89:
titleEntities: {'Subject': ['Netflix'], 'Date': ['2017']}
title: Netflix content watching worldwide by device 2017
X_Axis['Platform']: ['Television', 'PC/laptop', 'Mobile', 'Tablet']
Y_Axis['Share', 'of', 'time', 'spent']: ['70', '15', '10', '5']

gold: The figure shows the share of time spent watching Netflix worldwide in 2017 , by device . According to the source , 70 percent of content streamed on Netflix worldwide in 2017 was via a connected TV .
gold_template: The figure shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateTitle[5] . According to the source , templateYValue[max] templateScale of templateTitle[1] streamed on templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] was via a connected TV .

generated_template: This statistic shows the templateYLabel[0] of times templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] as of 2018 , templateTitle[6] templateTitle[7] templateTitle[8] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] company had a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Share of times Netflix content watching in Netflix as of 2018 , 2017 Platform .  In 2017 , Television company had a Share of 70 million spent .

Example 90:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Daily engagement rate of U.S. mobile social users 2019
X_Axis['Platform']: ['Facebook', 'Instagram', 'Snapchat', 'Facebook_Messenger', 'WhatsApp', 'Reddit', 'TikTok', 'Twitter', 'Tumblr', 'Pinterest']
Y_Axis['Share', 'of', 'daily', 'active', 'users']: ['63.7', '58.8', '55.3', '54.9', '46.2', '43.8', '40', '38.3', '23.6', '19.3']

gold: As of December 2019 , it was found that 46.2 percent of U.S. Android users who had installed WhatsApp were also daily active users . According to App Ape , mobile Facebook app audiences in the United States showed the highest daily app engagement rate with almost 64 percent .
gold_template: As of 2019 , it was found that templateYValue[4] templateScale of templateTitleSubject[0] Android templateYLabel[3] who had installed templateXValue[4] were also templateYLabel[1] templateYLabel[2] templateYLabel[3] . According to App Ape , templateTitle[4] templateXValue[0] app audiences in the templateTitle[3] showed the highest templateYLabel[1] app templateTitle[1] templateTitle[2] with almost templateYValue[max] templateScale .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in the templateTitle[5] as of 2019 , ranked first . During the measured period , it was found that templateYValue[min] templateScale of the templateYLabel[1] templateYLabel[2] in the social networking app .
generated: This statistic gives information on the Daily engagement rate networking mobile in the social as of 2019 , ranked first .  During the measured period , it was found that 19.3 % of the daily active in the social networking app .

Example 91:
titleEntities: {'Subject': ['Most'], 'Date': ['2019']}
title: Most utilized frameworks among developers worldwide 2019
X_Axis['Response']: ['Node.js', '.NET', '.NET_Core', 'Pandas', 'Unity_3D', 'React_Native', 'TensorFlow', 'Ansible', 'Cordova', 'Xamarin', 'Apache_Spark', 'Hadoop', 'Unreal_Engine', 'Flutter', 'Torch/PyTorch', 'Puppet', 'Chef', 'CryEngine']
Y_Axis['Share', 'of', 'respondents']: ['49.9', '37.4', '23.7', '12.7', '11.3', '10.5', '10.3', '9.4', '7.1', '6.5', '5.8', '4.9', '3.5', '3.4', '3.3', '2.7', '2.5', '0.6']

gold: The statistic shows the most used libraries , frameworks , and tools among software developers worldwide , as of early 2019 . According to the survey , 49.9 percent of respondents used Node.js , while 37.4 percent used .NET . The least used framework was CryEngine with only 0.6 percent of respondents reporting to use it .
gold_template: The statistic shows the templateTitleSubject[0] used libraries , templateTitle[2] , and tools templateTitle[3] software templateTitle[4] templateTitle[5] , as of early templateTitleDate[0] . According to the survey , templateYValue[max] templateScale of templateYLabel[1] used templateXValue[0] , while templateYValue[1] templateScale used templateXValue[1] . The least used framework was templateXValue[last] with only templateYValue[min] templateScale of templateYLabel[1] reporting to use it .

generated_template: As of early templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the most templateTitle[2] templateTitle[3] by getting in templateXValue[1] . Approximately templateYValue[max] templateScale of templateYLabel[1] stated that they like templateXValue[0] templateTitleDate[0] , followed by templateXValue[1] and templateXValue[2] . Headphone Market The retail value of global second and templateXValue[2] followed by templateYValue[1] and templateXValue[2] would be a `` hot trend '' for templateTitle[4] templateTitle[5] in Brazil , which and templateYValue[2] templateScale templateTitle[3] .
generated: As of early 2019 , Node.js and .NET were the most frameworks among by getting in .NET .  Approximately 49.9 % of respondents stated that they like Node.js 2019 , followed by .NET and .NET_Core .  Headphone Market The retail value of global second and .NET_Core followed by 37.4 and .NET_Core would be a `` hot trend '' for developers worldwide in Brazil , which and 23.7 % among .

Example 92:
titleEntities: {'Subject': ['France'], 'Date': ['2018']}
title: Best selling video games on all gaming platforms in France 2018 , by sales volume
X_Axis['Month']: ['FIFA_19', 'Red_Dead_Redemption_2', 'Call_of_Duty:_Black_Ops_4', 'Mario_Kart_8_Deluxe', 'Super_Mario_Party', 'Spider-Man', 'Super_Smash_Bros._Ultimate', 'Super_Mario_Odyssey', 'Assassin’s_Creed_Odyssey', 'God_of_War']
Y_Axis['Number', 'of', 'units', 'sold', 'in', 'thousands']: ['1353.4', '1011.0', '565.0', '542.4', '380.3', '345.3', '335.6', '328.7', '322.8', '301.4']

gold: This statistic represents the ten best selling video games across all gaming platforms combined in France in 2018 , by number of physical software units sold . With around 1.4 million units sold , FIFA 19 was the most sold video game in France that year .
gold_template: This statistic represents the ten templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] across templateTitle[4] templateTitle[5] templateTitle[6] combined in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[9] templateYLabel[0] of physical software templateYLabel[1] templateYLabel[2] . With around templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] , templateXValue[0] was the most templateYLabel[2] templateTitle[2] game in templateTitleSubject[0] that year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] . In templateXValue[0] , Switzerland and had an templateTitle[0] templateTitle[1] templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Best selling video games in the France ( ) from 2018 to .  In FIFA_19 , Switzerland and had an Best selling Number of 1353.4 thousands .

Example 93:
titleEntities: {'Subject': ['Latin America'], 'Date': ['2020']}
title: Latin America : gender gap index 2020 , by country
X_Axis['Country']: ['Nicaragua', 'Costa_Rica', 'Colombia', 'Trinidad_and_Tobago', 'Mexico', 'Barbados', 'Argentina', 'Cuba', 'Uruguay', 'Jamaica', 'Bolivia', 'Panama', 'Ecuador', 'Chile', 'Honduras', 'Bahamas', 'Peru', 'Venezuela', 'Suriname', 'El_Salvador', 'Dominican_Republic', 'Brazil', 'Paraguay', 'Belize', 'Guatemala']
Y_Axis['Index', 'score']: ['0.81', '0.78', '0.76', '0.76', '0.75', '0.75', '0.75', '0.75', '0.74', '0.74', '0.73', '0.73', '0.73', '0.72', '0.72', '0.72', '0.71', '0.71', '0.71', '0.71', '0.7', '0.69', '0.68', '0.67', '0.67']

gold: In 2020 , Nicaragua was the Latin American country with the highest gender gap index , with 0.81 points . Guatemala , on the other hand , had the worst score in the region with 0.67 points , which shows a gender pay gap of 33 percent ( on average , women had 33 percent less opportunities than men in Guatemala ) .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] American templateXLabel[0] with the highest templateTitle[2] templateTitle[3] templateYLabel[0] , with templateYValue[max] points . templateXValue[last] , on the other hand , had the worst templateYLabel[1] in the region with templateYValue[min] points , which shows a templateTitle[2] pay templateTitle[3] of 33 templateScale ( on average , women had 33 templateScale less opportunities than men in templateXValue[last] ) .

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] American templateXLabel[0] with the highest templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] , with templateYValue[max] points . templateXValue[last] , on the other hand , had the worst templateYLabel[1] in the region with templateYValue[min] points , which shows a templateTitle[4] templateTitle[5] templateTitle[6] of 54 templateScale ( on average , women 's income in templateXValue[last] was estimated to be 54 templateScale lower than men 's ) .
generated: In 2020 , Nicaragua was the Latin America American Country with the highest index 2020 by Index , with 0.81 points .  Guatemala , on the other hand , had the worst score in the region with 0.67 points , which shows a index 2020 by of 54 % ( on average , women 's income in Guatemala was estimated to be 54 % lower than men 's ) .

Example 94:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009']}
title: U.S. households - average hours per day of computer usage in 2009
X_Axis['Hours', 'used', 'per', 'day']: ['Less_than_1_hour', '1_to_3_hours', '3_to_6_hours', '6_to_10_hours', 'More_than_10_hours', 'No_computers']
Y_Axis['Housing', 'units', 'in', 'millions']: ['16.6', '32.0', '17.5', '7.7', '12.5', '27.4']

gold: This statistic shows the hours per day a computer is used in a U.S. home in 2009 . In 17.5 million U.S. households , a computer is used 3 to 6 hours per day .
gold_template: This statistic shows the templateXValue[1] templateXLabel[2] templateXLabel[3] a templateTitle[6] is templateXLabel[1] in a templateTitleSubject[0] home in templateTitleDate[0] . In templateYValue[2] templateScale templateTitleSubject[0] templateTitle[1] , a templateTitle[6] is templateXLabel[1] templateXValue[1] to templateXValue[2] templateXValue[1] templateXLabel[2] templateXLabel[3] .

generated_template: The statistic depicts the templateTitle[0] of templateTitle[1] in templateTitle[6] templateXValue[1] templateTitleSubject[0] in templateTitleDate[0] . The templateXValue[0] templateXLabel[0] of templateTitle[6] templateYLabel[0] was at around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .
generated: The statistic depicts the U.S. of households in computer 1_to_3_hours U.S. in 2009 .  The Less_than_1_hour Hours of computer Housing was at around 32.0 millions in 2009 .

Example 95:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Number of professionals at the leading accounting firms in the U.S. 2019
X_Axis['Accounting', 'firm']: ['Deloitte', 'PwC', 'Ernst_&_Young', 'KPMG', 'RSM_US', 'Grant_Thornton', 'BDO_USA', 'CliftonLarsonAllen', 'Crowe_Horwath', 'CBIZ_/_Mayer_Hoffman_McCann', 'Moss_Adams', 'CohnReznick', 'BKD', 'Baker_Tilly_Virchow_Krause', 'Marcum', 'Plante_Moran', 'Dixon_Hughes_Goodman', 'EisnerAmper', 'Wipfli', 'Carr_Riggs_&_Ingram', 'Eide_Bailly', 'Citrin_Cooperman_&_Co.', 'Armanino', 'Withum', 'Mazars_USA']
Y_Axis['Number', 'of', 'professionals']: ['73855', '35350', '33600', '26447', '7252', '6616', '4958', '4056', '3402', '2470', '2066', '1908', '1824', '2095', '1219', '1796', '1385', '979', '1229', '1346', '1386', '675', '900', '720', '601']

gold: This statistic shows the number of professionals at the leading accounting firms in the United States as of March 2019 . The largest accounting firm in the U.S. , Deloitte , employed 73,855 professionals at the end of their fiscal year in June 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at the templateTitle[2] templateXLabel[0] templateTitle[4] in the templateTitle[5] as of 2019 . The largest templateXLabel[0] templateXLabel[1] in the templateTitleSubject[0] , templateXValue[0] , employed templateYValue[max] templateYLabel[1] at the end of their fiscal year in 2018 .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , fourth templateXLabel[0] . As of that templateXLabel[0] of time , templateXValue[0] had a total of templateYValue[max] templateScale times .
generated: This statistic shows the Number of adults in the U.S. who were using Number as of 2019 , fourth Accounting .  As of that Accounting of time , Deloitte had a total of 73855 million times .

Example 96:
titleEntities: {'Subject': ['Terrorism'], 'Date': []}
title: Terrorism - kidnappings grouped by country
X_Axis['Country']: ['Somalia', 'Afghanistan', 'Pakistan', 'India', 'Colombia', 'Congo_Democratic_Republic', 'Sudan', 'Yemen', 'Central_African_Republic', 'Iraq', 'Gaza_Strip', 'Philippines', 'Turkey', 'Burma', 'Nigeria']
Y_Axis['Number', 'of', 'kidnappings']: ['2527', '902', '430', '341', '285', '189', '159', '117', '115', '111', '107', '102', '63', '25', '17']

gold: The statistic shows the number of kidnappings due to terrorism grouped by country in 2011 . 2,527 people werde kidnapped in Somalia .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitleSubject[0] templateTitle[2] templateTitle[3] templateXLabel[0] in 2011 . templateYValue[max] people werde kidnapped in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] worldwide as of 2019 , sorted by templateXLabel[0] . In that year , there were templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[0] .
generated: This statistic shows the Number of the kidnappings worldwide as of 2019 , sorted by Country .  In that year , there were 902 kidnappings in Somalia .

Example 97:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Production volume of tomatoes in Europe 2018
X_Axis['Country']: ['Turkey', 'Italy', 'Spain', 'Portugal', 'Poland', 'Netherlands', 'Greece', 'France', 'Romania', 'Albania_', 'Belgium', 'Hungary', 'Serbia_', 'North_Macedonia', 'Bulgaria', 'Germany', 'United_Kingdom', 'Austria', 'Switzerland', 'Bosnia_and_Herzegovina_', 'Finland', 'Croatia', 'Slovakia', 'Kosovo', 'Sweden', 'Cyprus_', 'Lithuania', 'Denmark', 'Malta', 'Norway', 'Czechia', 'Slovenia', 'Latvia', 'Ireland', 'Montenegro_', 'Iceland', 'Estonia', 'Luxembourg']
Y_Axis['Volume', 'in', 'thousand', 'tonnes']: ['12150.0', '6055.43', '4768.82', '1329.76', '928.83', '910.0', '885.15', '712.02', '451.76', '286.8', '258.68', '204.0', '170.76', '161.62', '148.08', '103.27', '66.9', '58.15', '48.24', '43.92', '39.32', '22.59', '22.29', '18.8', '18.23', '15.7', '12.19', '11.76', '11.19', '10.57', '10.08', '8.39', '5.2', '3.92', '3.27', '1.21', '0.29', '0.01']

gold: This statistic displays the volume of harvested tomato production in Europe in 2018 , by country . In this year , Turkey produced the largest volume of tomatoes at over 12.15 million tonnes , followed by Italy with 6.06 million tonnes produced .
gold_template: This statistic displays the templateYLabel[0] of harvested tomato templateTitle[0] in templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] . In this year , templateXValue[0] produced the largest templateYLabel[0] of templateTitle[2] at over templateYValue[max] templateScale templateYLabel[2] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[2] produced .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateYLabel[2] in templateTitle[3] as of 2020 , sorted by templateXLabel[0] . In that year , templateXValue[0] had the highest rate of templateTitle[6] templateTitle[7] as a templateTitle[1] templateTitle[2] templateTitle[3] .
generated: This statistic shows the Volume of the volume tonnes in Europe as of 2020 , sorted by Country .  In that year , Turkey had the highest rate of 2018 as a volume tomatoes Europe .

Example 98:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013', '2019']}
title: Internet retail sales value trend monthly in the United Kingdom ( UK ) 2013 to 2019
X_Axis['Month']: ['Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14', 'Dec_13', 'Nov_13', 'Oct_13', 'Sep_13', 'Aug_13', 'Jul_13', 'Jun_13', 'May_13', 'Apr_13', 'Mar_13', 'Feb_13', 'Jan_13']
Y_Axis['Percentage', 'change']: ['12.4', '11.8', '11.6', '13.3', '13', '12.9', '12', '14.2', '16.1', '15.8', '21', '13', '16.5', '15.3', '13.6', '9', '10.1', '9.6', '13.6', '19', '20.1', '20.1', '17.1', '22.1', '24.3', '22.2', '16.1', '30.1', '28.4', '30', '25.8', '23.6', '17.8', '17.2', '19.6', '12.9', '11.6', '12.4', '13.8', '8.4', '14.9', '11.6', '14.4', '8.7', '13.2', '13.9', '9.4', '14.3', '12.5', '11.4', '19.3', '11.1', '19.7', '12', '10.1', '13.7', '14.8', '15', '16.6', '15', '9.1', '11.9', '11', '13.5', '17.5', '17.8', '17.4', '21.7', '10.2', '18.1', '10.7', '13.9', '18.8', '12.9', '12.8']

gold: This statistic displays the monthly trend of the value of retail internet sales in the United Kingdom ( UK ) from January 2013 to March 2019 . In March 2019 , retail sales increased by 12.4 percent .
gold_template: This statistic displays the templateTitle[5] templateTitle[4] of the templateTitle[3] of templateTitle[1] templateTitle[0] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2013 to 2019 . In 2019 , templateTitle[1] templateTitle[2] templatePositiveTrend by templateYValue[0] templateScale .

generated_template: The number of templateTitle[1] templateTitle[3] templateTitle[4] in the European Union templatePositiveTrend by templateYValue[0] templateScale from 2018 to 2019 . This is primarily due to a low baseline from 2018 which saw low sales . In 2018 , European new-car intenders flocked to dealerships ahead of price increases tied to tougher emissions tests , and EU auto sales templatePositiveTrend templateYValue[max] templateScale over 2017 .
generated: The number of retail value trend in the European Union increased by 12.4 percentage from 2018 to 2019 .  This is primarily due to a low baseline from 2018 which saw low sales .  In 2018 , European new-car intenders flocked to dealerships ahead of price increases tied to tougher emissions tests , and EU auto sales increased 30.1 percentage over 2017 .

Example 99:
titleEntities: {'Subject': ['Philadelphia'], 'Date': ['2019']}
title: Player expenses ( payroll ) of Philadelphia Union 2019
X_Axis['Month']: ['Marco_Fabian', 'Alejandro_Bedoya', 'Sergio_Santos', 'Haris_Medunjanin', 'Jamiro_Monteiro', 'Andre_Blake', 'Kai_Wagner', 'Ilsinho', 'Kacper_Przybylko', 'Jack_Elliott', 'Raymon_Gaddis', 'Warren_Creavalle', 'Aurelien_Collin', 'Fafa_Picault', 'Carlos_Miguel_Coronel', 'Auston_Trusty', 'Fabinho_Alves', 'Brenden_Aaronson', 'Mark_McKenzie', 'Derrick_Jones', 'Cory_Burke', 'Matt_Freese', 'Olivier_Mbaizo', 'Anthony_Fontana', 'Matthew_Real', 'Michee_Ngalina']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['2274.09', '1266.25', '668.5', '595.01', '569.2', '550.0', '360.15', '357.0', '277.0', '265.0', '190.0', '175.3', '175.0', '173.67', '148.43', '124.1', '120.0', '95.81', '82.23', '80.9', '79.72', '77.65', '70.88', '70.26', '57.23', '56.25']

gold: The statistic shows the player expenses ( payroll ) of the Philadelphia Union club of Major League Soccer by player in 2019 . Marco Fabian received a salary of 2.27 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[1] Santos received a salary of templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Philadelphia club of Major League Soccer by Player in 2019 .  Alejandro_Bedoya Santos received a salary of 1266.25 thousand U.S. dollars .

Example 100:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading diversified financial service companies in the U.S. 2018 , by revenue
X_Axis['Month']: ['Fannie_Mae', 'Freddie_Mac', 'American_Express', 'INTL_FCStone', 'Icahn_Enterprises', 'Synchrony_Financial', 'Marsh_&_McLennan', 'Ameriprise_Financial', 'Ally_Financial', 'Voya_Financial']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['120.1', '73.6', '43.28', '27.66', '18.98', '18.25', '14.95', '12.92', '10.47', '8.93']

gold: The statistic displays the leading diversified financial service companies in the United States in 2018 , by revenue . In that year , Fannie Mae was ranked first with revenue of around 120.1 billion U.S. dollars .
gold_template: The statistic displays the templateTitle[0] templateTitle[1] templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateYLabel[0] . In that year , templateXValue[0] was ranked first with templateYLabel[0] of around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in selected templateTitleSubject[0] templateTitle[6] as of templateTitleDate[0] . As of that year , templateTitleSubject[0] had a total of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the diversified financial service companies U.S. in selected U.S. 2018 as of 2018 .  As of that year , U.S. had a total of 120.1 billion U.S. dollars .

Example 101:
titleEntities: {'Subject': ['United Kingdom'], 'Date': []}
title: Budgeted public sector spending in the United Kingdom 2019/20 by function
X_Axis['Industry']: ['Social_protection', 'Health', 'Education', 'Other_(including_EU_transactions', 'Defense', 'Net_debt_increase', 'Transport', 'Public_order_and_safety', 'Personal_social_services', 'Housing_and_environment', 'Housing_and_community_amenities', 'Industry_agriculture_and_employment']
Y_Axis['Amount', 'budgeted', 'in', 'billion', 'GBP']: ['256', '166', '103', '58', '52', '43', '37', '35', '34', '32', '32', '25']

gold: This statistic presents what the government of the United Kingdom has budgeted for public sector spending for the fiscal year 2019/20 , in billion British Pounds . During this year the government has budgeted 256 billion pounds for social protection , which includes spending on pensions and other welfare benefits . Government spending on health is expected to be 166 billion pounds , the second highest spending function in this fiscal year .
gold_template: This statistic presents what the government of the templateTitleSubject[0] has templateYLabel[1] for templateXValue[7] templateTitle[2] templateTitle[3] for the fiscal year templateTitle[6] , in templateScale British Pounds . During this year the government has templateYLabel[1] templateYValue[max] templateScale pounds for templateXValue[0] , which includes templateTitle[3] on pensions and templateXValue[3] welfare benefits . Government templateTitle[3] on templateXValue[1] is expected to be templateYValue[1] templateScale pounds , the second highest templateTitle[3] templateTitle[8] in this fiscal year .

generated_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateXLabel[0] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] templateTitle[6] as of 2019 , templateTitle[8] templateXLabel[0] . In templateTitleDate[0] , there were templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This graph shows the Budgeted public of United Kingdom Industry billion GBP of United Kingdom 2019/20 as of 2019 , function Industry .  In , there were 256 billion budgeted GBP .

Example 102:
titleEntities: {'Subject': ['Video'], 'Date': ['2016']}
title: Video game industry 's wealthiest entrepreneurs 2016
X_Axis['Entrepreneur', '(company)']: ['Ma_Huateng_(Tencent)', 'William_Ding_(NetEase)', 'Shi_Yuzhu_(Giant_Interactive)', 'Kwon_Hyuk-Bin_(SmileGate)', 'Kim_Jung-Ju_(Nexon)']
Y_Axis['Net', 'worth', 'in', 'billion', 'U.S.', 'dollars']: ['21.9', '11.5', '5.4', '4.9', '3.5']

gold: The graph shows the estimated net worth of the wealthiest entrepreneurs in the video game industry worldwide as of July 2016 . According to the source , Ma Huateng , the founder and chairman as well as CEO of Tencent , was worth 21.9 billion U.S. dollars in the measured period . Overall , Tencent reported 70.84 billion Chinese yuan revenue from its online games in 2016 .
gold_template: The graph shows the estimated templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] in the templateTitleSubject[0] templateTitle[1] templateTitle[2] worldwide as of 2016 . According to the source , templateXValue[0] , the founder and chairman as well as CEO of Tencent , was templateYLabel[1] templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the measured period . Overall , Tencent reported 70.84 templateScale Chinese yuan revenue from its online games in templateTitleDate[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[6] as of templateTitleDate[0] . In that year , the number of the templateTitle[2] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] templateXLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Net worth of the industry 's wealthiest entrepreneurs 2016 in the 2016 as of 2016 .  In that year , the number of the industry entrepreneurs 2016 Entrepreneur amounted to 21.9 billion U.S. dollars .

Example 103:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2017']}
title: Most popular mobile payment services in Sweden 2017
X_Axis['Response']: ['Swish', 'PayPal_Mobile', 'WyWallet', 'Other', 'None']
Y_Axis['Share', 'of', 'respondents']: ['75', '14', '3', '5', '21']

gold: Swish was the most popular mobile payment service in Sweden in 2017 . The share of respondents who used Swish amounted to 75 percent . It was followed by PayPal Mobile , which was used by 14 percent of respondents that year .
gold_template: templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[1] templateTitle[3] service in templateTitleSubject[0] in templateTitleDate[0] . The templateYLabel[0] of templateYLabel[1] who used templateXValue[0] amounted to templateYValue[max] templateScale . It was followed by templateXValue[1] , which was used by templateYValue[1] templateScale of templateYLabel[1] that year .

generated_template: This statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books , which of the following templateTitle[0] templateXValue[last] . templateYValue[max] templateScale of templateYLabel[1] stated that they used templateTitle[4] templateTitle[5] stated that they did n't identify themselves as having a templateXValue[last] .
generated: This statistic shows the results of a survey about the mobile payment of several individual Most None in Sweden 2015 , phrased PayPal_Mobile the question : `` except reading books , which of the following Most None .  75 % of respondents stated that they used services Sweden stated that they did n't identify themselves as having a None .

Example 104:
titleEntities: {'Subject': ['Q1'], 'Date': ['2017']}
title: Natural and synthetic diamonds price difference Q1 2016-Q3 2017
X_Axis['Quarter']: ['Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016']
Y_Axis['Price', 'difference', 'in', 'percent']: ['-18.3', '-15.7', '-20.1', '-18.1', '-15.3', '-17.8', '-17.6']

gold: This statistic shows the price difference between synthetic/lab-created diamonds and natural diamonds from the first quarter of 2016 ( Q1 2016 ) to the third quarter of 2017 ( Q3 2017 ) in percent . In the second quarter of 2017 , lab-created diamonds cost some 15.7 percent less than naturally occurring diamonds .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] between synthetic/lab-created templateTitle[2] and templateTitle[0] templateTitle[2] from the first templateXLabel[0] of templateXValue[3] ( templateXValue[2] templateXValue[3] ) to the third templateXLabel[0] of templateXValue[0] ( templateXValue[0] ) in templateScale . In the second templateXLabel[0] of templateXValue[0] , lab-created templateTitle[2] cost some 15.7 templateScale less than naturally occurring templateTitle[2] .

generated_template: The timeline shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] from the first templateXLabel[0] of templateXValue[0] to the fourth templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitle[0] 's templateTitle[1] had fourth templateXLabel[0] of templateYValue[0] templateScale of growth .
generated: The timeline shows the Natural synthetic diamonds of Q1 from the first Quarter of Q3_2017 to the fourth Quarter of Q3_2017 .  In the fourth Quarter of 2017 , Natural 's synthetic had fourth Quarter of -18.3 percent of growth .

Example 105:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Number of forcible rape cases by state U.S. 2018
X_Axis['State']: ['California', 'Texas', 'Florida', 'Michigan', 'New_York', 'Illinois', 'Ohio', 'Pennsylvania', 'Colorado', 'Arizona', 'Washington', 'Virginia', 'Missouri', 'Tennessee', 'Georgia', 'North_Carolina', 'Minnesota', 'South_Carolina', 'Massachusetts', 'Indiana', 'Nevada', 'Oklahoma', 'Wisconsin', 'Arkansas', 'Louisiana', 'Alabama', 'Maryland', 'Oregon', 'Utah', 'Kentucky', 'Kansas', 'New_Jersey', 'New_Mexico', 'Nebraska', 'Alaska', 'Iowa', 'Connecticut', 'Idaho', 'West_Virginia', 'Hawaii', 'South_Dakota', 'Montana', 'Mississippi', 'New_Hampshire', 'Rhode_Island', 'District_of_Columbia', 'Maine', 'North_Dakota', 'Delaware', 'Vermont', 'Wyoming']
Y_Axis['Number', 'of', 'forcible', 'rapes']: ['15505', '14693', '8438', '7690', '6575', '5859', '5300', '4483', '4070', '3638', '3413', '2924', '2912', '2821', '2651', '2633', '2462', '2434', '2410', '2370', '2329', '2299', '2248', '2196', '2085', '1996', '1979', '1975', '1753', '1707', '1567', '1424', '1354', '1233', '1192', '976', '840', '791', '652', '625', '614', '551', '537', '534', '481', '450', '446', '397', '338', '287', '243']

gold: In 2018 , California had the highest number of forcible rape cases in the United States , with 15,505 reported rapes . Vermont had the lowest number of reported forcible rape cases at 243 . Number vs. rate It is perhaps unsurprising that California had the highest number of reported rapes in the United States in 2018 , as California is the state with the highest population .
gold_template: In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in the templateTitle[6] , with templateYValue[max] reported templateYLabel[2] . templateXValue[49] had the lowest templateYLabel[0] of reported templateYLabel[1] templateTitle[2] templateTitle[3] at templateYValue[min] . templateYLabel[0] vs. rate It is perhaps unsurprising that templateXValue[0] had the highest templateYLabel[0] of reported templateYLabel[2] in the templateTitle[6] in templateTitleDate[0] , as templateXValue[0] is the templateXLabel[0] with the highest population .

generated_template: This graph shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] in the templateTitle[3] as of 2018 , templateTitle[5] templateXLabel[0] . As of templateTitleDate[0] , there were about templateYValue[1] templateYLabel[0] of templateXValue[0] .
generated: This graph shows the Number of U.S. forcible in the cases as of 2018 , state .  As of 2018 , there were about 14693 Number of California .

Example 106:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2017']}
title: Global YouTube usage for music consumption 2017 , by country
X_Axis['Country']: ['Mexico', 'Brazil', 'Italy', 'Spain', 'South_Korea', 'Canada', 'Total', 'Germany', 'United_States', 'France', 'Great_Britain', 'Sweden', 'Australia', 'Japan']
Y_Axis['Share', 'of', 'respondents']: ['97', '95', '90', '90', '86', '84', '83', '82', '82', '81', '79', '79', '77', '72']

gold: This statistic shows the share of global users who have accessed YouTube to consume music as of 2017 , sorted by country . During the survey period , 82 percent of respondents from the United States said that they had used YouTube for music .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] users who have accessed templateTitleSubject[0] to consume templateTitle[4] as of templateTitleDate[0] , sorted templateTitle[7] templateXLabel[0] . During the survey period , templateYValue[7] templateScale of templateYLabel[1] from the templateXValue[8] said that they had used templateTitleSubject[0] templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the global templateTitle[2] of templateTitleSubject[0] templateTitle[1] in selected countries . The templateXLabel[0] with the highest rate of templateTitleSubject[0] templateTitle[1] templateTitle[2] was templateXValue[1] , with templateYValue[max] templateScale of templateYLabel[1] reporting that they knew about templateTitleSubject[0] templateTitle[1] . According to Ipsos , templateYValue[18] templateScale of global templateYLabel[1] were aware of templateTitleSubject[0] templateTitle[1] .
generated: This statistic shows the global usage of YouTube in selected countries .  The Country with the highest rate of YouTube usage was Brazil , with 97 % of respondents reporting that they knew about YouTube .  According to Ipsos , 72 % of global respondents were aware of YouTube .

Example 107:
titleEntities: {'Subject': ['Russia'], 'Date': ['2017']}
title: Main export partners for Russia 2017
X_Axis['Country']: ['China', 'Netherlands', 'Germany', 'Belarus', 'Turkey']
Y_Axis['Share', 'of', 'total', 'exports']: ['10.9', '10', '7.1', '5.1', '4.9']

gold: This statistic shows the main export partners for Russia in 2017 . In 2017 , the most important export partner for Russia was China , accounting for 10.9 percent of all exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] templateScale of all templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] were the templateXValue[0] , accounting templateTitle[3] templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the Main export partners for Russia in 2017 .  In 2017 , the most important export partner for Russia were the China , accounting for 10.9 % of all exports .

Example 108:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2013', '2016']}
title: LinkedIn : unique mobile visiting members 2013 to 2016
X_Axis['Quarter']: ["Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13"]
Y_Axis['Number', 'of', 'mobile', 'visiting', 'members', 'in', 'millions']: ['63', '61', '57', '55', '51', '49', '45', '42', '38', '35', '31', '29', '26', '20']

gold: This timeline displays the number of unique mobile visiting members to social network LinkedIn . As of the second quarter of 2016 , LinkedIn had an average of 63 million unique visiting members via mobile . These accounted for 59 percent of all unique visiting members .
gold_template: This timeline displays the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] to social network templateTitleSubject[0] . As of the second templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] had an average of templateYValue[max] templateScale templateTitle[1] templateYLabel[2] templateYLabel[3] via templateYLabel[1] . These accounted for 59 templateScale of all templateTitle[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic gives information on the templateYLabel[0] of the templateTitle[1] templateTitle[2] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the most recently reported templateXLabel[0] , the social network templateXLabel[0] of templateYValue[0] templateScale of the mobile internet users .
generated: This statistic gives information on the Number of the unique mobile from the first Quarter of 2013 to the fourth Quarter of 2016 .  In the most recently reported Quarter , the social network Quarter of 63 millions of the mobile internet users .

Example 109:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2020', '2020']}
title: Countries with the most Facebook users 2020
X_Axis['Country']: ['India', 'United_States', 'Indonesia', 'Brazil', 'Mexico', 'Philippines', 'Vietnam', 'Thailand', 'Egypt', 'Turkey', 'United_Kingdom', 'Bangladesh', 'Pakistan', 'Colombia', 'France', 'Argentina', 'Italy', 'Germany', 'Nigeria', 'Malaysia', 'Peru', 'Canada', 'Maynmar', 'Spain']
Y_Axis['Number', 'of', 'Facebook', 'users', 'in', 'millions']: ['260', '180', '130', '120', '84', '70', '61', '47', '38', '37', '37', '34', '33', '32', '31', '29', '29', '28', '24', '22', '22', '21', '21', '21']

gold: There are over 260 million Facebook users in India alone , making it the leading country in terms of Facebook audience size . To put this into context , if India 's Facebook audience were a country then it would be ranked fourth in terms of largest population worldwide . Apart from India , there are several other markets with more than 100 million Facebook users each : The United States , Indonesia , and Brazil with 180 million , 130 million , and 120 million Facebook users respectively .
gold_template: There are over templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[0] alone , making it the leading templateXLabel[0] in terms of templateYLabel[1] audience size . To put this into context , if templateXValue[0] 's templateYLabel[1] audience were a templateXLabel[0] then it would be ranked fourth in terms of largest population worldwide . Apart from templateXValue[0] , there are several other markets templateTitle[1] more than 100 templateScale templateYLabel[1] templateYLabel[2] each : The templateXValue[1] , templateXValue[2] , and templateXValue[3] templateTitle[1] templateYValue[1] templateScale , templateYValue[2] templateScale , and templateYValue[3] templateScale templateYLabel[1] templateYLabel[2] respectively .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] worldwide as of 2019 , sorted by templateXLabel[0] . As of that month , templateXValue[0] had the largest templateYLabel[0] of templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .
generated: This statistic shows the Number of the Facebook users worldwide as of 2019 , sorted by Country .  As of that month , India had the largest Number of 84 Facebook users millions worldwide .

Example 110:
titleEntities: {'Subject': ['Frequency'], 'Date': ['2014', '2014']}
title: Frequency of eating fast food in the U.S. as of August 2014
X_Axis['Response']: ['Daily', 'A_few_times_per_week', 'About_once_per_week', 'A_few_times_per_month', 'About_once_per_month', 'Rarely', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['5.2', '19', '21', '22.7', '12.2', '17.5', '2.2']

gold: This statistic shows the frequency with which consumers ate fast food in the United States as of August 2014 . During the survey , 21 percent of respondents said that they ate fast food about once per week .
gold_template: This statistic shows the templateTitleSubject[0] with which consumers ate templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitle[5] templateTitleDate[0] . During the survey , templateYValue[2] templateScale of templateYLabel[1] said that they ate templateTitle[2] templateTitle[3] templateXValue[2] per templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2013 . During the survey , templateYValue[2] templateScale of the templateYLabel[1] stated that they planned to visit a templateXValue[2] or templateXValue[2] .
generated: This statistic shows the Frequency eating fast food U.S. August in the 2014 as of 2013 .  During the survey , 21 % of the respondents stated that they planned to visit a About_once_per_week or .

Example 111:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. social media user account privacy 2018
X_Axis['Response']: ['Yes_all_of_my_social_media_accounts_are_private', 'Yes_a_few_of_my_social_meda_accounts_are_private', 'Yes_one_of_my_social_media_accounts_is_private', 'No_none_of_my_social_media_accounts_are_private', "Don't_know/No_opinion"]
Y_Axis['Share', 'of', 'respondents']: ['45', '20', '7', '19', '8']

gold: This statistic presents the percentage of social media users in the United States who have a private social media account as of September 2018 . According to the findings , 45 percent of respondents reported that all of their social media accounts were private , while 19 percent of respondents stated the opposite saying none of their social media accounts were private at all .
gold_template: This statistic presents the templateScale of templateXValue[0] users in the templateTitle[0] who have a templateXValue[0] media templateTitle[4] as of 2018 . According to the findings , templateYValue[max] templateScale of templateYLabel[1] reported that templateXValue[0] of their templateXValue[0] accounts were templateXValue[0] , while templateYValue[3] templateScale of templateYLabel[1] stated the opposite saying templateXValue[3] of their templateXValue[0] accounts were templateXValue[0] at templateXValue[0] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] templateTitle[3] as of templateTitleDate[0] . Some templateYValue[max] templateScale of templateYLabel[1] stated that they knew templateXValue[0] a templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the U.S. nation in U.S. user as of 2018 .  Some 45 % of respondents stated that they knew Yes_all_of_my_social_media_accounts_are_private a .

Example 112:
titleEntities: {'Subject': ['Rakuten Group', 'GMS'], 'Date': ['2014', '2019']}
title: Rakuten Group : quarterly domestic e-commerce GMS 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14"]
Y_Axis['Gross', 'merchandise', 'sales', 'in', 'billion', 'Japanese', 'yen']: ['1053.1', '1012.8', '921.9', '871.7', '974.4', '855.3', '805.2', '770.0', '868.0', '771.3', '735.8', '704.0', '778.4', '748.9', '705.4', '682.8', '741.8', '685.3', '640.8', '611.9', '671.0', '607.7', '548.6', '607.0']

gold: In the fourth quarter of 2019 , the domestic gross transaction value of the Rakuten Group 's e-commerce sectors amounted to approximately 1.05 trillion Japanese yen , up 8.1 percent compared to the fourth quarter of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , the templateTitle[3] templateYLabel[0] transaction value of the templateTitleSubject[0] 's templateTitle[4] sectors amounted to approximately templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] , up 8.1 templateScale compared to the fourth templateXLabel[0] of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .

generated_template: This statistic gives information on the templateYLabel[0] of templateTitleSubject[0] templateYLabel[2] templateTitle[1] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitle[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] amounted to templateYValue[min] templateScale of Japanese e-commerce company .
generated: This statistic gives information on the Gross of Rakuten Group sales Group from the first Quarter of 2014 to the fourth Quarter of 2019 .  In the fourth Quarter of 2019 , Rakuten 's Group merchandise sales amounted to 548.6 billion of Japanese e-commerce company .

Example 113:
titleEntities: {'Subject': ['Annual'], 'Date': ['2009', '2015']}
title: Annual growth of the global generic market 2009 - 2015 by region
X_Axis['Country']: ['Rest_of_world', 'World', 'North_America', 'Europe', 'Japan']
Y_Axis['Average', 'annual', 'growth']: ['19', '12', '11', '9', '6']

gold: This statistic depicts the average annual growth of the global generic market between 2009 and 2015 , by region . In Japan , the average annual growth is estimated to be six percent in that period .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] between templateTitleDate[min] and templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateXValue[last] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] is estimated to be templateYValue[min] templateScale in that period .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] worldwide as of 2019 , templateTitle[6] templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , templateTitle[0] templateTitle[1] templateTitle[2] had an average of templateYValue[2] templateScale of the U.S. dollars .
generated: This statistic shows the Average of Annual growth worldwide as of 2019 , 2015 by Country .  In 2009 , Annual growth global had an average of 11 % of the U.S. dollars .

Example 114:
titleEntities: {'Subject': ['U.S. U.S.'], 'Date': ['2017']}
title: Biggest U.S. government cyber security problems according to U.S. adults 2017
X_Axis['Response']: ['Hacking_by_foreign_governments', 'Securing_confidential_intelligence_reports', 'Securing_citizen_records_(ex._IRS_filings)', 'Eavesdropping_through_smart_technology', 'Securing_records_of_military_personnel', 'Interfering_with_elections_through_propaganda', 'Interfering_with_elections_by_hacking_the_counting_of_ballots']
Y_Axis['Share', 'of', 'respondents']: ['72', '23', '17', '11', '11', '10', '8']

gold: This statistic presents a ranking of the biggest cyber security problems facing the U.S. government according to adults in the United States . During the January 2017 survey period , 72 percent of respondents stated that hacking by foreign governments was the U.S. government 's biggest cyber security problem .
gold_template: This statistic presents a ranking of the templateTitle[0] templateTitle[3] templateTitle[4] templateTitle[5] facing the templateTitleSubject[0] templateTitle[2] templateTitle[6] to templateTitle[8] in the templateTitle[1] . During the 2017 survey period , templateYValue[max] templateScale of templateYLabel[1] stated that templateXValue[0] foreign templateXValue[0] was the templateTitleSubject[0] templateTitle[2] 's templateTitle[0] templateTitle[3] templateTitle[4] problem .

generated_template: This statistic shows the results of a survey about the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] as of 2018 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that templateXValue[0] ( templateXValue[1] and templateXValue[0] .
generated: This statistic shows the results of a survey about the most government cyber security problems in according as of 2018 .  During the survey , 72 % of the respondents stated that Hacking_by_foreign_governments ( Securing_confidential_intelligence_reports and Hacking_by_foreign_governments .

Example 115:
titleEntities: {'Subject': ['Top'], 'Date': ['2014']}
title: Top countries by pharmaceutical industry employment 2014
X_Axis['Country']: ['China', 'India', 'United_States', 'Germany', 'Brazil', 'Japan', 'Russian_Federation', 'Indonesia', 'Italy', 'Mexico', 'Thailand', 'Egypt', 'France', 'Spain', 'Switzerland', 'United_Kingdom', 'Vietnam', 'Poland', 'Pakistan', 'Turkey']
Y_Axis['Number', 'of', 'employees']: ['2301534', '567469', '251995', '128000', '105253', '91529', '66523', '64059', '59300', '51125', '48000', '47753', '47000', '43723', '43258', '41690', '39749', '38000', '36336', '35100']

gold: This statistic displays the top 20 countries worldwide based on the number of people employed in the pharmaceutical industry as of 2014 . The pharmaceutical industry in Japan counted over 91,500 employees that year .
gold_template: This statistic displays the templateTitleSubject[0] 20 templateTitle[1] worldwide based on the templateYLabel[0] of people employed in the templateTitle[3] templateTitle[4] as of templateTitleDate[0] . The templateTitle[3] templateTitle[4] in templateXValue[5] counted over 91,500 templateYLabel[1] that year .

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[0] largest templateTitle[1] fourth as of 2018 , followed by templateXValue[1] and templateXValue[2] with over templateYValue[2] thousand people . As of templateTitleDate[0] , templateXValue[0] 's templateTitle[0] templateTitleSubject[0] 's second and the world 's third templateTitle[0] .
generated: In 2014 , China was the Top largest countries fourth as of 2018 , followed by India and United_States with over 251995 thousand people .  As of 2014 , China 's Top second and the world 's third Top .

Example 116:
titleEntities: {'Subject': ['Botswana'], 'Date': ['2018']}
title: Urbanization in Botswana 2018
X_Axis['urban', 'population', '(', 'of', 'total)']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Unnamed:', '1']: ['69.45', '68.7', '67.93', '67.16', '66.37', '65.57', '64.77', '63.87', '62.41', '60.94', '59.44']

gold: This statistic shows the percentage of the total population living in urban areas in Botswana from 2008 to 2018 . In 2018 , 69.45 percent of the total population of Botswana was living in urban areas .
gold_template: This statistic shows the templateScale of the total templateXLabel[1] living in templateXLabel[0] areas in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[max] templateScale of the total templateXLabel[1] of templateTitleSubject[0] was living in templateXLabel[0] areas .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] as of October templateTitleDate[0] . As of the measured period , templateTitleSubject[0] had an average of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] for the photo sharing app .
generated: This statistic shows the Botswana 2018 as of October 2018 .  As of the measured period , Botswana had an average of 69.45 % 1 for the photo sharing app .

Example 117:
titleEntities: {'Subject': ['Google'], 'Date': ['2017', '2017']}
title: Price of selected acquisitions by Google 2017
X_Axis['Company']: ['Motorola_Mobility_(2012)', 'Nest_Labs_(2014)', 'DoubleClick_(2008)', 'YouTube_(2006)', 'Part_of_HTC_mobile_division_and_licenses_(2017)', 'Waze_(2013)', 'AdMob_(2009)', 'ITA_Software_(2012)', 'Postini_(2007)', 'Apigee_(2016)', 'DeepMind_(2014)', 'Skybox_Imaging_(2014)', 'Admeld_(2011)', 'Bebop_(2015)', 'Wildfire_(2012)', 'Slide_(2010)', 'Widevine_Technologies_(2010)', 'Zagat_(2011)', 'On2_Technologies_(2010)', 'Channel_Intelligence_(2013)', 'Divide_(2014)', 'dMarc_Broadcasting_(2006)', 'Applied_Semantics_(2003)', 'Meebo_(2012)', 'FeedBurner_(2007)', 'Invite_Media_(2010)', 'Global_IP_Solutions_(2010)', 'Android_(2012)']
Y_Axis['Price', 'in', 'million', 'U.S.', 'dollars']: ['12500.0', '3200.0', '3100.0', '1650.0', '1100.0', '1100.0', '750.0', '700.0', '625.0', '625.0', '500.0', '500.0', '391.08', '380.2', '350.0', '182.0', '160.0', '151.0', '130.0', '125.0', '120.0', '102.0', '102.0', '100.0', '100.0', '80.0', '68.2', '50.0']

gold: This statistic shows a selection of companies Google , Inc. has acquired since 2003 , and their respective price . In June 2013 , Google acquired social traffic app Waze for 1.1 billion U.S. dollars . The internet company 's most expensive acquisition was Motorola Mobility in August 2011 , tallying 12.5 billion U.S. dollars .
gold_template: This statistic shows a selection of companies templateTitleSubject[0] , Inc. has acquired since 2003 , and their respective templateYLabel[0] . In 2013 , templateTitleSubject[0] acquired social traffic app templateXValue[5] for templateYValue[4] templateScale templateYLabel[2] templateYLabel[3] . The internet templateXLabel[0] 's most expensive acquisition was templateXValue[0] in 2011 , tallying templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitleSubject[0] producer as of 2019 , templateXValue[1] templateTitleSubject[0] 's templateTitle[1] a templateYLabel[0] of templateYValue[1] templateScale of their by templateXValue[0] . templateTitleSubject[0] is a British pounds as of the fourth templateXLabel[0] .
generated: The Google producer as of 2019 , Nest_Labs_(2014) Google 's selected a Price of 3200.0 million of their by Motorola_Mobility_(2012) .  Google is a British pounds as of the fourth Company .

Example 118:
titleEntities: {'Subject': ['Americans'], 'Date': ['2018']}
title: Americans ' moral stance towards abortion in 2018
X_Axis['Response']: ['Morally_acceptable', 'Morally_wrong', 'Depends_on_situation', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['43', '49', '7', '1']

gold: This statistic shows the results of a survey among Americans regarding their moral stance towards abortion in 2018 . In 2018 , 43 percent of respondents stated that they think having an abortion is morally acceptable , while 48 percent considered it morally wrong .
gold_template: This statistic shows the results of a survey among templateTitleSubject[0] regarding their templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[0] templateScale of templateYLabel[1] stated that they think having an templateTitle[5] is templateXValue[0] , while 48 templateScale considered it templateXValue[0] templateXValue[1] .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitleDate[0] . During this survey , templateYValue[max] templateScale of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] , while templateYValue[min] templateScale said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding abortion or 2018 in .  During this survey , 49 % of respondents stated they think abortion or 2018 are Morally_acceptable , while 1 % said it Depends_on_situation on the situation .

Example 119:
titleEntities: {'Subject': ['EU'], 'Date': ['2017']}
title: EU operating budgetary balances in 2017 , by member state
X_Axis['Country']: ['Poland', 'Greece', 'Romania', 'Hungary', 'Czech_Republic', 'Portugal', 'Bulgaria', 'Lithuania', 'Slowakia', 'Spain', 'Latvia', 'Estonia', 'Croatia', 'Slovenia', 'Malta', 'Cyprus', 'Luxembourg', 'Ireland', 'Finland', 'Denmark', 'Belgium', 'Austria', 'Netherlands', 'Sweden', 'Italy', 'France', 'United_Kingdom', 'Germany']
Y_Axis['Operating', 'budgetary', 'balances', 'in', 'billion', 'euros']: ['8.57', '3.74', '3.38', '3.14', '2.48', '2.44', '1.47', '1.27', '0.98', '0.73', '0.53', '0.47', '0.26', '0.15', '0.1', '0.05', '0.01', '-0.17', '-0.28', '-0.7', '-0.72', '-0.93', '-1.39', '-1.4', '-3.58', '-4.57', '-5.35', '-10.68']

gold: This statistic shows the operating budgetary balances of the EU member states in 2017 . A negative budgetary balance means that a country contributes more to the EU budget than it receives from it , a positive balance means the country contributes less than it receives . In 2017 , Germany contributed the most with approximately 10.68 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[6] states in templateTitleDate[0] . A negative templateYLabel[1] balance means that a templateXLabel[0] contributes more to the templateTitleSubject[0] budget than it receives from it , a positive balance means the templateXLabel[0] contributes less than it receives . In templateTitleDate[0] , templateXValue[last] contributed the most with approximately 10.68 templateScale templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] templateTitle[5] templateTitle[6] as of templateTitleDate[0] . As of templateTitleDate[0] , templateXValue[0] had a templateYLabel[0] of templateYValue[max] British pounds templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Operating of the EU operating budgetary balances in the 2017 by member as of 2017 .  As of 2017 , Poland had a Operating of 8.57 British pounds budgetary balances .

Example 120:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2015', '2019']}
title: Waitrose sales growth year-on-year in Great Britain 2015 to 2019
X_Axis['12', 'week', 'period', 'ending']: ['14_Jul_19', '16_Jun_19', '21_Apr_19', '24_Mar_19', '24_Feb_19', '28_Jan_19', '30_Dec_18', '2_Dec_18', '6_Nov_18', '8_Oct_18', '10_Sep_18', '13_Aug_18', '15_Jul_18', '17_Jun_18', '22_Apr_18', '25_Mar_18', '25_Feb_18', '28_Jan_18', '31_Dec_17', '3_Dec_17', '8_Oct_17', '10_Sep_17', '13_Aug_17', '16_Jul_17', '18_Jun_17', '21_May_17', '23_Apr_17', '26_Mar_17', '26_Feb_17', '29_Jan_17', '1_Jan_17', '4_Dec_16', '6_Nov_16', '9_Oct_16', '11_Sep_16', '14_Aug_16', '17_Jul_16', '19_Jun_16', '22_May_16', '24_Apr_16', '27_Mar_16', '31_Jan_16', '3_Jan_16', '6_Dec_15', '8_Nov_15', '11_Oct_15', '13_Sept_15', '16_Aug_15', '19_Jul_15']
Y_Axis['Percentage', 'growth', '(year-on-year)']: ['-1.9', '-', '0.7', '1.3', '1', '0.2', '-1.7', '-0.7', '-0.1', '0.1', '0.8', '2.4', '2.8', '0.1', '0.2', '1.5', '2.3', '1.5', '2.3', '1.6', '2.3', '2.4', '2.8', '2.8', '5.3', '3.3', '3.1', '0.3', '2.9', '3.4', '3', '1.1', '3', '3.5', '3.4', '1.4', '1.6', '1.3', '2.1', '1.5', '1.7', '0.1', '1.5', '2.7', '2.7', '2.1', '2.9', '3.7', '3']

gold: Waitrose sales have decreased by 1.9 percent in Great Britain over a 12-week period ending July 12 , 2019 compared to the same time period in 2018 . Waitrose has seen its sales grow during the last three and a half years . The second quarter of 2017 saw the highest growth , with sales going up over five percent .
gold_template: templateTitle[0] templateTitle[1] have templateNegativeTrend by 1.9 templateScale in templateTitleSubject[0] over a 12-week templateXLabel[2] templateXLabel[3] 12 , templateTitleDate[max] compared to the same time templateXLabel[2] in 2018 . templateTitle[0] has seen its templateTitle[1] grow during the last templateXValue[19] and a half years . The second quarter of 2017 saw the highest templateYLabel[1] , with templateTitle[1] going up over five templateScale .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] fourth quarter of templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , templateTitle[0] templateTitle[1] had an average of almost templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Waitrose sales growth year-on-year of Great Britain fourth quarter of 2015 , Great 12 .  In 2015 , Waitrose sales had an average of almost 2.8 percentage (year-on-year) .

Example 121:
titleEntities: {'Subject': ['June'], 'Date': ['2019', '2019']}
title: Business climate index of June 2019
X_Axis['Months', 'from', 'June', '2018', 'to', 'June', '2019']: ["Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18"]
Y_Axis['Index', 'points', '(50', '=', 'neutral)']: ['56.9', '56.5', '58.4', '61.9', '64.5', '64.7', '63.8', '63.2', '53.7', '52.8', '53.3', '50.2', '49.6']

gold: This statistic shows the business climate index for Brazil from June 2018 to June 2019 . The index is based on a survey of approximately 2,500 companies . Figures above 50 represent an optimistic outlook , while figures below 50 show a pessimistic business climate .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] templateXLabel[2] templateXLabel[3] to templateXLabel[2] templateXLabel[5] . The templateYLabel[0] is based on a survey of approximately 2,500 companies . Figures above templateYValue[11] represent an optimistic outlook , while figures below templateYValue[11] show a pessimistic templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateTitle[7] ( templateTitleSubject[0] ) in templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the Index of the climate index June 2019 in the 2019 ( June ) in 2019 .  In 2019 , around 64.7 % of all (50 .

Example 122:
titleEntities: {'Subject': ['Bible U.S.'], 'Date': ['2017']}
title: Preferred Bible version in the U.S. 2017
X_Axis['Response']: ['King_James_Version', 'New_International_Version', 'English_Standard_Version', 'New_King_James_Version', 'Amplified', 'Christian_Community', 'New_American_Standard', 'New_Living_Translation', 'Revised_Standard', 'Contemporary_English_Version', 'New_American_Bible', 'All_others_(1_or_less_combined)', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['31', '13', '9', '7', '7', '4', '3', '2', '2', '2', '2', '9', '8']

gold: The graph presents data on the popularity of the versions of the Bible read in the United States as of January 2017 . During the survey , 31 percent of the respondents stated they most often read the King James Version of the Bible . During the same survey , 32 percent of respondents stated that they had never read the Bible , whilst 16 percent stated that they read the Bible every day .
gold_template: The graph presents data on the popularity of the versions of the templateXValue[10] read in the templateTitle[3] as of 2017 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated they most often read the templateXValue[0] Version of the templateXValue[10] . During the same survey , 32 templateScale of templateYLabel[1] stated that they had never read the templateXValue[10] , whilst 16 templateScale stated that they read the templateXValue[10] every day .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitleSubject[1] as of 2018 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] named watching templateXValue[0] as their website templateXValue[0] .
generated: The statistic shows the Preferred Bible version U.S. 2017 Bible U.S. in the Bible U.S. as of 2018 .  During the survey , 31 % of the respondents named watching King_James_Version as their website King_James_Version .

Example 123:
titleEntities: {'Subject': ['June'], 'Date': ['2019', '2019']}
title: Consumer confidence index of June 2019
X_Axis['Months', 'from', 'June', '2018', 'to', 'June', '2019']: ["Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17"]
Y_Axis['Index', 'points', '(2001', '=', '100)']: ['114.3', '113.6', '110.6', '-', '104.7', '101.6', '98.3', '102.2', '102.2', '101.9', '102.7', '102.9', '100.5']

gold: This statistic shows the consumer confidence index for Brazil from December 2017 to December 2018 . The index is composed of several different indices , including an assessment of one 's personal financial situation . In December 2018 , Brazil 's consumer confidence was at 114.3 points .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] 2017 to 2018 . The templateYLabel[0] is composed of several different indices , including an assessment of one 's personal financial situation . In 2018 , Brazil 's templateTitle[0] templateTitle[1] was at templateYValue[0] templateYLabel[1] .

generated_template: This graph shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] templateTitle[5] templateTitle[6] of the templateTitle[6] templateTitle[7] ( templateTitleSubject[0] ) in templateTitleDate[0] . In templateTitleDate[0] , there were about templateYValue[1] templateScale templateYLabel[0] .
generated: This graph shows the Consumer confidence index 2019 of the 2019 ( June ) in 2019 .  In 2019 , there were about 113.6 % Index .

Example 124:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Canada - average weekly hours worked at the main job , by industry 2019
X_Axis['Industry']: ['Forestry_fishing_mining_quarrying_oil_and_gas', 'Agriculture', 'Goods-producing_sector', 'Construction', 'Transportation_and_warehousing', 'Manufacturing', 'Utilities', 'Professional_scientific_and_technical_services', 'Public_administration', 'Finance_insurance_real_estate_rental_and_leasing', 'Total_employed_all_industries', 'Other_services_(except_public_administration)', 'Services-producing_sector', 'Health_care_and_social_assistance', 'Business_building_and_other_support_services', 'Wholesale_and_retail_trade', 'Information_culture_and_recreation', 'Educational_services', 'Accommodation_and_food_services']
Y_Axis['Average', 'usual', 'weekly', 'hours']: ['45.0', '43.5', '40.4', '40.3', '40.2', '39.3', '38.6', '37.0', '36.7', '36.7', '35.7', '34.9', '34.5', '34.2', '33.8', '33.5', '32.5', '31.8', '29.8']

gold: This statistic shows the average usual weekly hours worked in Canada in 2019 , distinguished by industry . In 2019 , Canadian employees in agriculture were working about 43.5 hours a week , which is above the national average of 35.7 hours .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , distinguished templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , Canadian employees in templateXValue[1] were working about templateYValue[1] templateYLabel[3] a week , which is above the national templateYLabel[0] of templateYValue[10] templateYLabel[3] .

generated_template: This graph shows the templateXLabel[0] templateTitle[3] of the templateTitle[6] of templateTitleSubject[0] templateYLabel[2] in templateTitleDate[0] , sorted by templateXLabel[0] . In templateTitleDate[0] , the templateXValue[2] templateXValue[4] had a templateXValue[0] of templateYValue[4] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This graph shows the Industry hours of the job of Canada weekly in 2019 , sorted by Industry .  In 2019 , the Goods-producing_sector Transportation_and_warehousing had a Forestry_fishing_mining_quarrying_oil_and_gas of 40.2 % usual weekly hours .

Example 125:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018', '2018']}
title: Italy : volume of crude oil imported Q1 2018 , by country of origin
X_Axis['Country']: ['Azarbaijan', 'Iran', 'Iraq', 'Libya', 'Saudi_Arabia', 'Russia', 'Nigeria', 'Kazakhstan', 'USA', 'Angola', 'Canada', 'Kuwait', 'Egypt', 'Cameroon', 'Algeria', 'Equatorial_Guinea', 'Tunisia', 'Mauritania', 'Albania']
Y_Axis['Import', 'volume', 'in', 'tons']: ['12298989', '9324007', '2041664', '1840713', '1825182', '1052134', '767828', '703232', '504954', '322012', '317132', '312218', '204085', '185753', '155279', '89845', '56891', '33791', '123']

gold: During the first quarter of 2018 , Azerbaijan exported roughly 12.3 million tons of crude oil to Italy , establishing itself as the major crude oil supplier for the country . Iran followed with 9.3 million tons . The amount of crude oil imported from other suppliers was lower , during the first quarter of 2018 .
gold_template: During the first quarter of templateTitleDate[0] , Azerbaijan exported roughly templateYValue[max] templateScale templateYLabel[2] of templateTitle[2] templateTitle[3] to templateTitleSubject[0] , establishing itself as the major templateTitle[2] templateTitle[3] supplier for the templateXLabel[0] . templateXValue[1] followed with templateYValue[1] templateScale templateYLabel[2] . The amount of templateTitle[2] templateTitle[3] templateTitle[4] from other suppliers was lower , during the first quarter of templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[6] as of 2020 . According to the measured period , templateXValue[0] had the highest prevalence of templateTitle[3] of templateTitle[6] templateTitle[7] templateXValue[last] templateXLabel[0] .
generated: This statistic shows the Import of the Italy volume crude oil in 2018 as of 2020 .  According to the measured period , Azarbaijan had the highest prevalence of oil 2018 by Albania Country .

Example 126:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Leading crowdfunding platforms in the U.S. 2017 , by number of offerings
X_Axis['Country']: ['Wefunder', 'Start_Engine', 'Seed_Invest', 'uFunding', 'Dream_Funded', 'TruCrowd', 'Nextseed', 'Net_Capital', 'Microventures', 'Jumpstart_Micro', 'Flashfunders', 'Republic', 'GridShare', 'Growth_Fountain', 'Venture.co', 'Crowd_Source_Funded', 'FundingWonder', 'ibankers', 'Local_Stake', 'Open_Night_Capital']
Y_Axis['Amount', 'of', 'offerings']: ['95', '52', '29', '18', '14', '13', '13', '12', '11', '10', '9', '9', '7', '5', '4', '3', '2', '2', '1', '1']

gold: This statistic shows the leading crowdfunding platforms in the United States as of May 2017 , by number of offerings . Wefunder had 95 offerings , which made it the largest platform in terms of offerings as of May 2017 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] as of 2017 , templateTitle[5] templateTitle[6] of templateYLabel[1] . templateXValue[0] had templateYValue[max] templateYLabel[1] , which made it the largest platform in terms of templateYLabel[1] as of 2017 .

generated_template: This statistic shows the templateYLabel[0] of their templateTitle[1] templateTitle[2] in templateTitle[3] as of 2020 , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , templateTitle[0] templateTitle[1] a total of templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[0] .
generated: This statistic shows the Amount of their crowdfunding platforms in U.S. as of 2020 , 2017 Country .  In 2017 , Leading crowdfunding a total of 52 offerings in Wefunder .

Example 127:
titleEntities: {'Subject': ['Information Technology', 'Western Europe'], 'Date': ['2019']}
title: Information Technology ( IT ) : revenue in Western Europe Q4 2015-Q3 2019
X_Axis['Quarter']: ['Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['13.54', '12.83', '16.56', '17.06', '13.6', '12.44', '14.24', '16.3', '13.22', '11.81', '13.46', '16.48', '12.66', '12.21', '13.7', '18.0']

gold: The statistic shows trends in Information Technology ( IT ) revenues in the technical consumer goods market in Western Europe from the fourth quarter of 2015 to the third quarter of 2019 . Revenue reached 13.54 billion euros by the end of Q3 2019 .
gold_template: The statistic shows trends in templateTitleSubject[0] ( IT ) revenues in the technical consumer goods market in templateTitleSubject[1] from the fourth templateXLabel[0] of templateXValue[last] to the third templateXLabel[0] of templateXValue[0] . templateYLabel[0] reached templateYValue[0] templateScale templateYLabel[2] by the end of templateXValue[0] .

generated_template: German software company templateTitleSubject[0] reported revenues of around templateYValue[max] templateScale templateYLabel[2] in the fourth templateXLabel[0] of templateXValue[0] , marking the company templateTitle[1] seventh successive templateXLabel[0] of revenues in excess of templateYValue[3] templateScale templateYLabel[2] . templateXValue[0] templateTitleDate[max] is the company templateTitle[1] highest quarterly templateYLabel[0] figure to date . templateTitleSubject[0] Established in 1972 , templateTitleSubject[0] has made a name for itself as a top vendor of business and enterprise software tools .
generated: German software company Information Technology reported revenues of around 18.0 billion euros in the fourth Quarter of Q3_2019 , marking the company Technology seventh successive Quarter of revenues in excess of 17.06 billion euros .  Q3_2019 2019 is the company Technology highest quarterly Revenue figure to date .  Information Technology Established in 1972 , Information Technology has made a name for itself as a top vendor of business and enterprise software tools .

Example 128:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2016']}
title: Forecast of office rent growth in the U.S. 2015 to 2016
X_Axis['Quarter']: ['Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015']
Y_Axis['Rent', 'growth']: ['1', '0.9', '0.9', '0.9', '0.9', '0.9', '0.8']

gold: This statistic presents a forecast of office rent growth in the United States from second quarter of 2015 to fourth quarter of 2016 . It was expected that office rent would grow by one percent in the fourth quarter of 2016 in the United States . Coworking worldwide – additional information Coworking is an alternative to the traditional office space , wherein independent workers , such as freelancers and remote workers , share a working environment .
gold_template: This statistic presents a templateTitle[0] of templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[4] from second templateXLabel[0] of templateXValue[4] to fourth templateXLabel[0] of templateXValue[0] . It was expected that templateTitle[1] templateYLabel[0] would grow by templateYValue[max] templateScale in the fourth templateXLabel[0] of templateXValue[0] in the templateTitle[4] . Coworking worldwide – additional information Coworking is an alternative to the traditional templateTitle[1] space , wherein independent workers , such as freelancers and remote workers , share a working environment .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[1] templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitleDate[0] , the photo sharing app is projected to have been steadily templateNegativeTrend in templateTitleDate[0] .
generated: The statistic shows the Forecast office of U.S. from the first Quarter of 2015 to the fourth Quarter of Q4_2016 .  In the fourth Quarter of 2015 , the photo sharing app is projected to have been steadily dropping in 2015 .

Example 129:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2018']}
title: Twitter user share in selected countries 2018
X_Axis['Country']: ['United_States', 'Japan', 'Venezuela', 'United_Kingdom', 'Saudi_Arabia', 'Turkey', 'Brazil', 'Mexico', 'India', 'Spain']
Y_Axis['Share', 'of', 'Twitter', 'users']: ['18.9', '14.6', '5.8', '5.5', '4', '3.3', '3', '2.8', '2.6', '2.6']

gold: This statistic represents a ranking of the countries with the largest Twitter audiences as of July 2018 . During the measured period , the United States accounted for 18.9 percent of Twitter audiences . Japan was ranked second with a 14.6 percent share .
gold_template: This statistic represents a ranking of the templateTitle[4] with the largest templateYLabel[1] audiences as of 2018 . During the measured period , the templateXValue[0] accounted for templateYValue[max] templateScale of templateYLabel[1] audiences . templateXValue[1] was ranked second with a templateYValue[1] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] as of October templateTitleDate[0] . As of templateTitleDate[0] , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] with templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Twitter user in share selected on countries as of October 2018 .  As of 2018 , United_States was the Twitter user Country with 18.9 % of Twitter users .

Example 130:
titleEntities: {'Subject': ['India'], 'Date': ['2018']}
title: Market share of passengers carried in India 2018 by domestic airlines
X_Axis['Airline', 'Brand']: ['Indigo', 'Jet_Airways', 'Spicejet', 'Air_India', 'Go_Air', 'Air_Asia', 'Vistara', 'Jetlite', 'Alliance_Air', 'Truejet', 'Air_India_Express', 'Others']
Y_Axis['Domestic', 'market', 'share']: ['39.7', '15', '13.1', '12', '8.8', '4', '3.6', '2.2', '1', '0.4', '0.1', '0.02']

gold: India 's aviation sector had increasingly emerged as a fast-growing industry . The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail . With a visible growth trend , it was estimated that by 2034 , India would become one of the largest aviation markets in the world .
gold_template: templateXValue[3] 's aviation sector had increasingly emerged as a fast-growing industry . The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail . With a visible growth trend , it was estimated that templateTitle[6] 2034 , templateXValue[3] would become templateYValue[8] of the largest aviation markets in the world .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] templateTitle[5] as of 2018 , measured templateXLabel[0] . In that year , templateXValue[0] had a templateXValue[0] of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Domestic market of share India 2018 as of 2018 , measured Airline .  In that year , Indigo had a Indigo of 39.7 % share .

Example 131:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Share of ethnic minorities in the China 's minority autonomous regions 2018
X_Axis['Province']: ['Tibet', 'Hunan', 'Chongqing', 'Qinghai', 'Hebei', 'Sichuan', 'Gansu', 'Xinjiang', 'Guizhou', 'Yunnan', 'Hubei', 'Liaoning', 'Hainan', 'National_total', 'Guangxi', 'Guangdong', 'Ningxia', 'Jilin', 'Inner_Mongolia', 'Heilongjiang', 'Zhejiang']
Y_Axis['Share', 'of', 'ethnic', 'minorities']: ['90.05', '83.5', '74.39', '67.57', '63.75', '63.03', '62.69', '60.22', '60.14', '58.87', '56.78', '54.49', '51.69', '51.07', '44.75', '38.7', '37.39', '34.49', '22.16', '21.87', '11.81']

gold: The graph shows the share of ethnic minorities in the population of China 's minority autonomous regions by province . In 2018 , about 60.22 percent of the population in minority areas in Xinjiang belonged to ethnic minorities .
gold_template: The graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the population of templateTitleSubject[0] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] by templateXLabel[0] . In templateTitleDate[0] , about templateYValue[7] templateScale of the population in templateTitle[5] areas in templateXValue[7] belonged to templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleSubject[0] templateTitle[6] as of 2019 , templateTitle[8] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] had a templateYLabel[0] of templateYValue[max] templateScale of followed templateTitle[6] templateXValue[1] .
generated: This statistic shows the Share ethnic minorities China 's minority autonomous in China autonomous as of 2019 , 2018 Province .  In 2018 , Tibet had a Share of 90.05 % of followed autonomous Hunan .

Example 132:
titleEntities: {'Subject': ['Canada'], 'Date': ['2016']}
title: Top 5 origin countries of refugees admitted to Canada in 2016
X_Axis['Country']: ['Syria', 'Eritrea', 'Iraq', 'Congo', 'Afghanistan']
Y_Axis['Number', 'of', 'refugees', 'admitted']: ['33266', '3934', '1650', '1644', '1354']

gold: This statistic shows the top five origin counties of refugees that were admitted to Canada in 2016 . Syria topped the list in 2016 with 33,266 refugees from the country admitted into Canada .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] counties of templateYLabel[1] that were templateYLabel[2] to templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] topped the list in templateTitleDate[0] with templateYValue[max] templateYLabel[1] from the templateXLabel[0] templateYLabel[2] into templateTitleSubject[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[6] as of templateTitleDate[0] . In that year , templateXValue[0] had the highest templateYLabel[0] of any European Union .
generated: The statistic shows the Top 5 origin countries refugees admitted Canada 2016 in the Canada as of 2016 .  In that year , Syria had the highest Number of any European Union .

Example 133:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Estimated food waste from major supermarkets in the United Kingdom ( UK ) 2016
X_Axis['Month']: ['Tesco', "Sainsbury's", 'Asda', 'Aldi', 'Waitrose', 'Co-op', 'Marks_and_Spencer', 'Iceland']
Y_Axis['Volume', 'in', 'tonnes']: ['59400', '35832', '32020', '13377', '12529', '12411', '10152', '2080']

gold: This statistic shows estimates of wasted food from major supermarkets in the United Kingdom ( UK ) in 2016 . In this year Tesco was found to generate the highest volume of food waste at 59.4 thousand tonnes . This was followed by Sainsbury 's with a waste generation of approximately 35.8 thousand tonnes and Asda with 32 thousand tonnes of food waste generated .
gold_template: This statistic shows estimates of wasted templateTitle[1] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . In this year templateXValue[0] was found to generate the highest templateYLabel[0] of templateTitle[1] templateTitle[2] at templateYValue[max] thousand templateYLabel[1] . This was followed by Sainsbury 's with a templateTitle[2] generation of approximately templateYValue[1] thousand templateYLabel[1] and templateXValue[2] with templateYValue[2] thousand templateYLabel[1] of templateTitle[1] templateTitle[2] generated .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[0] , templateYValue[max] templateScale of the templateXValue[2] had a total of templateYValue[6] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Estimated food waste from in the United Kingdom ( ) from 2016 to .  In 2016 , 59400 % of the Asda had a total of 10152 tonnes .

Example 134:
titleEntities: {'Subject': ['Michelin'], 'Date': ['2018']}
title: Michelin - worldwide number of employees by region 2018
X_Axis['Region']: ['Europe', 'North_America', 'Asia_(excl._India)', 'South_America', 'Africa_India_Middle-East']
Y_Axis['Number', 'of', 'employees']: ['70599', '21541', '15259', '8166', '1848']

gold: The statistic shows the worldwide number of Michelin 's employees in 2018 , by region . Michelin had 21,541 employees in North America in 2018 . In that same year , some 17.5 percent of their employees worldwide were women .
gold_template: The statistic shows the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] 's templateYLabel[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . templateTitleSubject[0] had templateYValue[1] templateYLabel[1] in templateXValue[1] in templateTitleDate[0] . In that same year , some 17.5 templateScale of their templateYLabel[1] templateTitle[1] were women .

generated_template: This statistic shows the templateTitle[1] of templateYLabel[1] at templateTitleSubject[0] agricultural company in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateXValue[2] templateXValue[1] , the company employed approximately templateYValue[2] templateScale of the total people working for templateTitleSubject[0] .
generated: This statistic shows the worldwide of employees at Michelin agricultural company in 2018 , employees Region .  In Asia_(excl._India) North_America , the company employed approximately 15259 % of the total people working for Michelin .

Example 135:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2017']}
title: U.S. consumer business cyber security budget share 2017
X_Axis['Response']: ['3_to_4', '4_to_5', '5_to_6', '6_to_8', '8_to_10', 'More_than_10']
Y_Axis['Share', 'of', 'respondents']: ['14', '20', '21', '21', '10', '14']

gold: This statistic illustrates the share of cyber security budget as percentage of annual IT budget of consumer businesses in the United States . During the February 2017 survey period , 14 percent of C-level respondents stated that cyber security accounted for more than 10 percent of their annual IT budget .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] as templateScale of annual IT templateTitle[5] of templateTitle[1] businesses in the templateTitle[0] . During the 2017 survey period , templateYValue[last] templateScale of C-level templateYLabel[1] stated that templateTitle[3] templateTitle[4] accounted for templateXValue[last] 10 templateScale of their annual IT templateTitle[5] .

generated_template: As of early templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] that year , followed by templateXValue[1] and templateXValue[2] . Of templateYLabel[1] stated that they templateXValue[6] for the templateXValue[3] templateXValue[1] followed by templateYValue[1] templateScale of global headphone .
generated: As of early 2017 , 3_to_4 and 4_to_5 were the most business cyber security budget share 2017 that year , followed by 4_to_5 and 5_to_6 .  Of respondents stated that they More_than_10 for the 6_to_8 4_to_5 followed by 20 % of global headphone .

Example 136:
titleEntities: {'Subject': ['GDP'], 'Date': ['2020']}
title: Forecast of the gross domestic product ( GDP ) growth in the euro countries 2020
X_Axis['Country']: ['Malta', 'Slovak_Republic', 'Ireland', 'Cyprus', 'Latvia', 'Estonia', 'Slovenia', 'Luxembourg', 'Lithuania', 'Greece', 'Spain', 'Finland', 'Austria', 'Netherlands', 'Portugal', 'Germany', 'France', 'Belgium', 'Italy']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['4.44', '3.46', '3.45', '3.34', '3.15', '2.9', '2.83', '2.75', '2.63', '2.16', '1.88', '1.73', '1.7', '1.69', '1.5', '1.44', '1.41', '1.39', '0.91']

gold: This statistic shows a forecast of the gross domestic product ( GDP ) growth in the euro countries in 2020 . In 2020 , the gross domestic product in Germany is forecasted to grow by 1.44 percent over the previous year .
gold_template: This statistic shows a templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) templateYLabel[1] in the templateTitle[6] templateTitle[7] in templateTitleDate[0] . In templateTitleDate[0] , the templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[15] is forecasted to grow by templateYValue[15] templateScale over the templateYLabel[3] templateYLabel[4] .

generated_template: This statistic displays the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[0] ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[3] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of approximately templateYValue[3] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] around the world templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[0] ) is an indicator of the monetary value of all goods and services produced by a nation in a specific time period .
generated: This statistic displays the 20 Forecast gross the domestic growth of the GDP growth euro ( GDP ) in 2020 .  In 2020 , Cyprus ranked 4th gross an estimated GDP growth of approximately 3.34 % compared to the previous year .  GDP around the world GDP growth euro ( GDP ) is an indicator of the monetary value of all goods and services produced by a nation in a specific time period .

Example 137:
titleEntities: {'Subject': ['Netherlands', 'Buzz'], 'Date': ['2018']}
title: Leading brands in the Netherlands 2018 , ranked by Buzz score
X_Axis['Platform']: ['Samsung', 'Albert_Heijn', 'Philips', 'Google', 'Lidl', 'Jumbo', 'YouTube', 'Sony', 'Wikipedia', 'Bose']
Y_Axis['Buzz', 'score']: ['47.7', '46.8', '38.6', '37.9', '34.8', '33.8', '33.5', '28.9', '25.4', '21.9']

gold: In 2018 , Samsung was the brand with the highest Buzz score in the Netherlands , followed by two Dutch brands : food retailer Albert Heijn and Philips . A brand 's Buzz score indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .
gold_template: In templateTitleDate[0] , templateXValue[0] was the brand with the highest templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] , followed templateTitle[5] two Dutch templateTitle[1] : food retailer templateXValue[1] and templateXValue[2] . A brand 's templateYLabel[0] templateYLabel[1] indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in the templateTitle[5] as of 2019 , ranked templateTitle[6] . During the measured period , it was found that templateYValue[min] templateScale of the templateXValue[1] had a second most popular templateTitle[1] .
generated: This statistic gives information on the Leading brands Netherlands networking ranked in the by as of 2019 , ranked Buzz .  During the measured period , it was found that 21.9 % of the Albert_Heijn had a second most popular brands .

Example 138:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005']}
title: Total retail sales of U.S. shopping malls 2005 , by size
X_Axis['Gross', 'leasable', 'area', 'in', 'square', 'feet']: ['Less_than_100001', '100001_to_200000', '200001_to_400000', '400001_to_800000', '800001_to_1000000', 'More_than_one_million']
Y_Axis['Total', 'retail', 'sales', 'in', 'billion', 'U.S.', 'dollars']: ['443.8', '388.6', '234.2', '197.6', '97.3', '168.9']

gold: This statistic shows of the total retail sales of all retail shopping malls in the United States , sorted by mall size in square feet of gross leasable area . In 2005 , shopping malls sized between 200,001 and 400,000 square feet made a total of 234.2 billion U.S. dollars of retail sales .
gold_template: This statistic shows of the templateYLabel[0] templateYLabel[1] templateYLabel[2] of all templateYLabel[1] templateTitle[4] templateTitle[5] in the templateTitle[3] , sorted templateTitle[7] mall templateTitle[8] in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In templateTitleDate[0] , templateTitle[4] templateTitle[5] sized between templateXValue[2] and templateXValue[2] templateXLabel[3] templateXLabel[4] made a templateYLabel[0] of templateYValue[2] templateScale templateYLabel[4] templateYLabel[5] of templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[2] as of 2015 , sorted templateTitle[5] templateTitle[6] . During that period , templateYValue[max] templateScale of the templateYLabel[1] templateYLabel[2] in templateXValue[0] were templateXValue[1] via templateXValue[0] .
generated: This statistic shows the Total retail sales U.S. in the sales as of 2015 , sorted malls 2005 .  During that period , 443.8 billion of the retail sales in Less_than_100001 were 100001_to_200000 via Less_than_100001 .

Example 139:
titleEntities: {'Subject': ['YouTube YouTube Red U.S.'], 'Date': ['2017']}
title: Consumers with a YouTube or YouTube Red subscription in the U.S. 2017 , by age group
X_Axis['Response']: ['Total', 'Millennials', 'Gen_X', 'Boomers', 'Retirees']
Y_Axis['Share', 'of', 'respondents']: ['16', '27', '17', '6', '3']

gold: This statistic provides information on the share of consumers with an active YouTube or YouTube Red subscription in the United States as of January 2017 , sorted by age . According to the source , 27 percent of Millennials who subscribe to online video or music subscriptions had a YouTube or YouTube Red subscription as of January 2017 .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitleSubject[0] or templateTitleSubject[0] subscription in the templateTitle[6] as of 2017 , sorted templateTitle[8] templateTitle[9] . According to the source , templateYValue[max] templateScale of templateXValue[1] who subscribe to online video or music subscriptions had a templateTitleSubject[0] or templateTitleSubject[0] subscription as of 2017 .

generated_template: At the beginning of templateTitleDate[0] , templateYValue[0] templateScale of templateTitle[0] templateTitle[1] an online video or music templateTitle[3] confirmed that they had an active templateTitleSubject[0] templateTitle[3] at the time of survey . templateXValue[1] and Gen-Xers were more likely to have a templateTitleSubject[0] templateTitle[3] than their older peers , which comes as no surprise given that templateTitleSubject[0] is considered the most indispensable TV and video source among templateTitle[0] aged between 16 to 34 years old . One of the most appealing aspects of templateTitleSubject[0] for viewers is the availability of original content , which has fast become one of the keys to the company 's success .
generated: At the beginning of 2017 , 16 % of Consumers YouTube an online video or music Red confirmed that they had an active YouTube YouTube Red U.S. Red at the time of survey .  Millennials and Gen-Xers were more likely to have a YouTube YouTube Red U.S. Red than their older peers , which comes as no surprise given that YouTube YouTube Red U.S. is considered the most indispensable TV and video source among Consumers aged between 16 to 34 years old .  One of the most appealing aspects of YouTube YouTube Red U.S. for viewers is the availability of original content , which has fast become one of the keys to the company 's success .

Example 140:
titleEntities: {'Subject': ['Nordic'], 'Date': ['2017']}
title: Surface area of the Nordic countries 2017
X_Axis['Country']: ['Sweden', 'Greenland', 'Norway', 'Finland', 'Iceland', 'Denmark', 'Faroe_Islands']
Y_Axis['Surface', 'area', 'in', 'square', 'kilometers']: ['447420', '410450', '385178', '338420', '103000', '42922', '1396']

gold: This statistic shows the surface area of the Nordic countries in 2017 . The largest of all Nordic countries is Sweden , with a surface of roughly 447 thousand square kilometers . Its neighboring country Norway has a size of approximately 385 thousand square kilometers , which includes the arctic islands of Svalbard and Jan Mayen .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] . The largest of all templateTitleSubject[0] templateTitle[3] is templateXValue[0] , with a templateYLabel[0] of roughly templateYValue[max] thousand templateYLabel[2] templateYLabel[3] . Its neighboring templateXLabel[0] templateXValue[2] has a size of approximately templateYValue[2] thousand templateYLabel[2] templateYLabel[3] , which includes the arctic templateXValue[last] of Svalbard and Jan Mayen .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] in templateTitle[2] templateTitle[3] as of 2020 , sorted by templateXLabel[0] . According to the source , templateYValue[max] templateScale of templateYLabel[5] templateTitle[2] templateTitle[3] app and had the highest in the templateTitle[4] templateTitle[5] templateTitle[6] .
generated: This statistic shows the Surface of Nordic area in Nordic countries as of 2020 , sorted by Country .  According to the source , 447420 million of kilometers Nordic countries app and had the highest in the 2017 .

Example 141:
titleEntities: {'Subject': ['IMF'], 'Date': ['2011']}
title: IMF - biggest debtor nations 2011
X_Axis['Country']: ['Romania', 'Ukraine', 'Greece', 'Hungary', 'Pakistan', 'Ireland', 'Turkey', 'Belarus']
Y_Axis['Debt', 'in', 'billion', 'euros']: ['11.8', '10.3', '10.2', '8.5', '6.3', '5.6', '4.1', '2.5']

gold: The statistic shows IMF 's biggest debtor states in May 2011 . Belarus reported a debt of 2.5 billion euros .
gold_template: The statistic shows templateTitleSubject[0] 's templateTitle[1] templateTitle[2] states in 2011 . templateXValue[last] reported a templateYLabel[0] of templateYValue[min] templateScale templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] in templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of templateTitleDate[0] . At that time , there were templateYValue[max] templateScale in templateXValue[0] .
generated: This statistic shows the Debt of the billion euros in biggest debtor nations 2011 as of 2011 .  At that time , there were 11.8 billion in Romania .

Example 142:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Consumers with a newspaper or magazine subscriptions in the U.S. 2017 , by age group
X_Axis['Response']: ['Total', 'Millennials', 'Gen_X', 'Boomers', 'Retirees']
Y_Axis['Share', 'of', 'respondents']: ['41', '33', '35', '45', '54']

gold: This statistic provides information on the share of consumers with an active newspaper or magazine subscription in the United States as of January 2017 , sorted by age . According to the source , 54 percent of Retirees who subscribe to service subscriptions had a newspaper or magazine subscription as of January 2017 .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitle[2] or templateTitle[3] subscription in the templateTitle[5] as of 2017 , sorted templateTitle[7] templateTitle[8] . According to the source , templateYValue[max] templateScale of templateXValue[last] who subscribe to service templateTitle[4] had a templateTitle[2] or templateTitle[3] subscription as of 2017 .

generated_template: At the beginning of templateTitleDate[0] , templateYValue[0] templateScale of templateTitle[0] templateTitle[1] an online video or music templateTitle[3] confirmed that they had an active templateTitleSubject[0] templateTitle[3] at the time of survey . templateXValue[1] and Gen-Xers were more likely to have a templateTitleSubject[0] templateTitle[3] than their older peers , which comes as no surprise given that templateTitleSubject[0] is considered the most indispensable TV and video source among templateTitle[0] aged between 16 to 34 years old . One of the most appealing aspects of templateTitleSubject[0] for viewers is the availability of original content , which has fast become one of the keys to the company 's success .
generated: At the beginning of 2017 , 41 % of Consumers newspaper an online video or music subscriptions confirmed that they had an active U.S. subscriptions at the time of survey .  Millennials and Gen-Xers were more likely to have a U.S. subscriptions than their older peers , which comes as no surprise given that U.S. is considered the most indispensable TV and video source among Consumers aged between 16 to 34 years old .  One of the most appealing aspects of U.S. for viewers is the availability of original content , which has fast become one of the keys to the company 's success .

Example 143:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2019']}
title: Most viewed YouTube videos of all time 2019
X_Axis['Month']: ['Luis_Fonsi_-_Despacito_ft._Daddy_Yankee', 'Ed_Sheeran_-_Shape_of_You', 'Wiz_Khalifa_-_See_You_Again_ft._Charlie_Puth_[Official_Video]_Furious_7_Soundtrack', 'Masha_and_the_Bear:_Recipe_for_Disaster', "Pinkfong_Kids'_Songs_&_Stories_-_Baby_Shark_Dance", 'Mark_Ronson_ft._Bruno_Mars_-_Uptown_Funk', 'PSY_-_GANGNAM_STYLE', 'Justin_Bieber_-_Sorry', 'Maroon_5_-_Sugar', 'Katy_Perry_-_Roar']
Y_Axis['Number', 'of', 'views', 'in', 'billions']: ['6.55', '4.51', '4.31', '4.18', '4.06', '3.73', '3.47', '3.22', '3.08', '2.97']

gold: On January 12 , 2017 , Puerto Rican singer Luis Fonsi released his Spanish-language music video `` Despacito '' featuring Daddy Yankee , and the rest is history . In August of the same year , the video became the most-viewed YouTube video of all time and as of December 2019 , the video still holds the top spot with over 6.55 billion lifetime views on the video platform . Music videos on YouTube `` Descpacito '' might be the current record-holder in terms of total views , but Korean artist Psy 's `` Gangnam Style '' video remained on the top spot for longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .
gold_template: On 12 , 2017 , Puerto Rican singer templateXValue[0] released his Spanish-language music video `` templateXValue[0] '' featuring templateXValue[0] , and the rest is history . In of the same year , the video became the most-viewed templateTitleSubject[0] video of templateTitle[4] templateTitle[5] and as of 2019 , the video still holds the top spot with over templateYValue[max] templateScale lifetime templateYLabel[1] on the video platform . Music templateTitle[3] on templateTitleSubject[0] `` Descpacito '' might be the current record-holder in terms of total templateYLabel[1] , but Korean artist templateXValue[6] 's `` templateXValue[6] '' video remained on the top spot templateXValue[3] longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of 2018 . As of that templateXLabel[0] , the templateTitle[0] templateTitle[1] a total of templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Most viewed YouTube videos all YouTube 2019 as of 2018 .  As of that Month , the Most viewed a total of 6.55 billions views .

Example 144:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019', '2019']}
title: Canada : Gross Domestic Product ( GDP ) by industry December 2019
X_Axis['Industry']: ['Real_estate_and_rental_and_leasing', 'Manufacturing', 'Mining_quarrying_and_oil_and_gas_extraction', 'Construction', 'Health_care_and_social_assistance', 'Public_administration', 'Finance_and_insurance', 'Professional_scientific_and_technical_services', 'Educational_services', 'Wholesale_trade', 'Retail_trade', 'Transportation_and_warehousing', 'Information_and_cultural_industries', 'Administrative_and_support_waste_management_and_remediation_services', 'Accommodation_and_food_services', 'Utilities', 'Agriculture_forestry_fishing_and_hunting', 'Other_services_(except_public_administration)', 'Arts_entertainment_and_recreation', 'Management_of_companies_and_enterprises']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['254294', '199234', '145909', '142530', '142028', '134370', '133599', '120820', '104771', '103082', '102619', '89599', '64323', '52649', '45160', '43668', '40058', '38127', '15699', '9303']

gold: This statistic shows the Gross Domestic Product ( GDP ) of Canada in December 2019 , distinguished by major industry . In December 2019 , the construction industry of Canada contributed about 142.5 billion Canadian dollars to the total Canadian GDP .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) of templateTitleSubject[0] in templateTitle[7] templateTitleDate[0] , distinguished templateTitle[5] major templateXLabel[0] . In templateTitle[7] templateTitleDate[0] , the templateXValue[3] templateXLabel[0] of templateTitleSubject[0] contributed about templateYValue[3] templateScale templateYLabel[4] templateYLabel[5] to the total templateYLabel[4] templateYLabel[0] .

generated_template: This graph shows the average templateYLabel[0] of templateTitleSubject[0] templateYLabel[2] fourth as of templateTitleSubject[0] templateTitle[6] as of 2019 , templateTitle[8] templateXLabel[0] . In templateTitleDate[0] , there were a templateYLabel[0] of templateYValue[4] templateScale in templateTitle[6] .
generated: This graph shows the average GDP of Canada chained fourth as of Canada industry as of 2019 , Industry .  In 2019 , there were a GDP of 142028 million in industry .

Example 145:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Population in China in 2018 , by region
X_Axis['Province']: ['Guangdong', 'Shandong', 'Henan', 'Sichuan', 'Jiangsu', 'Hebei', 'Hunan', 'Anhui', 'Hubei', 'Zhejiang', 'Guangxi', 'Yunnan', 'Jiangxi', 'Liaoning', 'Fujian', 'Shaanxi', 'Heilongjiang', 'Shanxi', 'Guizhou', 'Chongqing', 'Jilin', 'Gansu', 'Inner_Mongolia', 'Xinjiang', 'Shanghai', 'Beijing', 'Tianjin', 'Hainan', 'Ningxia', 'Qinghai', 'Tibet']
Y_Axis['Population', 'in', 'million', 'inhabitants']: ['113.46', '100.47', '96.05', '83.41', '80.51', '75.56', '68.99', '63.24', '59.17', '57.37', '49.26', '48.3', '46.48', '43.59', '39.41', '38.64', '37.73', '37.18', '36.0', '31.02', '27.04', '26.37', '25.34', '24.87', '24.24', '21.54', '15.6', '9.34', '6.88', '6.03', '3.44']

gold: This statistic shows the regional distribution of the population in China in 2018 . That year , approximately 75.6 million people lived in Hebei province in China . Regional differences in China China is the world 's most populous country , with an exceptional economic growth momentum .
gold_template: This statistic shows the regional distribution of the templateYLabel[0] in templateTitleSubject[0] in templateTitleDate[0] . That year , approximately templateYValue[5] templateScale people lived in templateXValue[5] templateXLabel[0] in templateTitleSubject[0] . Regional differences in templateTitleSubject[0] is the world 's most populous country , with an exceptional economic growth momentum .

generated_template: This graph shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] ( templateTitleSubject[0] ) templateTitle[6] as of 2020 . As of the fourth templateXLabel[0] of 2019 , templateYValue[max] templateScale of templateTitleSubject[0] stated that they went to templateXValue[0] .
generated: This graph shows the Population China 2018 by region ( China ) region as of 2020 .  As of the fourth Province of 2019 , 113.46 million of China stated that they went to Guangdong .

Example 146:
titleEntities: {'Subject': ['Global'], 'Date': ['2016']}
title: Global tobacco production value 2016 , by country
X_Axis['Country']: ['China_mainland', 'United_States_of_America', 'Brazil', 'India', 'Japan', 'Indonesia', 'Turkey', 'Republic_of_Korea', 'Italy', 'Mozambique']
Y_Axis['Production', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3434.02', '1108.88', '873.93', '616.44', '304.35', '264.49', '232.77', '212.25', '177.31', '146.08']

gold: This statistic shows the leading tobacco producing countries worldwide in 2016 , based on gross production value . In that year , China 's produced tobacco was worth approximately 3.43 billion U.S. dollars .
gold_template: This statistic shows the leading templateTitle[1] producing countries worldwide in templateTitleDate[0] , based on gross templateYLabel[0] templateYLabel[1] . In that year , templateXValue[0] 's produced templateTitle[1] was worth approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitleDate[0] . During this year , it was found that templateYValue[max] templateScale of reporting templateTitle[3] .
generated: This statistic shows the Production of the Global tobacco production value in the 2016 as of 2016 .  During this year , it was found that 3434.02 million of reporting value .

Example 147:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2018']}
title: Music industry employment in the United Kingdom ( UK ) 2018 , by sector
X_Axis['Industry']: ['Music_creators', 'Music_retail', 'Recorded_music', 'Music_representatives', 'Music_publishing', 'Live_music']
Y_Axis['Number', 'of', 'workers']: ['139352', '11688', '5379', '2624', '1363', '30529']

gold: This statistic shows employment in the UK music industry in 2018 , by thematic grouping . In 2018 , it was estimated that there were over 30 thousand workers in live music . In the same year , there were 139 thousand people working as music creators .
gold_template: This statistic shows templateTitle[2] in the templateTitleSubject[1] templateXValue[0] templateXLabel[0] in templateTitleDate[0] , templateTitle[7] thematic grouping . In templateTitleDate[0] , it was estimated that there were over 30 thousand templateYLabel[1] in templateXValue[last] templateXValue[0] . In the same year , there were templateYValue[max] thousand people working as templateXValue[0] .

generated_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] fourth templateTitle[6] as of 2018 . As of the measured period , templateYValue[max] templateScale of the website and templateXValue[0] sugar templateXLabel[0] .
generated: This graph shows the Music industry of United Kingdom fourth 2018 as of 2018 .  As of the measured period , 139352 % of the website and Music_creators sugar Industry .

Example 148:
titleEntities: {'Subject': ['Play Q4'], 'Date': ['2019']}
title: Google Play : number of available apps as of Q4 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15"]
Y_Axis['Number', 'of', 'available', 'apps']: ['2570520', '2469894', '2327628', '2134302', '1977776', '2108450', '2977833', '3849865', '3662276', '3364880', '3172310', '2956763', '2811106', '2781508', '2539526', '2294798', '2012040', '1811532', '1670113', '1605359']

gold: This statistic gives information on the number of available apps in the Google Play app store . As of the fourth quarter of 2019 , over 2.57 million mobile apps were available , representing a 4.07 percent increase compared to the previous quarter .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitleSubject[0] app store . As of the fourth templateXLabel[0] of templateTitleDate[0] , over templateYValue[0] templateScale mobile templateYLabel[2] were templateYLabel[1] , representing a 4.07 templateScale templatePositiveTrend compared to the previous templateXLabel[0] .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateTitle[4] templateYLabel[2] in the templateTitle[0] templateTitleSubject[0] app store . As of the third templateXLabel[0] of templateTitleDate[0] , approximately templateYValue[last] mobile templateTitle[4] templateYLabel[2] were templateYLabel[1] , representing a 3.88 templateScale templatePositiveTrend compared to the previous templateXLabel[0] .
generated: This statistic gives information on the Number of available apps in the Google Play Q4 app store .  As of the third Quarter of 2019 , approximately 1605359 mobile apps were available , representing a 3.88 % increase compared to the previous Quarter .

Example 149:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Canada : number of individual firearms licenses held , by province or territory 2018
X_Axis['Month']: ['Ontario', 'Quebec', 'Alberta', 'British_Columbia', 'Saskatchewan', 'Manitoba', 'Newfoundland_and_Labrador', 'Nova_Scotia', 'New_Brunswick', 'Yukon', 'Prince_Edward_Island', 'Northwest_Territories', 'Nunavut']
Y_Axis['Number', 'of', 'firearms', 'licenses']: ['616489', '500058', '316791', '301775', '110573', '91107', '76802', '76180', '70111', '7711', '6363', '5955', '3912']

gold: This graph shows the number of individual firearms licenses held in Canada in 2018 , by province or territory . In Ontario , 616,489 firearms licenses were held in 2018 .
gold_template: This graph shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[5] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateTitle[7] or templateTitle[8] . In templateXValue[0] , templateYValue[max] templateYLabel[1] templateYLabel[2] were templateTitle[5] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitle[3] as of templateTitleSubject[0] templateTitle[6] templateTitle[6] . As of templateTitleDate[0] , there were a total of templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of firearms in the firearms as of Canada by .  As of 2018 , there were a total of 616489 firearms licenses .

Example 150:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. user reasons for using online dating sites or apps 2017
X_Axis['Response']: ['To_meet_people_who_share_my_interests_or_hobbies', 'To_meet_people_who_share_my_beliefs_or_values', 'To_find_someone_for_a_long-term_relationship_or_marriage', 'I_have_a_schedule_that_makes_it_hard_to_meet_interesting_people_in_other_ways', 'To_meet_people_who_just_want_to_have_fun_without_being_in_a_serious_relationship', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['61', '44', '42', '21', '26', '7']

gold: This statistic presents the reasons why users in the United States use online dating sites or apps . During the April 2017 survey , 61 percent of responding current or former dating website or app users said they used dating websites and apps to meet people who share their interests or hobbies .
gold_template: This statistic presents the templateTitle[2] why users in the templateTitle[0] use templateTitle[5] templateTitle[6] templateTitle[7] or templateTitle[8] . During the 2017 survey , templateYValue[max] templateScale of responding current or former templateTitle[6] website or app users said they used templateTitle[6] websites and templateTitle[8] to templateXValue[0] who templateXValue[0] their templateXValue[0] or templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2015 . During a survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[0] and templateXValue[0] .
generated: This statistic shows the U.S. user in reasons for on using online in the dating as of 2015 .  During a survey , 61 % of the respondents stated that they used To_meet_people_who_share_my_interests_or_hobbies and .

Example 151:
titleEntities: {'Subject': ['Share'], 'Date': ['2012']}
title: Share of global seeds market value by country 2012
X_Axis['Country']: ['United_States', 'China', 'France', 'Brazil', 'Canada', 'India', 'Japan', 'Germany', 'Argentina', 'Italy']
Y_Axis['Market', 'value', 'share']: ['26.71', '22.15', '6.23', '5.84', '4.72', '4.45', '3.01', '2.6', '2.2', '1.71']

gold: This graph depicts the shares of the global seeds market value in 2012 , by country . The United States and China both held more than 20 percent of the market value worldwide in that year .
gold_template: This graph depicts the shares of the templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . The templateXValue[0] and templateXValue[1] both held more than 20 templateScale of the templateYLabel[0] templateYLabel[1] worldwide in that year .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] . In that year , the templateXValue[0] was the templateTitle[0] templateTitle[2] templateXLabel[0] .
generated: This statistic shows the Market of the global seeds market in 2012 .  In that year , the United_States was the Share seeds Country .

Example 152:
titleEntities: {'Subject': ['Bing'], 'Date': ['2017']}
title: Bing global search market share 2017 , by country
X_Axis['Country']: ['Worldwide', 'United_States', 'Brazil', 'Canada', 'Latin_America', 'Asia_Pacific', 'Australia', 'Hong_Kong', 'India', 'Indonesia', 'Malaysia', 'New_Zealand', 'Philippines', 'Singapore', 'Taiwan', 'Vietnam', 'Europe', 'Austria', 'Belgium', 'Denmark', 'Finland', 'France', 'Germany', 'Ireland', 'Italy', 'Netherlands', 'Norway', 'Spain', 'Sweden', 'Switzerland', 'United_Kingdom']
Y_Axis['Share', 'of', 'search', 'traffic']: ['9', '33', '3', '17', '5', '3', '12', '19', '7', '7', '8', '6', '5', '8', '24', '8', '9', '12', '12', '9', '7', '19', '12', '8', '9', '9', '17', '9', '12', '12', '26']

gold: This statistic shows the worldwide search market share of Bing as of August 2017 in leading online markets . During the measured period , Bing accounted for 17 percent of search traffic in Canada . The Microsoft-owned platform accounted for nine percent of search traffic worldwide .
gold_template: This statistic shows the templateXValue[0] templateYLabel[1] templateTitle[3] templateYLabel[0] of templateTitleSubject[0] as of 2017 in leading online markets . During the measured period , templateTitleSubject[0] accounted for templateYValue[3] templateScale of templateYLabel[1] templateYLabel[2] in templateXValue[3] . The Microsoft-owned platform accounted for templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , templateTitle[0] templateTitle[1] templateTitle[2] had a total of templateYValue[2] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Share of Bing global search traffic in 2017 , share Country .  In that year , Bing global search had a total of 3 search traffic .

Example 153:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Revenue of the fastest-growing private security companies in the U.S. 2018
X_Axis['Company']: ['Netizen', 'Cisoshare', 'Exabeam', 'KnowBe4', 'Transcend_Security_Solutions', 'Perimeter_Security_Partners', 'Tomahawk_Strategic_Solutions', 'Kisi_Security', 'Aysco_Technology_Integration', 'Kenna_Security', 'Point3_Security', 'BOS_Security', 'Satelles', 'Skynet_Integrations', 'Home_View_Technologies']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['6.3', '3.9', '38.8', '72.3', '8.3', '15.3', '2.8', '2.8', '20.4', '13.2', '5.8', '7.3', '5.0', '2.0', '16.5']

gold: This statistic shows the revenue of the fastest-growing private security companies in the United States in 2018 . The fastest growing security company in the United States was Netizen , which generated revenue of 6.3 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateXValue[4] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . The fastest templatePositiveTrend templateXValue[4] templateXLabel[0] in the templateTitle[5] was templateXValue[0] , which generated templateYLabel[0] of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateYLabel[0] in templateTitleDate[0] . In that year , templateXValue[2] , had templateYLabel[0] of templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .
generated: This statistic shows the Revenue of the U.S. Revenue in 2018 .  In that year , Exabeam had Revenue of 38.8 million U.S. dollars in 2018 .

Example 154:
titleEntities: {'Subject': ['LINE'], 'Date': ['2014', '2016']}
title: LINE : number of monthly active users 2014 to 2016
X_Axis['Quarter']: ["Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14"]
Y_Axis['Number', 'of', 'monthly', 'users', 'in', 'millions']: ['217.0', '220.0', '220.0', '218.4', '215.0', '212.0', '211.0', '205.0', '190.0', '179.0', '170.0']

gold: This statistic shows a timeline with the amount of monthly active LINE users worldwide as of the fourth quarter of 2016 . As of that period , the mobile messaging app announced more than 217 million monthly active users . In October 2014 , LINE had also reported 560 million registered users worldwide .
gold_template: This statistic shows a timeline with the amount of templateYLabel[1] templateTitle[3] templateTitleSubject[0] templateYLabel[2] worldwide as of the fourth templateXLabel[0] of templateTitleDate[max] . As of that period , the mobile messaging app announced more than templateYValue[0] templateScale templateYLabel[1] templateTitle[3] templateYLabel[2] . In October templateTitleDate[min] , templateTitleSubject[0] had also reported 560 templateScale registered templateYLabel[2] worldwide .

generated_template: Messaging app templateTitle[0] is templateTitleSubject[0] 's most popular online communication tool , peaking at approximately templateYValue[max] templateScale templateYLabel[1] actives templateYLabel[2] in templateTitleSubject[0] during the fourth templateXLabel[0] of templateTitleDate[max] . templateTitle[0] 's main target group are young adults aged 15 to 34 years , representing more than 50 templateScale of the company 's Japanese user base in 2018 . The rise of templateTitle[0] in templateTitleSubject[0] The success story of messaging service templateTitle[0] , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East templateTitleSubject[0] Earthquake .
generated: Messaging app LINE is 's most popular online communication tool , peaking at approximately 220.0 millions monthly actives users in LINE during the fourth Quarter of 2016 .  LINE 's main target group are young adults aged 15 to 34 years , representing more than 50 millions of the company 's Japanese user base in 2018 .  The rise of LINE in The success story of messaging service LINE , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East LINE Earthquake .

Example 155:
titleEntities: {'Subject': ['El Pais', 'Spain'], 'Date': ['1999', '2018']}
title: El Pais ( Spain ) : circulation 1999 to 2018
X_Axis['Month']: ['July_2017-June_2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Circulation']: ['163759', '194005', '221390', '259775', '292227', '324814', '365118', '370080', '391816', '431034', '435083', '432204', '453602', '469183', '440226', '435299', '433617', '436302', '435433']

gold: This timeline shows the average circulation of the Spanish daily El Pais from 1999 to 2018 . In the period between July 2017 and June 2018 , the Spanish newspaper sold on average 163.8 thousand copies daily .
gold_template: This timeline shows the average templateYLabel[0] of the Spanish daily templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the period between templateXValue[0] 2017 and 2018 , the Spanish newspaper sold on average templateYValue[min] thousand copies daily .

generated_template: This statistic shows the leading templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the country in templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] had a company 's templateYLabel[0] of templateYValue[max] templateScale of templateYLabel[1] .
generated: This statistic shows the leading El Pais Pais Spain circulation 1999 2018 in the country in 1999 .  In 1999 , El Pais had a company 's Circulation of 469183 % of Circulation .

Example 156:
titleEntities: {'Subject': ['Great Britain', 'UK'], 'Date': ['2016']}
title: Best cars to own based on ride quality in Great Britain ( UK ) 2016
X_Axis['Car', 'Model']: ['Tesla_Model_S_MkI', 'Land_Rover_Discovery_MkIV', 'Renault_Kadjar_MkI', 'Lexus_IS_MkIII', 'Renault_ZOE_MkI', 'Lexus_GS_MkIV', 'Jaguar_XF_MkI', 'Citroen_C5_MkII', 'Skoda_Citigo_MkI', 'Jeep_Grand_Cherokee_MKIV', 'Toyota_Land_Cruiser_MkVII', 'Lexus_RX_MkII', 'MG_MG6_MkI', 'Lexus_RX_MkIII', 'Subaru_Forester_MkIV']
Y_Axis['Percentage']: ['97.19', '94.63', '93.72', '93.3', '92.99', '92.8', '92.68', '92.62', '92.41', '92.38', '92.26', '92.07', '91.7', '91.35', '91.32']

gold: This statistic shows the leading 15 car models according to the Auto Express Driver Power 2016 survey responses based on ride quality . The survey was carried out by the British automotive magazine online between 2015 and 2016 . Lexus had four models in the top 15 based on ride quality .
gold_template: This statistic shows the leading 15 templateXLabel[0] models according to the Auto Express Driver Power templateTitleDate[0] survey responses templateTitle[3] on templateTitle[4] templateTitle[5] . The survey was carried out by the British automotive magazine online between 2015 and templateTitleDate[0] . templateXValue[3] had four models in the top 15 templateTitle[3] on templateTitle[4] templateTitle[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[6] as of 2020 . As of the measured period , it was found that templateYValue[max] templateScale any templateXValue[0] .
generated: This statistic shows the Best cars of the own based ride in Great as of 2020 .  As of the measured period , it was found that 97.19 percentage any Tesla_Model_S_MkI .

Example 157:
titleEntities: {'Subject': ['North America'], 'Date': ['2018']}
title: Leading cinema circuits in North America in 2018 , by number of screens
X_Axis['Month']: ['AMC_Theatres', 'Regal_Entertainment_Group', 'Cinemark_USA_Inc.', 'Cineplex_Entertainment_LP', 'Marcus_Theatres_Corp.', 'Harkins_Theatres', 'Southern_Theatres_LLC', 'B_&_B_Theatres', 'National_Amusements_Inc.', 'Malco_Theatres_Inc.']
Y_Axis['Number', 'of', 'screens']: ['8218', '7350', '4544', '1683', '895', '515', '499', '400', '392', '353']

gold: The graph shows leading cinema circuits in North America as of July 2018 , ranked by number of screens . AMC Theatres ranked first with 8,218 screens . Total attendance at AMC Theatres worldwide reached record levels in 2017 , with over 346 million attendees .
gold_template: The graph shows templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] as of 2018 , ranked templateTitle[6] templateYLabel[0] of templateYLabel[1] . templateXValue[0] ranked first with templateYValue[max] templateYLabel[1] . Total attendance at templateXValue[0] worldwide reached record levels in 2017 , with over 346 templateScale attendees .

generated_template: This statistic shows the templateYLabel[0] of times templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] as of 2018 . In that year , templateTitle[0] templateTitle[1] templateTitle[2] had a total of templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of times Leading cinema circuits North in North America as of 2018 .  In that year , Leading cinema circuits had a total of 7350 screens .

Example 158:
titleEntities: {'Subject': ['North America'], 'Date': ['2017']}
title: Reasons for cutting the cord in North America 2017
X_Axis['Response']: ['Price_-_too_expensive', 'I_use_an_internet_streaming_service_such_as_Netflix_Hulu_Amazon_Video_etc.', 'I_use_an_antenna_to_get_the_basic_channels_on_my_TV', 'I_like_to_binge_watch_an_entire_season_of_a_TV_series_through_my_streaming_service', 'I_moved/relocated_and_I_do_not_plan_to_sign-up_for_cable/satellite_service_again', 'The_bulk_of_my_TV_viewing_was_the_original_series_on_streaming_services', "I_share_a_friend/family_member's_login_to_watch_shows_on_their_cable/satellite_provider's_app"]
Y_Axis['Share', 'of', 'respondents']: ['86.7', '39.7', '23', '15.9', '13', '7.7', '0.9']

gold: The graph shows reasons for cutting the cord named by respondents from North America in the fourth quarter of 2017 . During a a survey , it was found that 86.7 percent of respondents cut off their cable or satellite service because it was too expensive .
gold_template: The graph templateXValue[last] templateTitle[0] templateXValue[4] templateTitle[2] the templateTitle[3] named by templateYLabel[1] from templateTitleSubject[0] in the fourth quarter of templateTitleDate[0] . During a survey , it templateXValue[5] found that templateYValue[max] templateScale of templateYLabel[1] cut off templateXValue[last] cable or satellite templateXValue[1] because it templateXValue[5] templateXValue[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2013 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they planned templateXValue[0] a templateXValue[0] .
generated: The statistic shows the Reasons for cutting cord North America in the 2017 as of 2013 .  During the survey , 86.7 % of the respondents stated that they planned Price_-_too_expensive a .

Example 159:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Dietary supplement usage in U.S. adults by gender 2018
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'U.S.', 'adults']: ['77', '73']

gold: This statistic indicates the percentage of U.S. adults that take dietary supplements , distributed by gender . The statistic is based on a survey conducted in August 2018 . Among U.S. adult males , some 73 percent reported taking dietary supplements .
gold_template: This statistic indicates the templateScale of templateYLabel[1] templateYLabel[2] that take templateTitle[0] supplements , distributed templateTitle[5] templateTitle[6] . The statistic is based on a survey conducted in 2018 . Among templateYLabel[1] adult males , some templateYValue[min] templateScale reported taking templateTitle[0] supplements .

generated_template: This statistic shows the results of a survey among Indian templateTitle[1] templateTitle[2] in the templateTitle[2] templateTitle[4] templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[2] were templateXValue[0] and templateYValue[min] templateScale were templateXValue[last] .
generated: This statistic shows the results of a survey among Indian supplement usage in the usage adults by gender .  During the survey period , 77 percentage of U.S. usage were Female and 73 percentage were Male .

Example 160:
titleEntities: {'Subject': ['Major League Soccer'], 'Date': ['2019']}
title: Major League Soccer teams ranked by operating income 2019
X_Axis['Team', 'Name']: ['Atlanta_United', 'LA_Galaxy', 'Portland_Timbers', 'Real_Salt_Lake', 'Seattle_Sounders', 'D.C._United', 'Sporting_Kansas_City', 'Orlando_City_SC', 'New_England_Revolution', 'Philadelphia_Union', 'Los_Angeles_FC', 'Vancouver_Whitecaps', 'Colorado_Rapids', 'San_Jose_Earthquakes', 'New_York_Red_Bulls', 'Houston_Dynamo', 'FC_Dallas', 'Columbus_Crew', 'Minnesota_United', 'Montreal_Impact', 'Chicago_Fire', 'New_York_City_FC', 'Toronto_FC']
Y_Axis['Operating', 'income/loss', 'in', 'million', 'U.S.', 'dollars']: ['7', '5', '4', '2', '1', '1', '1', '-1', '-2', '-5', '-5', '-5', '-5', '-5', '-6', '-6', '-7', '-8', '-8', '-12', '-16', '-16', '-19']

gold: The statistic shows a ranking of Major League Soccer teams according to their operating income/loss . Atlanta United had an operating income of seven million U.S. dollars in the 2019 MLS season .
gold_template: The statistic shows a ranking of templateTitleSubject[0] Soccer templateTitle[3] according to their templateYLabel[0] templateYLabel[1] . templateXValue[0] had an templateYLabel[0] templateTitle[7] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the templateTitleDate[0] MLS season .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of October templateTitleDate[0] . At that time , there were templateYValue[max] templateYLabel[1] templateTitle[1] .
generated: This statistic shows the Operating of the League Soccer teams ranked by in the operating as of October 2019 .  At that time , there were 7 income/loss League .

Example 161:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Number of people with hearing loss global vs European countries 2015
X_Axis['Country']: ['Global', 'Europe', 'European_Union', 'United_Kingdom', 'France', 'Spain', 'Netherlands', 'Austria', 'Sweden', 'Belgium', 'Poland', 'Denmark', 'Ireland']
Y_Axis['Estimated', 'number', 'of', 'people', 'with', 'hearing', 'loss']: ['328.0', '119.0', '51.0', '10.0', '6.0', '3.5', '1.6', '1.6', '1.4', '1.3', '1.0', '0.8', '0.8']

gold: This statistic shows the estimated number of people with hearing loss worldwide and in Europe as of 2015 , by country , in millions . As of this time an estimated 119 million people in the whole of Europe were hard of hearing , with 3.5 million of these people located in Spain .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] worldwide and in templateXValue[1] as of templateTitleDate[0] , by templateXLabel[0] , in templateScale . As of this time an templateYLabel[0] templateYValue[1] templateScale templateYLabel[2] in the whole of templateXValue[1] were hard of templateYLabel[4] , templateYLabel[3] templateYValue[5] templateScale of these templateYLabel[2] located in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in selected templateTitle[4] as of 2019 , templateTitle[6] templateXLabel[0] . According to the source , templateTitle[2] is projected to the most expensive templateXLabel[0] .
generated: This statistic shows the Estimated of the Number people hearing loss in selected global as of 2019 , European Country .  According to the source , hearing is projected to the most expensive Country .

Example 162:
titleEntities: {'Subject': ['Michael Kors'], 'Date': ['2020']}
title: Number of followers of Michael Kors on social media 2020
X_Axis['Platform']: ['Facebook', 'Instagram', 'Twitter']
Y_Axis['Number', 'of', 'followers', 'in', 'millions']: ['17.91', '16.0', '3.5']

gold: This statistic depicts the number of followers of Michael Kors on social media as of January 2020 . During the measured period , the largest social media presence of the brand was on Facebook with 17.91 million followers , as opposed to its 3.5 million follower base on Twitter .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] on templateTitle[4] templateTitle[5] as of 2020 . During the measured period , the largest templateTitle[4] templateTitle[5] presence of the brand was on templateXValue[0] with templateYValue[max] templateScale templateYLabel[1] , as opposed to its templateYValue[min] templateScale follower base on templateXValue[last] .

generated_template: This statistic gives information on the global active usage penetration rate of leading templateTitle[2] media sites as of 2019 . During the survey period , it was found that templateYValue[max] templateScale of internet users had found that they used the templateYLabel[1] .
generated: This statistic gives information on the global active usage penetration rate of leading Michael media sites as of 2019 .  During the survey period , it was found that 17.91 millions of internet users had found that they used the followers .

Example 163:
titleEntities: {'Subject': ['Foursquare'], 'Date': ['2010', '2014']}
title: Number of registered members on Foursquare 2010 to 2014
X_Axis['Month']: ["Dec_'10", "Jan_'11", "Mar_'12", "May_'12", "Jan_'13", "Jan_'14", "May_'14", "Oct_'14"]
Y_Axis['Number', 'of', 'registered', 'members', 'in', 'millions']: ['5', '6', '15', '20', '30', '45', '50', '55']

gold: This statistic gives information on the number of registered members on Foursquare between December 2010 and October 2014 . As of that month , the social check-in app community had accumulated over 55 million members worldwide .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] on templateTitleSubject[0] between 2010 and October templateTitleDate[max] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] templateScale templateYLabel[2] worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In that year , the majority of the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Number of registered members in Foursquare from 2010 to 2014 .  In that year , the majority of the Number registered members in the Foursquare amounted to 5 millions .

Example 164:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Dating website or app usage among U.S. online users 2019
X_Axis['Response']: ["Yes_I'm_doing_so_currently", "Yes_I've_done_so_in_the_past", 'No_never', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['7', '24', '65', '3']

gold: This statistic presents the percentage of adult online users in the United States who have used a dating website or app as of January 2019 . According to the findings , only seven percent of respondents stated that they were currently using a dating website or app , while in comparison 65 percent of respondents reported to have never used a dating app or website before .
gold_template: This statistic presents the templateScale of adult templateTitle[6] templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of 2019 . According to the findings , only templateYValue[0] templateScale of templateYLabel[1] stated that they were templateXValue[0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison templateYValue[max] templateScale of templateYLabel[1] reported to have templateXValue[2] used a templateTitle[0] templateTitle[2] or templateTitle[1] before .

generated_template: This statistic shows the hypothetical templateTitle[2] templateTitle[3] templateXValue[0] a templateTitle[1] on templateTitleSubject[0] membership in templateTitleSubject[1] templateXValue[0] ( templateTitle[6] ) as of October templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] stated that they had attained a templateXValue[1] .
generated: This statistic shows the hypothetical app usage Yes_I'm_doing_so_currently a website on U.S. membership in U.S. Yes_I'm_doing_so_currently ( online ) as of October 2019 .  During the survey , 24 % of respondents stated that they had attained a Yes_I've_done_so_in_the_past .

Example 165:
titleEntities: {'Subject': ['Inflation'], 'Date': ['2019']}
title: Inflation rate of the main industrialized and emerging countries 2019
X_Axis['Country']: ['Russia', 'Brazil', 'India', 'China', 'USA', 'United_Kingdom', 'Germany', 'France', 'Japan']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4.68', '3.79', '3.44', '2.31', '1.82', '1.81', '1.49', '1.17', '0.99']

gold: This statistic shows the inflation rate of the main industrialized and emerging countries in 2019 . In 2019 , the inflation rate in China was estimated to amount to approximately 2.31 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] in templateXValue[3] was estimated to amount to approximately templateYValue[3] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[14] templateTitle[4] in templateTitle[5] templateTitleDate[0] . In templateTitleDate[0] , templateXValue[3] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of about templateYValue[3] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Inflation rate in the Japan emerging in countries 2019 .  In 2019 , China ranked 4th rate an estimated Inflation rate of about 2.31 % compared to the previous year .

Example 166:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2020']}
title: Monthly car loan rates in the U.S. 2017 to 2020
X_Axis['Month']: ['Jan_20', 'Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14']
Y_Axis['Interest', 'rate']: ['4.56', '4.61', '4.59', '4.61', '4.61', '4.63', '4.66', '4.74', '4.72', '4.77', '4.77', '4.78', '4.77', '4.96', '4.93', '4.93', '4.79', '4.8', '4.83', '4.82', '4.64', '4.59', '4.52', '4.53', '4.51', '4.43', '4.29', '4.3', '4.29', '4.25', '4.51', '4.44', '4.38', '4.35', '4.38', '4.36', '4.35', '4.32', '4.27', '4.26', '4.23', '4.18', '4.17', '4.17', '4.25', '4.28', '4.33', '4.29', '4.33', '4.4', '4.33', '4.3', '4.32', '4.38', '4.37', '4.39', '4.37', '4.37', '4.31', '4.07', '4.07', '4.12', '4.06', '4.04', '4.02', '4.03', '4.03', '4.13', '4.13', '4.18', '4.23', '4.21', '4.25']

gold: This statistic presents the average interest rate on 60-month new car loans in the United States from January 2014 to January 2020 . Car loan interest rates amounted to 4.56 percent as of January 30 , 2020 . The smaller the car loan interest rates , the cheaper the loan is .
gold_template: This statistic presents the average templateYLabel[0] templateYLabel[1] on 60-month new templateTitle[1] loans in the templateTitle[4] from 2014 to 2020 . templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] amounted to templateYValue[0] templateScale as of 30 , templateTitleDate[max] . The smaller the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] , the cheaper the templateTitle[2] is .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] as of 2019 . As of 2020 , the last reported period , the templateYLabel[1] templateYLabel[2] of templateYValue[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Monthly car loan in the rates as of 2019 .  As of 2020 , the last reported period , the rate of 4.56 rate .

Example 167:
titleEntities: {'Subject': ['Europe'], 'Date': ['2016']}
title: Number of natural mineral waters in Europe 2016 , by country
X_Axis['Country']: ['Germany', 'Italy', 'Hungary', 'Spain', 'Poland', 'France', 'Romania', 'United_Kingdom', 'Greece', 'Austria', 'Belgium', 'Bulgaria', 'Portugal', 'Slovakia', 'Lithuania', 'Netherlands', 'Czech_Republic', 'Denmark', 'Sweden', 'Slovenia', 'Latvia', 'Estonia', 'Croatia', 'Ireland', 'Finland']
Y_Axis['Litres', 'consumed', 'per', 'capita']: ['821', '322', '214', '165', '119', '90', '69', '67', '44', '33', '27', '22', '21', '20', '17', '13', '11', '11', '11', '9', '5', '4', '4', '2', '1']

gold: This statistic represents the number of natural mineral waters in Europe in 2016 . Germany had the highest number of natural mineral waters with 821 certified natural mineral water sources .
gold_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had the highest templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] certified templateTitle[1] templateTitle[2] water sources .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In this year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the market with templateYValue[1] liters liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the European Union is predominantly made up of the natural mineral templateTitle[1] category.Germany is the market with the largest amount of different mineral templateTitle[1] brands .
generated: This statistic shows the per capita mineral of Number natural in Europe 2016 .  In this year , Germany was the leading market for the mineral of Number natural with 821 liters of Number natural consumed per person .  Number natural in the market with 322 liters of Number natural consumed per person .  Number natural in the European Union is predominantly made up of the natural mineral category.Germany is the market with the largest amount of different mineral natural brands .

Example 168:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2016']}
title: Luxury destinations with the largest growth in travel worldwide 2016
X_Axis['Country']: ['Kenya', 'Iceland', 'Saint_Martin', 'China', 'Ecuador', 'Japan', 'South_Africa', 'Tanzania', 'Croatia', 'Jamaica']
Y_Axis['Year-over-year', 'travel', 'growth']: ['59', '56', '39', '35', '34', '32', '28', '27', '25', '23']

gold: This statistic shows the luxury travel destinations with the largest growth in travel worldwide as of August 2016 . Luxury travel to Kenya grew by 59 percent in 2016 compared with the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[1] templateTitle[1] templateTitle[2] the templateTitle[3] templateYLabel[2] in templateYLabel[1] templateTitle[6] as of 2016 . templateTitleSubject[0] templateYLabel[1] to templateXValue[0] templatePositiveTrend by templateYValue[max] templateScale in templateTitleDate[0] compared templateTitle[2] the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] were ranked first templateTitle[1] an templateYLabel[0] of templateYValue[min] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Year-over-year of the destinations largest growth travel in the United Kingdom ( Luxury ) in 2016 .  In 2016 , Kenya were ranked first destinations an Year-over-year of 23 travel growth .

Example 169:
titleEntities: {'Subject': ['European Union'], 'Date': []}
title: Fresh orange production volume in the European Union 2016/17 , by country
X_Axis['Country']: ['Spain', 'Italy', 'Greece', 'Portugal', 'Cyprus']
Y_Axis['Volume', 'in', '1,000', 'tons']: ['3731', '1500', '920', '344', '30']

gold: In 2018/2019 , Spain was the leading producer of fresh oranges in the European Union ( EU28 ) , with over 3.7 million tons of fresh oranges produced . The Spanish production was more than two times the production of Italy , the second largest producer of oranges . The other three producers in the EU produced less than one million tons during this year .
gold_template: In 2018/2019 , templateXValue[0] was the leading producer of templateTitle[0] oranges in the templateTitleSubject[0] ( EU28 ) , with over 3.7 templateScale templateYLabel[2] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than two times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the EU produced less than one templateScale templateYLabel[2] during this year .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , broken down templateTitle[7] templateXLabel[0] . In that year , templateXValue[0] had the highest templateYLabel[0] of any templateTitle[0] templateTitle[1] at templateYValue[max] templateScale .
generated: This statistic shows the Volume of European Union orange production volume European in , broken down by Country .  In that year , Spain had the highest Volume of any Fresh orange at 3731 % .

Example 170:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017']}
title: Most important export partner countries for Brazil in 2017
X_Axis['Country']: ['China', 'United_States', 'Argentina', 'Netherlands']
Y_Axis['Share', 'in', 'total', 'export']: ['21.8', '12.5', '8.1', '4.3']

gold: This statistic shows the most important export partner countries for Brazil in 2017 . In 20167 the main export partner country of Brazil was China with a share of 21.8 percent in exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleSubject[0] in templateTitleDate[0] . In 20167 the main templateYLabel[2] templateTitle[3] templateXLabel[0] of templateTitleSubject[0] was templateXValue[0] with a templateYLabel[0] of templateYValue[max] templateScale in exports .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2019 , sorted templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] templateXLabel[0] . During that year , around templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the Share of Brazil important export partner as of 2019 , sorted for Brazil 2017 Country .  During that year , around 21.8 % of all export .

Example 171:
titleEntities: {'Subject': ['Stuxnet'], 'Date': []}
title: Stuxnet - percentage of infected hosts by country
X_Axis['Country']: ['Iran', 'Indonesia', 'India', 'Azerbaijan', 'Pakistan', 'Malaysia', 'U.S.', 'Uzbekistan', 'Russia', 'Great_Britain', 'Other']
Y_Axis['Percentage', 'of', 'infected', 'hosts']: ['58.31', '17.83', '9.96', '3.4', '1.4', '1.16', '0.89', '0.71', '0.61', '0.57', '5.15']

gold: The statistic shows the percentage of Stuxnet infected hosts by country in 2010 . 58.31 percent of infected hosts were located in Iran .
gold_template: The statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateXLabel[0] in 2010 . templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] were located in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] templateTitle[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of templateTitleDate[0] . As of that year , there were templateYValue[max] templateScale templateTitle[0] templateTitle[1] templateTitle[2] .
generated: This statistic shows the Percentage of the percentage Stuxnet infected hosts by country as of .  As of that year , there were 58.31 percentage Stuxnet infected .

Example 172:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2020']}
title: LinkedIn : distribution of global audiences 2020 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'users']: ['43', '57']

gold: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 43 percent of LinkedIn audiences were female and 57 percent were male .
gold_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] templateScale of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] templateScale were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitle[0] templateYLabel[1] in the templateTitle[2] as of Fall templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] templateScale of templateTitle[0] templateYLabel[1] were templateXValue[0] and templateYValue[min] templateScale of templateYLabel[1] were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users in the global as of Fall 2020 , sorted by gender .  During the survey period , 57 percentage of LinkedIn users were Female and 43 percentage of users were Male .

Example 173:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Most popular social networks of U.S. teens 2016
X_Axis['Platform']: ['YouTube', 'Gmail', 'Snapchat', 'Instagram', 'Facebook', 'Kik_Messenger', 'Skype', 'Twitter', 'Vine', 'Tumblr']
Y_Axis['Percentage', 'of', 'teenagers']: ['91', '75', '66', '65', '61', '52', '43', '40', '31', '24']

gold: This statistic provides information about the most popular websites visited by teenagers in the United States as of June 2016 . During the survey period , video sharing platform YouTube was most popular among U.S. teens with a 91 percent usage rate . Snapchat was ranked third with 66 percent reporting that they accessed the photo sharing app .
gold_template: This statistic provides information about the templateTitle[0] templateTitle[1] websites visited by templateYLabel[1] in the templateTitle[4] as of 2016 . During the survey period , video sharing templateXLabel[0] templateXValue[0] was templateTitle[0] templateTitle[1] among templateTitleSubject[0] templateTitle[5] with a templateYValue[max] templateScale usage rate . templateXValue[2] was ranked third with templateYValue[2] templateScale reporting that they accessed the photo sharing app .

generated_template: This statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] as of 2019 , ranked first . During the survey period , it was found that templateYValue[min] templateScale of the templateYLabel[1] templateYLabel[2] in the previous year .
generated: This statistic depicts the Percentage of the popular social networks U.S. in the teens as of 2019 , ranked first .  During the survey period , it was found that 24 percentage of the teenagers in the previous year .

Example 174:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading baby wipes vendors in the U.S. 2016 , based on sales
X_Axis['Company']: ['Private_label', 'Kimberly_Clark_Corp.', 'Procter_&_Gamble', 'Seventh_Generation', 'The_Honest_Co.', 'Johnson_&_Johnson', 'Nice-Pak_Products', 'Paper_Partners', 'Kas_Direct', 'Irish_Breeze']
Y_Axis['Million', 'U.S.', 'dollars']: ['494.4', '416.2', '276.6', '9.6', '8.6', '7.1', '6.3', '6.1', '5.7', '4.4']

gold: The statistic shows the leading baby wipes vendors in the United States in 2016 , based on sales . In that year , Kimberly Clark was the second largest U.S. baby wipes vendor with sales of 416.2 million U.S. dollars . Total sales of U.S. baby wipes vendors amounted to about 1.25 billion U.S. dollars in 2016 .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[6] on templateTitle[7] . In that year , templateXValue[1] was the second largest templateYLabel[1] templateTitle[1] templateTitle[2] vendor with templateTitle[7] of templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 templateScale templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateTitle[5] templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateXValue[0] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateXValue[1] had a templateYValue[1] templateScale global communications and templateXValue[1] .
generated: The statistic shows the Million U.S. of the wipes vendors U.S. in 2016 .  In that year , the 2016 Company , the baby wipes vendors U.S. of Private_label .  baby wipes vendors U.S. Kimberly_Clark_Corp. had a 416.2 million global communications and Kimberly_Clark_Corp. .

Example 175:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Number of aggravated assaults in the U.S. in 2018 , by state
X_Axis['State']: ['California', 'Texas', 'Florida', 'New_York', 'Tennessee', 'Michigan', 'Illinois', 'North_Carolina', 'Pennsylvania', 'Arizona', 'Georgia', 'Missouri', 'Alabama', 'South_Carolina', 'Louisiana', 'Ohio', 'Indiana', 'Massachusetts', 'Maryland', 'Colorado', 'Washington', 'New_Mexico', 'Oklahoma', 'Arkansas', 'Wisconsin', 'New_Jersey', 'Virginia', 'Nevada', 'Kansas', 'Oregon', 'Minnesota', 'Iowa', 'Kentucky', 'Mississippi', 'Alaska', 'Utah', 'Connecticut', 'District_of_Columbia', 'West_Virginia', 'Nebraska', 'Montana', 'Idaho', 'Delaware', 'South_Dakota', 'Hawaii', 'North_Dakota', 'New_Hampshire', 'Rhode_Island', 'Wyoming', 'Maine', 'Vermont']
Y_Axis['Number', 'of', 'aggravated', 'assaults']: ['105412', '73656', '55551', '43171', '31717', '31021', '30539', '27526', '24077', '23528', '22783', '22042', '18944', '18446', '17866', '17674', '16834', '16648', '16135', '14547', '14251', '13598', '13084', '12378', '11263', '10463', '10113', '10027', '9559', '7360', '6857', '5931', '5059', '4696', '4391', '4319', '4294', '3971', '3945', '3461', '3120', '2957', '2845', '2682', '1925', '1560', '1435', '1366', '870', '803', '710']

gold: This statistic shows the total number of aggravated assaults reported in the United States in 2018 , by state . In 2018 , the federal state of California was ranked first with 105,412 cases of aggravated assaults , followed by Texas with 73,656 reported cases of aggravated assaults .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] reported in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , the federal templateXLabel[0] of templateXValue[0] was ranked first with templateYValue[max] cases of templateYLabel[1] templateYLabel[2] , followed templateTitle[5] templateXValue[1] with templateYValue[1] reported cases of templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitleDate[0] . As of this time , there were about templateYValue[1] templateYLabel[0] of templateXValue[0] .
generated: This statistic shows the Number of U.S. aggravated assaults U.S. in the 2018 as of 2018 .  As of this time , there were about 73656 Number of California .

Example 176:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012', '2015']}
title: Smartphone use for professional reasons among U.S. physicians 2012 to 2015
X_Axis['Month']: ['March_2015', 'March_2014', 'March_2013', 'March_2012']
Y_Axis['Percentage', 'of', 'respondents']: ['84', '79', '76', '68']

gold: This survey indicates the percentage of physicians in the United States who use smartphones for professional purposes from March 2012 to March 2015 . In March 2014 , 79 percent of surveyed physicians used smartphones for their medical practice . Use of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .
gold_template: This survey indicates the templateScale of templateTitle[7] in the templateTitle[6] who templateTitle[1] smartphones templateTitle[2] templateTitle[3] purposes from templateXValue[0] templateXValue[last] to templateXValue[0] . In templateXValue[0] templateXValue[1] , templateYValue[1] templateScale of surveyed templateTitle[7] used smartphones templateTitle[2] their medical practice . templateTitle[1] of digital health has grown in the medical industry as consumers are expressing templatePositiveTrend interests in mobile health technologies .

generated_template: The statistic shows the results of a global survey on templateTitleSubject[0] in God or a supreme being . The survey was conducted in 23 countries in 2010 . templateYValue[5] templateScale of templateYLabel[1] in the country stated they believe in God or a higher being .
generated: The statistic shows the results of a global survey on U.S. in God or a supreme being .  The survey was conducted in 23 countries in 2010 .  68 percentage of respondents in the country stated they believe in God or a higher being .

Example 177:
titleEntities: {'Subject': ['Instagram Stories'], 'Date': ['16', '19']}
title: Daily active users of Instagram Stories 2019
X_Axis['Month']: ["Jan_'19", "Jun_'18", "Oct_'17", "Jun_'17", "Apr_'17", "Jan_'17", "Oct_'16"]
Y_Axis['Number', 'of', 'DAU', 'in', 'millions']: ['500', '400', '300', '250', '200', '150', '100']

gold: In January 2019 , photo sharing platform Instagram reported 500 million daily active Stories users worldwide , up from 400 million global DAU in June 2018 . Stories is a feature of the app allowing users post photo and video sequences that disappear 24 hours after being posted . Instagram usageInstagram has over one billion monthly active users and is one of the most popular social networks worldwide .
gold_template: In 2019 , photo sharing platform templateTitleSubject[0] reported templateYValue[max] templateScale templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] worldwide , up from templateYValue[1] templateScale global templateYLabel[1] in 2018 . templateTitleSubject[0] is a feature of the app allowing templateTitle[2] post photo and video sequences that disappear 24 hours after being posted . templateTitleSubject[0] usageInstagram has over templateYValue[max] templateScale monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: As of 2019 , templateTitleSubject[0] had templateYValue[max] templateScale templateTitle[1] templateTitle[2] templateYLabel[2] worldwide , up from templateYValue[4] templateScale templateYLabel[2] in the previous year . templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] is is one of the most popular social network . As of 2019 . The templateTitleSubject[0] is relevant for teenagers Originally launched in the templateTitle[1] templateTitle[2] has become one of the most popular social messaging and photo sharing apps worldwide ; making its CEO and co-founder Evan Spiegel one of the world 's richest social media entrepreneurs . With an estimated templateYValue[min] templateScale monthly templateTitle[1] templateTitle[2] in the country , templateTitleSubject[0] easily ranks among the most popular social apps in the country .
generated: As of 2019 , Instagram Stories had 500 millions active users millions worldwide , up from 200 millions in the previous year .  Daily active users in the Instagram Stories is one of the most popular social network .  As of 2019 .  The Instagram Stories is relevant for teenagers Originally launched in the active users has become one of the most popular social messaging and photo sharing apps worldwide ; making its CEO and co-founder Evan Spiegel one of the world 's richest social media entrepreneurs .  With an estimated 100 millions monthly active users in the country , Instagram Stories easily ranks among the most popular social apps in the country .

Example 178:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2017']}
title: Latin America & the Caribbean : homicide rates 2017 , by country
X_Axis['Country']: ['El_Salvador', 'Jamaica', 'Honduras', 'Belize', 'Bahamas', 'Brazil', 'Guatemala', 'Colombia', 'Mexico', 'Puerto_Rico', 'Guyana', 'Costa_Rica', 'Dominican_Republic', 'Grenada', 'Panama', 'Uruguay', 'Peru', 'Nicaragua', 'Ecuador', 'Suriname', 'Argentina', 'Chile']
Y_Axis['Homicides', 'per', '100,000', 'inhabitants']: ['61.8', '57.0', '41.7', '37.9', '30.9', '30.5', '26.1', '24.9', '24.8', '18.5', '14.8', '12.3', '11.3', '11.1', '9.7', '8.2', '7.7', '7.4', '5.8', '5.5', '5.2', '4.3']

gold: Countries in Central America and the Caribbean registered some of the highest homicide rates in the Latin American region in 2017 . El Salvador ranked first , with nearly 62 homicides committed per 100,000 inhabitants . Jamaica came in second , with 57 homicides per 100,000 people .
gold_template: Countries in Central templateTitleSubject[0] and the templateTitleSubject[0] registered some of the highest templateTitle[4] templateTitle[5] in the templateTitleSubject[0] American region in templateTitleDate[0] . templateXValue[0] ranked first , with nearly templateYValue[max] templateYLabel[0] committed templateYLabel[1] 100,000 templateYLabel[3] . templateXValue[1] came in second , with templateYValue[1] templateYLabel[0] templateYLabel[1] 100,000 people .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , broken down templateTitle[4] . According to the source , templateXValue[0] had a templateYLabel[0] of templateYValue[max] templateScale of internet users in templateXValue[1] .
generated: This statistic shows the Homicides of the Latin America Caribbean America & Caribbean in 2017 , broken down homicide .  According to the source , El_Salvador had a Homicides of 61.8 % of internet users in Jamaica .

Example 179:
titleEntities: {'Subject': ['Moscow'], 'Date': ['2019']}
title: Prime office rental prices in Moscow Q1 2015-Q2 2019
X_Axis['Quarter']: ["Q1_'15", "Q2_'15", "Q3_'15", "Q4_'15", "Q1_'16", "Q2_'16", "Q3_'16", "Q4_'16", "Q1_'17", "Q2_'17", "Q3_'17", "Q4_'17", "Q2_'18", "Q3_'18", "Q1_'19", "Q2_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['760', '697', '692', '670', '670', '720', '613', '760', '726', '684', '669', '654', '693', '693', '703', '704']

gold: The statistic displays the rental costs per square meter of prime office spaces in Moscow , Russia , from the first quarter 2015 to the second quarter 2019 . It can be seen that the price of prime office properties in Moscow fluctuated , reaching the lowest price in the third quarter of 2016 at 613 euros per square meter per year . As of the second quarter of 2019 , rental costs per square meter of prime office spaces in Moscow amounted to 703 .
gold_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] , Russia , from the first templateXLabel[0] 2015 to the second templateXLabel[0] templateTitleDate[0] . It can be seen that the price of templateTitle[0] templateTitle[1] properties in templateTitleSubject[0] fluctuated , reaching the lowest price in the third templateXLabel[0] of 2016 at templateYValue[min] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . As of the second templateXLabel[0] of templateTitleDate[0] , templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] amounted to templateYValue[14] .

generated_template: The statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[1] spaces in templateTitleSubject[0] ( CBD ) , France , from the first templateXLabel[0] of templateTitleDate[min] to the corresponding templateXLabel[0] . It can be seen that the price of Parisian templateTitle[0] templateTitle[1] properties templatePositiveTrend over time , reaching templateYValue[last] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year as of the first templateXLabel[0] .
generated: The statistic gives information on the Cost of per square meter office spaces in Moscow ( CBD ) , France from the first Quarter of 2019 to the corresponding Quarter .  It can be seen that the price of Parisian Prime office properties increased over time , reaching 704 euros per square meter per year as of the first Quarter .

Example 180:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Average U.S. brand response rate on social media 2017 , by vertical
X_Axis['Month']: ['Utilities', 'Retail', 'Consumer_Goods', 'Banking/Finance', 'Travel/Hospitality', 'Internet/Technology', 'Marketing/Advertising', 'Automotive', 'Real_Estate', 'Healthcare', 'Professional_Services', 'Government', 'Education', 'Nonprofit', 'Media/Entertainment']
Y_Axis['Average', 'response', 'rate']: ['18', '16', '14', '13', '12', '11', '11', '11', '10', '9', '9', '8', '7', '7', '6']

gold: This statistic presents the average brand response rate on social media in the United States as of the third quarter of 2017 , by vertical . According to the findings , the retail industry had an average response rate of 16 percent to communicating back to their consumers on social media , while the consumer goods industry reported in 14 percent .
gold_template: This statistic presents the templateYLabel[0] templateTitle[2] templateYLabel[1] templateYLabel[2] on templateTitle[5] templateTitle[6] in the templateTitle[1] as of the third quarter of templateTitleDate[0] , templateTitle[8] templateTitle[9] . According to the findings , the templateXValue[1] industry had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[1] templateScale to communicating back to their consumers on templateTitle[5] templateTitle[6] , while the templateXValue[2] industry reported in templateYValue[2] templateScale .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of 2018 , sorted by global templateYLabel[1] templateYLabel[2] . As of that templateXLabel[0] , there were templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[last] , earned an average of templateYValue[max] templateScale U.S. dollars in templateXValue[0] .
generated: This statistic shows the Average U.S. brand response rate U.S. media as of 2018 , sorted by global response rate .  As of that Month , there were 14 response rate in Media/Entertainment , earned an average of 18 million U.S. dollars in Utilities .

Example 181:
titleEntities: {'Subject': ['UK'], 'Date': ['2018', '2018']}
title: UK : reach of top active social media platforms in Q3 2018
X_Axis['Platform']: ['Youtube', 'Facebook', 'FB_Messenger', 'Whatsapp', 'Instagram', 'Twitter', 'Snapchat', 'LinkedIn', 'Pinterest', 'Skype', 'Reddit', 'Tumblr', 'Twitch', 'WeChat', 'Viber', 'Imgur']
Y_Axis['Share', 'of', 'respondents']: ['80', '78', '60', '58', '47', '46', '27', '27', '27', '22', '14', '13', '12', '8', '8', '7']

gold: This statistic illustrates the results of a survey about the leading active social media platforms in the UK in 2018 . During the survey period , it was found that 80 percent of the respondents reported that they used Facebook . Facebook is a popular free social networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .
gold_template: This statistic illustrates the results of a survey about the leading templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[max] templateScale of the templateYLabel[1] reported that they used templateXValue[1] . templateXValue[1] is a popular free templateTitle[4] networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .

generated_template: templateXValue[0] was the most popular templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitleSubject[0] who were using templateTitleSubject[0] as of templateTitleDate[0] . During the survey period it was found that templateYValue[max] templateScale of templateYLabel[1] stated that go online had experienced templateXValue[13] abuse stated that they used templateXValue[0] . templateXValue[1] and templateYValue[1] templateScale of templateYLabel[1] .
generated: Youtube was the most popular reach top Platform in the UK who were using UK as of 2018 .  During the survey period it was found that 80 % of respondents stated that go online had experienced WeChat abuse stated that they used Youtube .  Facebook and 78 % of respondents .

Example 182:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1900', '2016']}
title: Earthquakes that caused the most economic damage in the U.S. 1900 to 2016
X_Axis['Date,', 'Location']: ['January_17_1994_Los_Angeles', 'October_18_1989_San_Francisco', 'February_28_2001_Seattle', 'March_28_1964_Prince_William_Sound', 'August_24_2014_San_Francisco_California', 'February_9_1971_Los_Angeles', 'April_18_1906_San_Francisco', 'October_1_1987_Los_Angeles', 'December_22_2003_San_Robbles_(California)', 'October_15_2006_Hawai_Island', 'June_28_1992_Landers_California', 'April_22_1992_South_California']
Y_Axis['Damage', 'in', 'million', 'U.S.', 'dollars']: ['30000', '5600', '2000', '1020', '700', '535', '524', '213', '200', '150', '100', '100']

gold: The statistic shows the earthquakes that resulted in the most economic damage in the United States from 1900 to 2016 . The earthquake that occurred on January 17 , 1994 in Los Angeles caused approximately 30 billion U.S. dollars worth of damage and is the costliest earthquake on record .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] resulted in the templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[6] from templateTitleDate[min] to templateTitleDate[max] . The earthquake templateTitle[1] occurred on templateXValue[0] , templateXValue[0] in templateXValue[0] caused approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] worth of templateYLabel[0] and is the costliest earthquake on record .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] templateTitle[6] as of 2013 . As of that templateXLabel[0] templateXValue[0] had a total of around templateYValue[max] templateScale templateYLabel[1] .
generated: This statistic shows the Earthquakes that caused most of U.S. damage as of 2013 .  As of that Date, January_17_1994_Los_Angeles had a total of around 30000 million .

Example 183:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: U.S. consumers ' purchase location of shampoos and conditioners 2014
X_Axis['Response']: ['"Big_Box"_retail_store_(e.g._Walmart_Target)', 'Grocery_store/Supermarket', 'Pharmacy_(e.g._CVS_Walgreens)', 'Online_(Net)', 'Online_mass_merchandiser_(e.g._Amazon_drugstore.com)', "Department_Stores_(e.g._Macy's_Nordstrom)", 'In-person_at_a_specialty_beauty_products_merchant_(e.g._Sephora)', 'Online_specialty_beauty_products_merchant_(e.g._Sephora_Ultra)', 'In-person/not_in_a_store_(e.g._Avon_Mary_Kay)', 'Online_through_a_"sampling"_membership_program_(e.g._Ipsy_Birchbox)', "Online_through_a_specific_brand's_website_(e.g._Clairol_CoverGirl)", 'Somewhere_else']
Y_Axis['Share', 'of', 'respondents']: ['62', '36', '31', '12', '8', '5', '4', '3', '2', '1', '1', '10']

gold: This statistic presents the results of a survey among U.S. adult consumers . The survey was fielded online by Harris Interactive in June 2014 , asking the respondents where they usually purchase their shampoo and/or conditioners . Some 12 percent of U.S. adults indicated that they buy their shampoo/conditioner online .
gold_template: This statistic presents the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] templateScale of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] they templateXValue[0] . As of templateTitleDate[0] , templateYValue[max] templateScale of templateYLabel[1] stated that they believed that they had abandoned shopping templateTitle[5] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the U.S. they "Big_Box"_retail_store_(e.g._Walmart_Target) .  As of 2014 , 62 % of respondents stated that they believed that they had abandoned shopping shampoos .

Example 184:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading U.S. states in sunflower production 2019
X_Axis['State']: ['South_Dakota', 'North_Dakota', 'Minnesota', 'California', 'Colorado', 'Kansas', 'Nebraska', 'Texas']
Y_Axis['Production', 'in', 'thousand', 'pounds']: ['831600', '740700', '102630', '70680', '59400', '53925', '44850', '39650']

gold: The U.S. state with the highest production volume of sunflowers is South Dakota at 831.6 million pounds in 2019 . North Dakota came in second at 740.7 million pounds of sunflowers . Sunflower products There are several products that are derived from sunflowers .
gold_template: The templateTitleSubject[0] templateXLabel[0] with the highest templateYLabel[0] volume of sunflowers is templateXValue[0] at templateYValue[max] templateScale templateYLabel[2] in templateTitleDate[0] . templateXValue[1] templateXValue[0] came in second at templateYValue[1] templateScale templateYLabel[2] of sunflowers . templateTitle[3] products There are several products that are derived from sunflowers .

generated_template: This statistic shows the top ten templateTitle[0] producing templateTitleSubject[0] states in templateTitleDate[0] . In that year , templateXValue[0] was the company with a templateYLabel[0] of templateYValue[max] templateScale of templateTitleSubject[0] dollars in templateTitleDate[0] .
generated: This statistic shows the top ten Leading producing U.S. states in 2019 .  In that year , South_Dakota was the company with a Production of 831600 thousand of U.S. dollars in 2019 .

Example 185:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2016']}
title: Leading eSports pro players on Twitter worldwide 2016 , by number of followers
X_Axis['Month']: ['Søren_Bjerg_(Bjergsen)', 'Enrique_Cedeño_Martínez_(xPeke)', 'Yiliang_Peng_(Doublelift)', 'Jason_Tran_(WildTurtle)', 'Danil_Ishutin_(Dendi)', 'Hai_Du_Lam_(Hai)', 'Henrik_Hansen_(Froggen)', 'Martin_Larsson_(Rekkles)', 'Bora_Kim_(Yell0wStaR)', 'Zachary_Scuderi_(Sneaky)']
Y_Axis['Number', 'of', 'Twitter', 'followers', 'in', 'thousands']: ['604', '596', '393', '376', '334', '297', '270', '258', '256', '244']

gold: The graph shows the leading eSports professional players on Twitter worldwide as of January 2016 , ranked by the number of fans . As of the measured period , Søren Bjerg , a player from Denmark also known as Bjergsen , was the most famous on Twitter , with 604 thousand followers . He was followed by Enrique Martínez , aka xPeke , who gathered 596 thousand followers on Twitter .
gold_template: The graph shows the templateTitle[0] templateTitle[1] professional templateTitle[3] on templateYLabel[1] templateTitle[5] as of 2016 , ranked templateTitle[7] the templateYLabel[0] of fans . As of the measured period , templateXValue[0] , a player from Denmark also known as Bjergsen , was the most famous on templateYLabel[1] , with templateYValue[max] thousand templateYLabel[2] . He was followed templateTitle[7] templateXValue[1] , aka xPeke , who gathered templateYValue[1] thousand templateYLabel[2] on templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , the most affected by templateXValue[0] . As of that templateXLabel[0] templateXValue[0] time , had a total of templateYValue[max] templateScale templateYLabel[2] .
generated: This statistic shows the Leading eSports of Twitter pro in 2016 .  In 2016 , the most affected by Søren_Bjerg_(Bjergsen) .  As of that Month Søren_Bjerg_(Bjergsen) time , had a total of 604 thousands followers .

Example 186:
titleEntities: {'Subject': ['ACSI'], 'Date': ['2019']}
title: ACSI - U.S. customer satisfaction with social media 2019
X_Axis['Platform']: ['Pinterest', 'YouTube', 'Wikipedia', 'Instagram', 'Snapchat', 'Twitter', 'LinkedIn', 'Tumblr', 'Facebook']
Y_Axis['ACSI', 'score', '(100-point', 'scale)']: ['80', '78', '74', '72', '71', '69', '69', '64', '63']

gold: This graph shows the American Customer Satisfaction Index ( ACSI ) of customer satisfaction with social media websites in 2019 . Overall , Pinterest scored the highest level of customer satisfaction with 80 index points . Facebook was ranked last with an index score rating of 63 / 100 index points .
gold_template: This graph shows the American templateTitle[2] templateTitle[3] Index ( templateYLabel[0] ) of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] websites in templateTitleDate[0] . Overall , templateXValue[0] scored the highest level of templateTitle[2] templateTitle[3] templateTitle[4] templateYValue[max] index points . templateXValue[last] was ranked last templateTitle[4] an index templateYLabel[1] rating of templateYValue[min] / 100 index points .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] . During the survey period , it was found that templateYValue[max] templateScale of responding adults have used templateXValue[0] .
generated: This statistic shows the ACSI of (100-point in the U.S. customer satisfaction social media 2019 .  During the survey period , it was found that 80 % of responding adults have used Pinterest .

Example 187:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. mobile device owner monthly app download rate 2018 , by age group
X_Axis['Response']: ['Teens_(13-17)', 'Millennials_(18-34)', 'Gen_X_(35-54)', 'Boomers_(55-64)']
Y_Axis['Share', 'of', 'respondents']: ['83', '74', '59', '39']

gold: This statistic gives information on the percentage of mobile device owners in the United States who download apps at least once a month or more as of April 2018 , sorted by age group . During the survey period , it was found that 74 percent of responding Millennial app users downloaded apps to their mobile device on a monthly basis .
gold_template: This statistic gives information on the templateScale of templateTitle[1] templateTitle[2] owners in the templateTitle[0] who templateTitle[6] apps at least once a month or more as of 2018 , sorted templateTitle[9] templateTitle[10] templateTitle[11] . During the survey period , it was found that templateYValue[1] templateScale of responding Millennial templateTitle[5] users downloaded apps to their templateTitle[1] templateTitle[2] on a templateTitle[4] basis .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on templateXValue[1] templateTitle[3] as of 2018 . In today 's templateXValue[0] , templateYValue[min] templateScale of the templateYLabel[1] stated that they had attained a templateXValue[1] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on Millennials_(18-34) owner as of 2018 .  In today 's Teens_(13-17) , 39 % of the respondents stated that they had attained a Millennials_(18-34) .

Example 188:
titleEntities: {'Subject': ['Leading'], 'Date': ['2017']}
title: Leading countries worldwide based on coffee area harvested 2017
X_Axis['Country']: ['Brazil', 'Indonesia', 'Côte_d’Ivoire', 'Colombia', 'Ethiopia', 'Mexico', 'Viet_Nam', 'Honduras', 'India', 'Peru']
Y_Axis['Area', 'in', 'thousand', 'hectares']: ['1800.4', '1253.8', '925.44', '798.36', '694.33', '638.6', '605.18', '505.12', '449.36', '423.55']

gold: This statistic illustrates the global leading 10 countries based on coffee area harvested in 2017 . In that year , Mexico harvested an area of 638.6 thousand hectares of green coffee and was ranked sixth among coffee-growing countries worldwide .
gold_template: This statistic illustrates the global templateTitleSubject[0] 10 templateTitle[1] templateTitle[3] on templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitleDate[0] . In that year , templateXValue[5] templateTitle[6] an templateYLabel[0] of templateYValue[5] thousand templateYLabel[2] of green templateTitle[4] and was ranked sixth among coffee-growing templateTitle[1] templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , broken down by templateXLabel[0] . According to the source , had a templateYLabel[0] of templateYValue[min] templateScale of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] .
generated: This statistic shows the Area of the Leading countries worldwide based in 2017 , broken down by Country .  According to the source , had a Area of 423.55 thousand of based coffee area harvested .

Example 189:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2019']}
title: Facebook : worldwide quarterly revenue 2011 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['21082', '17652', '16886', '15077', '16914', '13727', '13231', '11966', '12972', '10328', '9321', '8032', '8809', '7011', '6436', '5382', '5841', '4501', '4042', '3543', '3851', '3203', '2910', '2502', '2585', '2016', '1813', '1458', '1585', '1262', '1184', '1058', '1131']

gold: In the fourth quarter of 2019 , social network Facebook 's total revenues amounted to 21.08 billion U.S. dollars , the majority of which were generated through advertising . The company announced over seven million active advertisers on Facebook during the third quarter of 2019 . During that fiscal period , the company 's net income was 7.35 billion U.S. dollars .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , social network templateTitleSubject[0] 's total revenues amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateScale active advertisers on templateTitleSubject[0] during the third templateXLabel[0] of templateTitleDate[max] . During that fiscal period , the company 's net income was 7.35 templateScale templateYLabel[2] templateYLabel[3] .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] . templateTitleSubject[0] 's main templateYLabel[0] source is advertising through templateTitleSubject[0] sites and online products . In templateTitleDate[max] , templateTitleSubject[0] accounted for the majority of parent company Alphabet 's revenues with 113.26 templateScale templateYLabel[2] templateYLabel[3] in the fourth templateXLabel[0] .
generated: In the fourth Quarter of 2019 , Facebook 's Revenue amounted to 21082 million U.S. dollars , up from 17652 million U.S. dollars in the preceding Quarter .  Facebook 's main Revenue source is advertising through Facebook sites and online products .  In 2019 , Facebook accounted for the majority of parent company Alphabet 's revenues with 113.26 million U.S. dollars in the fourth Quarter .

Example 190:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Payment type preference when shopping at fast food restaurants in the U.S. 2018
X_Axis['Response']: ['Credit_card', 'Debit_card', 'Cash']
Y_Axis['Share', 'of', 'respondents']: ['18', '44', '32']

gold: This statistic shows the preferred form of payment when shopping at fast food restaurants among consumers in the United States in 2018 . In the study it was found that 32 percent of consumers preferred to use cash when making purchases at fast food restaurants .
gold_template: This statistic shows the preferred form of templateTitle[0] templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] templateTitle[7] among consumers in the templateTitle[8] in templateTitleDate[0] . In the study it was found that templateYValue[last] templateScale of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the preferred templateTitle[3] templateTitle[4] for templateTitle[1] templateTitle[2] according to internet users in the templateTitle[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[1] templateScale of templateYLabel[1] most frequently used a templateXValue[1] templateXValue[0] to pay for templateTitle[1] purchases .
generated: This statistic shows the preferred when shopping for type preference according to internet users in the Payment in 2018 .  During the survey period , it was found that 44 % of respondents most frequently used a Debit_card Credit_card to pay for type purchases .

Example 191:
titleEntities: {'Subject': ['Easter U.S.'], 'Date': ['2019']}
title: Planned Easter expenditure per capita in the U.S. by item 2019
X_Axis['Month']: ['Food', 'Clothing', 'Gifts', 'Candy', 'Flowers', 'Decorations', 'Greeting_cards', 'Other']
Y_Axis['Average', 'expenditure', 'in', 'U.S.', 'dollars']: ['47.97$', '27.29$', '24.01$', '20.78$', '10.79$', '8.73$', '6.52$', '5.15$']

gold: This statistic shows the results of a survey among people in the United States on the amount of money they are planning to spend on the following items for the 2019 Easter holidays . Respondents stated that they are planning to spend an average of 20.78 U.S. dollars on candy for the upcoming Easter holidays .
gold_template: This statistic shows the results of a survey among people in the templateTitle[5] on the amount of money they are planning to spend on the following items for the templateTitleDate[0] templateTitleSubject[0] holidays . Respondents stated that they are planning to spend an templateYLabel[0] of 20.78 templateYLabel[2] templateYLabel[3] on templateXValue[3] for the upcoming templateTitleSubject[0] holidays .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[6] as of 2018 , broken down by templateXValue[2] . As of that year , around templateYValue[max] templateScale templateTitle[2] reporting templateTitle[3] were templateXValue[0] .
generated: This statistic shows the Planned Easter expenditure per in the by as of 2018 , broken down by Gifts .  As of that year , around 47.97$ % expenditure reporting per were Food .

Example 192:
titleEntities: {'Subject': ['GDP'], 'Date': ['2018']}
title: National debt of selected countries in relation to gross domestic product ( GDP ) 2018
X_Axis['Country']: ['Japan', 'United_States', 'France', 'Brazil', 'United_Kingdom', 'India', 'Germany', 'China', 'Russia']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'gross', 'domestic', 'product', '(GDP)']: ['237.69', '106.22', '99.31', '91.57', '85.55', '69.04', '58.58', '55.57', '16.49']

gold: This statistic shows the national debt of important industrial and emerging countries in 2019 in relation to the gross domestic product ( GDP ) . In 2019 , the national debt of China was at about 55.57 percent of the gross domestic product .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of important industrial and emerging templateTitle[3] in 2019 in templateYLabel[2] to the templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitleSubject[0] ) . In 2019 , the templateYLabel[0] templateYLabel[1] of templateXValue[7] was at about templateYValue[7] templateScale of the templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] of the global templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , broken down by templateXLabel[0] . According to the source , there were templateYValue[2] templateScale of all templateYLabel[2] .
generated: This statistic shows the National of the global GDP debt countries in 2018 , broken down by Country .  According to the source , there were 99.31 % of all relation .

Example 193:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': []}
title: Liver transplants in the United Kingdom ( UK ) 2018/19
X_Axis['Country', 'of', 'residence']: ['England', 'Scotland', 'Wales', 'Northern_Ireland']
Y_Axis['Number', 'of', 'transplants']: ['779', '114', '35', '34']

gold: In the period 2018/19 , 779 liver transplants were carried out in England , followed by 114 conducted in Scotland . England has by far the largest population of the countries in the United Kingdom , so it is unsurprising it has the highest number of transplants performed in a year . State of liver transplants in the UK The number of liver transplants in the United Kingdom in 2018/19 was an five percent increase from the number that took place in the preceding year .
gold_template: In the period templateTitle[5] , templateYValue[max] templateTitle[0] templateYLabel[1] were carried out in templateXValue[0] , followed by templateYValue[1] conducted in templateXValue[1] . templateXValue[0] has by far the largest population of the countries in the templateTitleSubject[0] , so it is unsurprising it has the highest templateYLabel[0] of templateYLabel[1] performed in a year . State of templateTitle[0] templateYLabel[1] in the templateTitleSubject[1] The templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitle[5] was an five templateScale templatePositiveTrend from the templateYLabel[0] that took place in the preceding year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , there were almost templateYValue[max] templateScale templateYLabel[1] during templateTitle[1] templateTitle[2] .
generated: This statistic shows the Liver transplants United Kingdom UK 2018/19 in the 2018/19 in , UK Country residence .  In , there were almost 779 million transplants during United .

Example 194:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest infant mortality rate 2017
X_Axis['Country']: ['Afghanistan', 'Somalia', 'Central_African_Republic', 'Guinea-Bissau', 'Chad', 'Niger', 'Burkina_Faso', 'Nigeria', 'Mali', 'Sierra_Leone', 'Democratic_Republic_of_Congo', 'Angola', 'Mozambique', 'Equatorial_Guinea', 'South_Sudan', 'Zambia', 'Gambia', 'Comoros', 'Burundi', 'Uganda']
Y_Axis['Child', 'deaths', 'in', 'the', 'first', 'year', 'of', 'life', 'per', '1,000', 'live', 'births']: ['110.6', '94.8', '86.3', '85.7', '85.4', '81.1', '72.2', '69.8', '69.5', '68.4', '68.2', '67.6', '65.9', '65.2', '62.8', '61.1', '60.2', '60.0', '58.8', '56.1']

gold: This statistic shows the 20 countries  with the highest infant mortality rate in 2017 . An estimated 110.6 infants per 1,000 live births died in the first year of life in Afghanistan in 2017 . Infant and child mortality Infant mortality usually refers to the death of children younger than one year .
gold_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . An estimated templateYValue[max] infants templateYLabel[5] 1,000 templateYLabel[7] templateYLabel[8] died in the templateYLabel[2] templateYLabel[3] of templateYLabel[4] in templateXValue[0] in templateTitleDate[0] . templateTitle[3] and templateYLabel[0] templateTitle[4] templateTitle[3] templateTitle[4] usually refers to the death of children younger than one templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateYLabel[2] templateTitle[3] templateTitle[4] as of 2020 . As of templateTitleDate[0] , templateXValue[0] had the templateTitle[2] templateXLabel[0] with a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Child of the Countries highest first mortality rate as of 2020 .  As of 2017 , Afghanistan had the infant Country with a Child of 110.6 deaths first .

Example 195:
titleEntities: {'Subject': ['Hollywood'], 'Date': ['2016']}
title: Stereotyping of ethnic minorities in Hollywood movies 2016
X_Axis['Response']: ['Do_a_good_job_of_portraying_racial_minorities', 'Give_into_stereotypes_when_portraying_racial_minorities', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['38', '37', '24']

gold: The survey shows result of survey on stereotyping of racial minorities in Hollywood movies in the United States as of February 2016 . Durign the survey , 38 of respondents stated Hollywood movies did a good job of potraying racial minorities .
gold_template: The survey shows result of survey on templateTitle[0] of templateXValue[0] in templateTitleSubject[0] templateTitle[4] in the country as of 2016 . Durign the survey , templateYValue[max] of templateYLabel[1] stated templateTitleSubject[0] templateTitle[4] did a templateXValue[0] of potraying templateXValue[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[7] in the templateTitle[6] as of October templateTitleDate[0] . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[0] .
generated: The statistic shows the Stereotyping ethnic minorities Hollywood movies 2016 in the 2016 as of October 2016 .  During the survey , 38 % of the respondents stated that they used Do_a_good_job_of_portraying_racial_minorities .

Example 196:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2018']}
title: Total number of U.S. children enrolled in pre-K , by state 2017 to 2018
X_Axis['State']: ['United_States_', 'California', 'Texas', 'Florida', 'New_York', 'Georgia', 'Illinois', 'New_Jersey', 'Wisconsin', 'Oklahoma', 'Michigan', 'Massachusetts', 'Maryland', 'Pennsylvania', 'North_Carolina', 'South_Carolina', 'Iowa', 'Kentucky', 'Colorado', 'Arkansas', 'Louisiana', 'Tennessee', 'Virginia', 'Ohio', 'Alabama', 'West_Virginia', 'Connecticut', 'Kansas', 'District_of_Columbia', 'Nebraska', 'Washington', 'Oregon', 'New_Mexico', 'Vermont', 'Minnesota', 'Maine', 'Arizona', 'Missouri', 'Nevada', 'Mississippi', 'Rhode_Island', 'North_Dakota', 'Delaware', 'Hawaii', 'Alaska', 'Montana', 'Guam']
Y_Axis['Number', 'of', 'children', 'enrolled']: ['1565168', '241859', '231485', '173645', '121572', '80536', '74940', '50684', '46736', '39807', '37325', '34130', '31162', '29710', '28385', '27443', '27195', '21270', '21037', '19498', '18911', '18354', '17959', '17913', '16051', '14629', '14449', '14022', '13332', '12950', '12491', '9464', '9119', '8449', '7672', '5551', '5256', '2378', '2102', '1840', '1080', '965', '845', '373', '315', '279', '71']

gold: The statistic above provides information on the number of the 3- and 4-year-old children enrolled in pre-kindergarten programs in the United States for the 2017/2018 school year , by state . Between 2017 and 2018 , about 50,684 children in New Jersey were enrolled in pre-K programs .
gold_template: The statistic above provides information on the templateYLabel[0] of the 3- and 4-year-old templateYLabel[1] templateYLabel[2] in pre-kindergarten programs in the templateXValue[0] for the 2017/2018 school year , templateTitle[6] templateXLabel[0] . Between templateTitleDate[min] and templateTitleDate[max] , about templateYValue[7] templateYLabel[1] in templateXValue[4] templateXValue[7] were templateYLabel[2] in templateTitle[5] programs .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2020 . As of templateYLabel[0] fourth templateTitle[7] templateTitleDate[0] , had the highest of templateTitleSubject[0] templateTitle[6] templateTitle[7] templateXLabel[0] .
generated: This statistic shows the Number of U.S. number children in the enrolled as of 2020 .  As of Number fourth state 2017 , had the highest of U.S. by state .

Example 197:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: UK : real estate prime office rent prices in selected cities Q3 2019
X_Axis['City']: ['Reading', 'Manchester', 'Bristol', 'Edinburgh', 'Birmingham', 'Glasgow', 'Leeds', 'Cardiff', 'Newcastle']
Y_Axis['Price', 'per', 'square', 'meter', 'in', 'euros']: ['468', '444', '438', '425', '413', '389', '365', '304', '298']

gold: This statistic displays the most expensive cities for prime office rents in the United Kingdom ( UK ) as of September 2019 , excluding London . As of September 2019 , it can be seen that Reading was the most expensive location within the UK for prime office rents outside of London , with an average price reaching 468 euros per square meter per year . This was followed by Manchester , Bristol and Edinburgh .
gold_template: This statistic displays the most expensive templateTitle[8] for templateTitle[3] templateTitle[4] rents in the United Kingdom ( templateTitleSubject[0] ) as of 2019 , excluding London . As of 2019 , it can be seen that templateXValue[0] was the most expensive location within the templateTitleSubject[0] for templateTitle[3] templateTitle[4] rents outside of London , with an average templateYLabel[0] reaching templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . This was followed by templateXValue[1] , templateXValue[2] and templateXValue[3] .

generated_template: This graph shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[6] as of 2019 , templateTitle[8] templateXLabel[0] . As of that point , templateTitleSubject[0] had a total of templateYValue[min] templateScale of templateXValue[last] American templateXLabel[0] templateXLabel[1] .
generated: This graph shows the Price of the UK real estate prime in the prices as of 2019 , cities City .  As of that point , UK had a total of 298 % of Newcastle American City .

Example 198:
titleEntities: {'Subject': ['Worlds'], 'Date': ['2018']}
title: Worlds ' most dangerous cities , by murder rate 2018
X_Axis['City']: ['Tijuana_-_Mexico', 'Acapulco_-_Mexico', 'Caracas_-_Venezuela', 'Ciudad_Victoria_-_Mexico', 'Ciudad_Juarez_-_Mexico', 'Irapuato_-_Mexico', 'Ciudad_Guayana_-_Venezuela', 'Natal_-_Brazil', 'Fortaleza_-_Brazil', 'Ciudad_Bolivar_-_Venezuela', 'Cape_Town_-_South_Africa', 'Belem_-_Brazil', 'Cancun_-_Mexico', 'Feira_de_Santana_-_Brazil', 'St._Louis_Missouri_-_U.S.', 'Culiacan_-_Mexico', 'Barquisimeto_-_Venezuela', 'Uruapan_-_Mexico', 'Kingston_-_Jamaica', 'Ciudad_Obregón_-_Mexico', 'Maceio_-_Brazil', 'Vitoria_da_Conquista_-_Brazil', 'Baltimore_Maryland_-_U.S.', 'San_Salvador_-_El_Salvador', 'Aracaju_-_Brazil', 'Coatzacoalcos_-_Mexico', 'Palmira_-_Colombia', 'Maturin_-_Venezuela', 'Salvador_-_Brazil', 'Macapa_-_Brazil', 'Cali_-_Colombia', 'Celaya_-_Mexico', 'San_Pedro_Sula_-_Honduras', 'Ensenada_-_Mexico', 'Campos_dos_Goytacazes_-_Brazil', 'Tepic_-_Mexico', 'Manaus_-_Brazil', 'Guatemala_City_-_Guatemala', 'Recife_-_Brazil', 'Distrito_Central_-_Honduras', 'San_Juan_-_Puerto_Rico', 'Valencia_-_Venezuela', 'Reynosa_-_Mexico', 'João_Pessoa_-_Brazil', 'Nelson_Mandela_Bay_-_South_Africa', 'Detroit_Michigan_-_U.S.', 'Durban_-_South_Africa', 'Teresina_-_Brazil', 'Chihuahua_-_Mexico', 'New_Orleans_Louisiana_-_U.S.']
Y_Axis['Murder', 'rate', 'per', '100,000', 'inhabitants']: ['138.26', '110.5', '99.98', '86.01', '85.56', '81.44', '78.3', '74.67', '69.15', '69.09', '66.36', '65.31', '64.46', '63.29', '60.59', '60.52', '56.67', '54.52', '54.12', '52.09', '51.46', '50.75', '50.52', '50.32', '48.77', '48.35', '47.97', '47.24', '47.23', '47.2', '47.03', '46.99', '46.67', '46.6', '46.28', '44.89', '44.0', '43.73', '43.72', '43.3', '42.4', '42.36', '41.48', '41.36', '39.16', '38.78', '38.51', '37.61', '37.5', '36.87']

gold: This statistic ranks the 50 most dangerous cities of 2018 , by murder rate per 100,000 inhabitants . Tijuana 's murder rate was 138.26 for every 100,000 people living in the city . The world 's most dangerous cities The Citizens ' Council for Public Security and Criminal Justice published a ranking of the world 's most dangerous cities in 2018 , ranking cities according to the number of murders per 100,000 inhabitants that year .
gold_template: This statistic ranks the templateYValue[23] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] , templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] 100,000 templateYLabel[4] . templateXValue[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[max] for every 100,000 people living in the templateXValue[37] . The world 's templateTitle[2] templateTitle[3] templateTitle[4] The Citizens templateTitle[1] Council for Public Security and Criminal Justice published a ranking of the world 's templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , ranking templateTitle[4] according to the number of murders templateYLabel[2] 100,000 templateYLabel[4] that year .

generated_template: This graph shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[6] as of 2020 . In that year , templateTitle[0] templateTitle[1] had a templateTitle[6] of templateYValue[max] templateScale of female templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This graph shows the Murder of the Worlds ' most dangerous in the murder as of 2020 .  In that year , Worlds ' had a murder of 138.26 % of female rate per 100,000 .

Example 199:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading internet traffic categories worldwide 2018
X_Axis['Category']: ['Video', 'Web', 'Gaming', 'Social_media', 'Content_marketplaces', 'File_sharing', 'Audio_streaming']
Y_Axis['Share', 'of', 'downstream', 'internet', 'traffic']: ['57.7', '17', '7.8', '5.1', '4.6', '2.8', '1']

gold: This statistic presents the distribution of global downstream internet traffic as of October 2018 , by category . During the measured period , video accounted for over half of downstream internet traffic volume . Within that category , Netflix was by far the market leader in terms of global video traffic .
gold_template: This statistic presents the distribution of global templateYLabel[1] templateYLabel[2] templateYLabel[3] as of October templateTitleDate[0] , by templateXLabel[0] . During the measured period , templateXValue[0] accounted for over half of templateYLabel[1] templateYLabel[2] templateYLabel[3] volume . Within that templateXLabel[0] , Netflix was by far the market leader in terms of global templateXValue[0] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2020 . As of the measured period , templateYValue[max] templateScale of templateXValue[0] respondents stated they used templateTitle[0] templateTitle[1] .
generated: This statistic shows the Leading internet traffic categories worldwide 2018 in the 2018 as of 2020 .  As of the measured period , 57.7 % of Video respondents stated they used Leading internet .

Example 200:
titleEntities: {'Subject': ['Golf-Association'], 'Date': ['2012', '2013']}
title: Golf-Association executives ' compensation 2012 to 2013
X_Axis['Month']: ['Tim_Finchem_(PGA_Tour_commissioner_&_CEO)_2013', 'Joe_Steranka_(Former_of_America_CEO)_2013', 'Dick_Rugge_(Former_USGA_senior_director_equipment_standards)_2012', 'Tom_Wade_(PGA_Tour_global_commercial_officer)_2013', 'Charles_Zink_(PGA_Tour_co-chief_operating_officer)_2013', 'Ed_Moorhouse_(PGA_Tour_co-chief_operating_officer)_2013', 'Ron_Price_(PGA_Tour_executive_VP_CFO)_2013', 'David_Pillsbury_(PGA_Tour_executive_VP_championship_managment_&_tournament_business_affairs)_2013', 'Mike_Whan_(LPGA_Tour_commissioner)_2012', 'Ty_Votaw_(PGA_Tour_executive_VP_&_chief_of_global_communications)_2013', 'Mike_Davis_(USGA_executive_director)_2012', 'Joseph_Monahan_(PGA_Tour_executive_VP_&_chief_marketing_officer)_2013', 'David_Fay_(Former_USGA_executive_director)_2012', 'Michael_Butz_(USGA_senior_managing_director_Open_championships_&_association_relations)', 'Joe_Louis_Barrow_Jr._(World_Golf_Foundation_executive_VP_The_First_Tee_CEO)_2013', 'Bill_Calfee_(PGA_Tour_president_Web.com_Tour)_2013', 'Darrell_Crall_(PGA_of_America_COO)_2013', 'Kerry_Haigh_(PGA_of_America_chief_championships_officer)_2013', 'Rick_Anderson_(PGA_Tour_executive_VP_television_and_digital)_2013', 'James_Pazder_(PGA_Tour_executive_VP_&_chief_of_operations)_2013', 'Mike_Stevens_(PGA_Tour_president_Champions_Tour)_2013', 'Stephen_Mona_(World_Golf_Foundation_CEO)_2013', 'Mark_Russell_(PGA_Tour_VP_rules_and_competitions)_2013', 'Stephen_Hamblin_(American_Junior_Golf_Assosiation_executive_director)_2012', 'Joseph_Beditz_(National_Golf_Foundation_president/CEO)_2012']
Y_Axis['Compensations', 'in', 'million', 'U.S.', 'dollars']: ['4.58', '2.59', '1.8', '1.17', '1.16', '1.13', '1.06', '0.97', '0.89', '0.79', '0.77', '0.73', '0.65', '0.64', '0.62', '0.55', '0.54', '0.54', '0.54', '0.51', '0.49', '0.45', '0.45', '0.43', '0.25']

gold: The graph depicts the earnings of 25 golf association executives in 2012 and 2013 . Tim Finchem , PGA Tour commissioner and CEO , tops the earnings with an amount of 4.58 million U.S. dollars .
gold_template: The graph depicts the earnings of 25 templateXValue[14] templateXValue[13] templateTitle[1] in templateXValue[2] and templateXValue[0] . templateXValue[0] , PGA templateXValue[0] and CEO , tops the earnings with an amount of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of times templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] as of 2018 . As of that templateXLabel[0] , the website had an average of templateYValue[0] templateScale templateYLabel[2] .
generated: This statistic shows the Compensations of times Golf-Association executives ' compensation in Golf-Association as of 2018 .  As of that Month , the website had an average of 4.58 million U.S. .

Example 201:
titleEntities: {'Subject': ['Piracy'], 'Date': ['2019']}
title: Piracy - actual and attempted attacks worldwide by country 2019
X_Axis['Country']: ['Nigeria', 'Indonesia', 'Singapore_Staits', 'Malaysia', 'Peru', 'Venezuela', 'Cameroon']
Y_Axis['Number', 'of', 'incidents']: ['35', '25', '12', '11', '10', '6', '6']

gold: The statistic represents the total number of actual and attempted piracy attacks in the world 's most perilous territorial waters in 2019 . That year , there were six actual and attempted piracy attacks off the Venezuelan coast .
gold_template: The statistic represents the total templateYLabel[0] of templateTitle[1] and templateTitle[2] templateTitleSubject[0] templateTitle[3] in the world 's most perilous territorial waters in templateTitleDate[0] . That year , there were templateYValue[min] templateTitle[1] and templateTitle[2] templateTitleSubject[0] templateTitle[3] off the Venezuelan coast .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] in selected countries in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , there were templateYValue[max] templateTitle[1] in templateXValue[0] .
generated: This statistic shows the Number of Piracy incidents in selected countries in 2019 , worldwide Country .  In 2019 , there were 35 actual in Nigeria .

Example 202:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. women who have given oral sex to a male in their lifetime , by age group
X_Axis['Age', 'group']: ['14-15', '16-17', '18-19', '20-24', '25-29', '30-39', '40-49', '50-59', '60-69', '70_and_older']
Y_Axis['Share', 'of', 'respondents']: ['13', '29', '61', '78', '89', '80', '83', '80', '73', '43']

gold: This statistic shows the share of American women who have ever given oral sex to a male in their lifetime , sorted by age group . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral sex to a male at some time during their life .
gold_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] in templateTitle[8] templateTitle[9] , sorted templateTitle[10] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] templateScale of templateYLabel[1] aged 25 to templateYValue[1] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] at some time during templateTitle[8] life .

generated_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] at some point during templateTitle[7] templateTitle[8] , sorted templateTitle[9] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] templateScale of templateYLabel[1] aged 25 to 29 stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] at some point during templateTitle[7] life .
generated: This statistic shows the Share of American women who have given oral sex at some point during male their , sorted lifetime Age group .  The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the U.S. , in 2010 .  89 % of respondents aged 25 to 29 stated they have given oral sex at some point during male life .

Example 203:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Millionaire households number in Europe in 2014 , by country
X_Axis['Country']: ['Germany', 'France', 'Italy', 'United_Kingdom', 'Netherlands', 'Switzerland', 'Belgium', 'Austria', 'Spain', 'Luxembourg', 'Portugal', 'Greece', 'Finland', 'Slovakia', 'Cyprus', 'Slovenia']
Y_Axis['Number', 'of', 'millionaire', 'households']: ['1433985', '1334066', '818538', '796646', '703108', '555483', '415117', '200298', '168134', '50612', '46416', '34723', '25995', '9532', '7269', '6784']

gold: The statistic displays the number of households that own net private wealth of at least one million euros in Europe as of 2014 . The countries with the largest number of millionaire households include Germany ( 1.4 million of ultra-rich households ) and France ( 1.3 million households ) .
gold_template: The statistic displays the templateYLabel[0] of templateYLabel[2] that own net private wealth of at least one templateScale euros in templateTitleSubject[0] as of templateTitleDate[0] . The countries with the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] include templateXValue[0] ( 1.4 templateScale of ultra-rich templateYLabel[2] ) and templateXValue[1] ( 1.3 templateScale templateYLabel[2] ) .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] as of each templateXLabel[0] . As of templateTitleDate[0] , templateXValue[0] had the highest templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] templateTitle[6] templateTitle[7] .
generated: This statistic shows the Number of Europe Millionaire households number Europe as of each Country .  As of 2014 , Germany had the highest Millionaire households number of Europe country .

Example 204:
titleEntities: {'Subject': ['Kickstarter'], 'Date': ['2019']}
title: Distribution of Kickstarter funding amounts raised 2019
X_Axis['Money', 'raised', '(in', 'U.S.', 'dollars)']: ['Less_than_1000', '1000_to_9999', '10000_to_19999', '20000_to_99999', '100K_to_999999', 'More_than_1M+']
Y_Axis['Number', 'of', 'projects']: ['21945', '92970', '24579', '24804', '6063', '385']

gold: The statistic shows the number of successfully funded projects on the crowdfunding platform Kickstarter as of October 2 , 2018 . It shows the number of total successfully funded projects by funds raised . As of that time , the number of successfully funded projects at Kickstarter which raised more than one million U.S. dollars amounted to 385 projects .
gold_template: The statistic shows the templateYLabel[0] of successfully funded templateYLabel[1] on the crowdfunding platform templateTitleSubject[0] as of October 2 , 2018 . It shows the templateYLabel[0] of total successfully funded templateYLabel[1] by funds templateXLabel[1] . As of that time , the templateYLabel[0] of successfully funded templateYLabel[1] at templateTitleSubject[0] which templateXLabel[1] templateXValue[last] templateXValue[0] one templateScale templateXLabel[3] dollars amounted to templateYValue[min] templateYLabel[1] .

generated_template: The graph shows the templateTitle[0] templateTitle[6] of the templateTitle[6] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , it was found that templateXValue[0] had templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The graph shows the Distribution 2019 of the 2019 of Distribution Kickstarter funding raised in the Kickstarter in 2019 .  In 2019 , it was found that Less_than_1000 had 92970 million projects .

Example 205:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2019']}
title: Facebook : number of followers of popular luxury brands 2019
X_Axis['Designer', 'Brand']: ['Louis_Vuitton', 'Chanel', 'Gucci', 'Michael_Kors', 'Burberry', 'Dior', 'Dolce_&_Gabbana', 'Ralph_Lauren', 'Armani', 'Coach', 'Prada', 'Versace', 'Jimmy_Choo', 'Christian_Louboutin', 'Hermès']
Y_Axis['Followers', 'in', 'millions']: ['23.28', '21.96', '18.2', '17.94', '17.31', '16.65', '11.74', '9.16', '8.63', '7.36', '6.6', '5.37', '3.71', '3.35', '3.13']

gold: This statistic provides information on the leading luxury brands with the most followers on Facebook as of May 2019 , ranked by number of followers . According to the findings , the luxury brand Louis Vuitton had recorded in a total of 23.28 million likes on Facebook , and ranking second was Chanel with 21.96 million page likes .
gold_template: This statistic provides information on the leading templateTitle[4] templateTitle[5] with the most templateYLabel[0] on templateTitleSubject[0] as of 2019 , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] had recorded in a total of templateYValue[max] templateScale likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateScale page likes .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] as of 2015 . During the survey period , it was found that templateYValue[4] templateScale of the templateXValue[last] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the Followers of millions number followers popular luxury in the brands 2019 as of 2015 .  During the survey period , it was found that 17.31 millions of the Hermès millions .

Example 206:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Leading prescriptions dispensed in the U.S. diabetes market 2014
X_Axis['Medicine']: ['Metformin_HCI', 'Glimepiride', 'Metformin_ER_(G)', 'Glipizide', 'Lantus_(long-acting_insulin)', 'Lantus_SoloStar_(long-acting_insulin)', 'Januvia_(sitagliptin)', 'Glipizide_ER', 'Glyburide', 'Pioglitazone']
Y_Axis['Rx', 'dispensed', 'in', 'million', 'units']: ['59.2', '12.7', '12.5', '10.4', '9.6', '9.5', '8.8', '7.1', '6.5', '5.5']

gold: The statistic shows the leading prescriptions dispensed in the U.S. diabetes market in 2014 . In that year , Metformin HCI was the leading diabetes prescription dispensed in the United States at 59.2 million units .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[4] prescription templateYLabel[1] in the templateTitle[3] at templateYValue[max] templateScale templateYLabel[3] .

generated_template: In templateTitleDate[0] , there were a total of 303 templateTitleSubject[0] templateTitle[1] in the templateTitle[6] , with a pipe . templateXValue[1] had a templateYLabel[0] of templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[4] templateScale during templateTitleDate[0] . In 2018 , templateXValue[0] had a fourth templateXLabel[0] templateXLabel[1] of around earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned them to advance to finally return to their pre-recessionary levels in templateXLabel[0] templateXLabel[1] at Since Since a spouse or a last few years that has been ongoing for many years . What is a Japanese subsidiary of those those those those those those those which is a number of those those belonging to date of those those classed as industrial industrial industrial industrial industrial industrial industrial industrial industrial enterprises on the fourth quarter of those those those classed as those those those those those those those classed as those classed as those those who earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned earned
generated: In 2014 , there were a total of 303 U.S. prescriptions in the 2014 , with a pipe .  Glimepiride had a Rx of 12.7 dispensed million units of 9.6 million during 2014 .  In 2018 , Metformin_HCI had a fourth Medicine of around earned them to advance finally return to their pre-recessionary levels in Medicine at Since a spouse or a last few years that has been ongoing for many years .  What is a Japanese subsidiary of those which is a number of those belonging to date of those classed as industrial enterprises on the fourth quarter of those classed as those classed as those classed as those who earned . 

Example 207:
titleEntities: {'Subject': ['Photo'], 'Date': ['2013']}
title: Photo sharing sites : daily upload market share 2013
X_Axis['Platform']: ['Snapchat', 'Facebook', 'Instagram', 'Flickr']
Y_Axis['Share', 'of', 'uploads']: ['49', '43', '7', '1']

gold: This statistic presents the four most popular photo sharing sites as of November 2013 , sorted by share of daily photo uploads . During that month , Instagram accounted for seven percent of daily photo uploads .
gold_template: This statistic presents the four most popular templateTitleSubject[0] templateTitle[1] templateTitle[2] as of 2013 , sorted by templateYLabel[0] of templateTitle[3] templateTitleSubject[0] templateYLabel[1] . During that month , templateXValue[2] accounted for templateYValue[2] templateScale of templateTitle[3] templateTitleSubject[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of users of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[max] templateScale of responding adults were templateTitle[1] templateTitle[2] templateXLabel[0] .
generated: This statistic shows the Share of users the Photo sharing sites daily in Photo 2013 .  During the survey period , it was found that 49 % of responding adults were sharing sites Platform .

Example 208:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: Leading food and beverage retailers of Europe 2017 , based on revenue
X_Axis['Company', '(Country', 'of', 'origin)']: ['Schwarz_Unternehmenstreuhand_KG_(Germany)', 'Aldi_Einkauf_GmbH_&_Co._oHG_(Germany)', 'Tesco_PLC_(UK)', 'Ahold_Delhaize_(formerly_Koninklijke_Ahold_N.V._and_Delhaize_Group_SA_[Netherlands])', 'Auchan_Holding_SA_(France)', 'Edeka_Group_(Germany)', 'Rewe_Combine_(Germany)', 'Casino_Guichard-Perrachon_S.A._(France)', 'Centres_Distributeurs_E._Leclerc_(France)_', 'Metro_AG_(Germany)', 'The_IKEA_Group_(INGKA_Holding_B.V.)_(Netherlands)', 'J_Sainsbury_plc_(UK)', 'LVMH_Moët_Hennessy-Louis_Vuitton_S.A._(France)', 'ITM_Developpement_International_(Intermarche;_France)_', 'Inditex_S.A._(Spain)', 'Migros-Genossenschafts_Bund_(Switzerland)_', 'Ceconomy_AG_(Germany)', 'Mercadona_SA_(Spain)', 'Coop_Group_(Switzerland)_', 'Wm_Morrison_Supermarkets_PLC_(UK)']
Y_Axis['Billion', 'U.S.', 'dollars']: ['111.77', '98.29', '73.96', '72.31', '58.61', '57.48', '49.71', '42.6', '41.54', '40.96', '37.43', '36.6', '33.29', '31.85', '28.89', '24.53', '24.43', '23.68', '22.52', '22.43']

gold: In 2018 , the German based Schwarz Gruppe was the leading food and beverage retailer from Europe and generated 111.77 billion U.S. dollars in revenue . The second largest retailer was also German . Aldi Einkauf GmbH & Ko .
gold_template: In 2018 , the German templateTitle[6] templateXValue[0] Gruppe was the templateTitle[0] templateTitle[1] and templateTitle[2] retailer from templateTitleSubject[0] and generated templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateTitle[7] . The second largest retailer was also German . templateXValue[1] GmbH templateXValue[1] Ko .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] on retail templateTitle[6] . In that year , templateXValue[0] was templateTitleSubject[0] 's templateTitle[0] retailer with about templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] worth of sales.Retail in templateTitleSubject[0] AmericaAs in 2015 .
generated: This statistic shows the Leading food of Europe in 2017 , on retail based .  In that year , Schwarz_Unternehmenstreuhand_KG_(Germany) was Europe 's Leading retailer with about 111.77 billion U.S. dollars worth of sales.Retail in Europe AmericaAs in 2015 .

Example 209:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Wealth per adult on average in Europe in 2014 , by country
X_Axis['Country']: ['Luxembourg', 'Switzerland', 'Belgium', 'Netherlands', 'Austria', 'Germany', 'United_Kingdom', 'France', 'Italy', 'Cyprus', 'Finland', 'Spain', 'Portugal', 'Slovenia', 'Greece', 'Slovakia']
Y_Axis['Average', 'wealth', 'per', 'adult']: ['432221', '394917', '240928', '213365', '188552', '185857', '183325', '178862', '163493', '137298', '124285', '92341', '84847', '67878', '58877', '33295']

gold: The statistic displays the average value of wealth per adult in selected European countries as of 2014 . The average value of wealth per adult in Luxembourg amounted to 432.2 thousand euros , while in the United Kingdom ( UK ) it reached approximately 188.6 thousand euros .
gold_template: The statistic displays the templateYLabel[0] value of templateYLabel[1] templateYLabel[2] templateYLabel[3] in selected European countries as of templateTitleDate[0] . The templateYLabel[0] value of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[0] amounted to templateYValue[max] thousand euros , while in the templateXValue[6] ( UK ) it reached approximately templateYValue[4] thousand euros .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] as of each templateXLabel[0] . As of templateTitleDate[0] , templateXValue[0] had the highest templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] templateTitle[6] templateTitle[7] .
generated: This statistic shows the Average of Europe Wealth per adult average as of each Country .  As of 2014 , Luxembourg had the highest Wealth per adult of Europe by country .

Example 210:
titleEntities: {'Subject': ['Engie'], 'Date': ['2018']}
title: Engie - revenue by region 2018
X_Axis['Country']: ['France', 'Other_EU_countries', 'Belgium', 'Asia_Middle_East_and_Oceania', 'South_America', 'North_America', 'Other_European_countries', 'Africa']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['24.98', '15.45', '5.96', '4.94', '4.2', '3.87', '0.82', '0.39']

gold: This statistic represents Engie 's revenue in the fiscal year of 2018 , by region . The French multinational energy company generated a revenue of around six billion euros in its Belgium segment . The company was formed by the merger of Gaz de France and Suez to GDF Suez and officially changed its name to Engie in April 2015 .
gold_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] in the fiscal year of templateTitleDate[0] , templateTitle[2] templateTitle[3] . The French multinational energy company generated a templateYLabel[0] of around templateYValue[2] templateScale templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitleSubject[0] in 2015 .

generated_template: The statistic shows a ranking of key templateTitle[1] templateTitle[2] in selected countries in templateTitleDate[0] , with the highest rate of templateTitleSubject[0] templateTitle[1] worldwide . The majority of templateXValue[0] , with around templateYValue[max] templateScale templateYLabel[2] .
generated: The statistic shows a ranking of key revenue by in selected countries in 2018 , with the highest rate of Engie revenue worldwide .  The majority of France , with around 24.98 billion euros .

Example 211:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2014']}
title: Reasons for unreported vandalism against businesses in England and Wales 2014
X_Axis['Response']: ['Trivial/no_loss', 'Lack_of_police_engagement', 'Private/dealt_with_ourselves', 'Lack_of_evidence', 'Reported_to_other_authorities', 'Inconvenient_to_report', 'Police_came', 'Common_occurrence', 'Fear_of_reprisal', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['43', '40', '13', '3', '3', '3', '3', '2', '1', '6']

gold: This survey shows the reasons businesses gave for not reporting cases of vandalism on their premises to the police in England and Wales in 2014 . Of respondents , 43 percent claimed they did not report the incident as there was no loss or damage or the crime was too trivial to report to the police .
gold_template: This survey shows the templateTitle[0] templateTitle[5] gave templateTitle[1] not reporting cases of templateTitle[3] on their premises to the templateXValue[1] in templateTitleSubject[0] and templateTitleSubject[1] in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[max] templateScale claimed they did not templateXValue[5] the incident as there was no templateXValue[0] or damage or the crime was too trivial to templateXValue[5] to the templateXValue[1] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] templateTitle[3] as of 2018 . templateYValue[2] templateScale of templateYLabel[1] stated templateXValue[2] was currently the templateTitle[0] templateXLabel[0] in templateTitleSubject[0] templateTitle[3] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Reasons nation in England vandalism as of 2018 .  13 % of respondents stated Private/dealt_with_ourselves was currently the Reasons Response in England vandalism .

Example 212:
titleEntities: {'Subject': ['Subaru', 'United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Subaru car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['708', '157', '88', '526', '52', '103', '244', '138', '101', '632', '74', '174', '193', '145', '135', '829', '100', '112', '265', '231', '153', '761', '62', '155', '246', '216', '99', '510', '44', '152', '202', '155', '123', '706', '48', '178', '330', '219', '256', '762', '69', '148']

gold: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[5] new templateTitleSubject[0] cars had been templateYLabel[1] .
generated: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between 2016 and 2019 .  Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months .  In 2019 , 103 new Subaru cars had been sold .

Example 213:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries ranked by number of ice hockey players 2018/19
X_Axis['Country']: ['Canada', 'United_States', 'Czech_Republic', 'Russia', 'Finland', 'Sweden', 'Switzerland', 'France', 'Germany', 'Japan', 'Slovakia', 'Norway', 'Great_Britain', 'Austria', 'Hungary', 'Latvia', 'Kazakhstan', 'Ukraine', 'Italy', 'Belarus']
Y_Axis['Number', 'of', 'players']: ['621026', '567908', '121613', '112236', '64641', '55431', '27867', '21667', '21340', '18837', '11394', '10353', '8162', '7670', '7106', '7000', '6915', '5384', '5210', '4580']

gold: The statistics ranks countries by the number of registered ice hockey players in 2018/19 . In the 2018/19 season , Canada had the most registered ice hockey players with 621 thousand according to the International Ice Hockey Federation .
gold_template: The statistics ranks templateTitleSubject[0] templateTitle[2] the templateYLabel[0] of registered templateTitle[4] templateTitle[5] templateYLabel[1] in templateTitle[7] . In the templateTitle[7] season , templateXValue[0] had the most registered templateTitle[4] templateTitle[5] templateYLabel[1] with templateYValue[max] thousand according to the International templateTitle[4] templateTitle[5] Federation .

generated_template: This graph shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitleDate[0] . During the measured period , templateXValue[0] had the highest rate of any templateXLabel[0] .
generated: This graph shows the Number of Countries ranked by number in the ice as of .  During the measured period , Canada had the highest rate of any Country .

Example 214:
titleEntities: {'Subject': ['Frequency'], 'Date': ['2014', '2014']}
title: Frequency of making online restaurant reservations in the U.S. as of June 2014
X_Axis['Response']: ['Yes_many_times', 'Yes_once_or_twice', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['9.6', '37.6', '52.8']

gold: This statistic shows the frequency with which consumers made online reservations when dining out in restaurants in the United States as of June 2014 . During the survey , 37.6 percent of respondents said they had made online reservations once or twice .
gold_template: This statistic shows the templateTitleSubject[0] with which consumers made templateTitle[2] templateTitle[4] when dining out in restaurants in the templateTitle[5] as of templateTitle[6] templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] said they had made templateTitle[2] templateTitle[4] templateXValue[1] or templateXValue[1] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] in the templateTitle[6] as of 2018 . During the survey , templateYValue[1] templateScale of templateTitleSubject[0] stated that they used templateXValue[0] for templateXValue[2] .
generated: This statistic presents the Frequency making online restaurant of Frequency in the June as of 2018 .  During the survey , 37.6 % of Frequency stated that they used Yes_many_times for Never .

Example 215:
titleEntities: {'Subject': ['Overwatch'], 'Date': ['2018']}
title: Number of Overwatch players worldwide 2018
X_Axis['Month']: ['May_2018', 'October_2017', 'April_2017', 'January_2017', 'October_2016', 'August_2016', 'May_2016']
Y_Axis['Number', 'of', 'players', 'in', 'millions']: ['40', '35', '30', '25', '20', '15', '7']

gold: How many people play Overwatch ? Overwatch , a team-based first-person shooter video game , launched in May 2016 and already a week later it was reported to have had seven million players . As of May 2018 , Overwatch had 40 million players worldwide . Overwatch 's eSports success While the number of gamers playing Overwatch has increased dramatically , so has the appeal of the game as an eSport .
gold_template: How many people play templateTitleSubject[0] ? templateTitleSubject[0] , a team-based first-person shooter video game , launched in templateXValue[0] templateXValue[4] and already a week later it was reported to have had templateYValue[min] templateScale templateYLabel[1] . As of templateXValue[0] , templateTitleSubject[0] had templateYValue[max] templateScale templateYLabel[1] templateTitle[3] . templateTitleSubject[0] 's eSports success While the templateYLabel[0] of gamers playing templateTitleSubject[0] has templatePositiveTrend dramatically , so has the appeal of the game as an eSport .

generated_template: templateTitleSubject[0] is a free-to-play online collectible card game which reached a milestone templateYValue[max] templateScale templateYLabel[1] templateTitle[5] in templateXValue[4] templateXValue[last] , thereby doubling the templateYLabel[0] of templateYLabel[1] since templateXValue[5] . The game originally bore the subtitle `` templateTitleSubject[0] of templateTitleSubject[0] '' as it is based in the same universe as Blizzard 's extremely popular templateTitleSubject[0] series . templateTitleSubject[0] still going strong after templateYValue[2] years templateTitleSubject[0] is seen as one of the jewels in the crown of Blizzard Entertainment .
generated: Overwatch is a free-to-play online collectible card game which reached a milestone 40 millions players 2018 in October_2016 May_2016 , thereby doubling the Number of players since August_2016 .  The game originally bore the subtitle `` Overwatch of '' as it is based in the same universe as Blizzard 's extremely popular Overwatch series .  Overwatch still going strong after 30 years Overwatch is seen as one of the jewels in the crown of Blizzard Entertainment .

Example 216:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Production of copper in Canada by province 2018
X_Axis['Month']: ['Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Nova_Scotia', 'New_Brunswick', 'Quebec', 'Ontario', 'Manitoba', 'Saskatchewan', 'Alberta', 'British_Columbia', 'Yukon', 'Northwest_Territories', 'Nunavut']
Y_Axis['Production', 'in', 'metric', 'tons']: ['27456', '0', '0', '487', '35912', '135297', '33608', '0', '0', '293468', '9282', '0', '0']

gold: This statistic displays preliminary estimates of the copper production in Canada , distributed by province , in 2018 . During that year , Quebec produced some 35,912 metric tons of this mineral . Copper is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .
gold_template: This statistic displays preliminary estimates of the templateTitle[1] templateYLabel[0] in templateTitleSubject[0] , distributed templateTitle[3] templateTitle[4] , in templateTitleDate[0] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[1] templateYLabel[2] of this mineral . templateTitle[1] is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[7] . In that year , there were a total of around templateYValue[max] templateScale templateTitle[1] templateYLabel[1] .
generated: This statistic shows the Production of copper metric in Canada 2018 , broken down 2018 .  In that year , there were a total of around 293468 million copper metric .

Example 217:
titleEntities: {'Subject': ['Number'], 'Date': ['2014']}
title: Number of crowdfunding platforms worldwide 2014 , by region
X_Axis['Country']: ['Europe', 'North_America', 'Asia', 'South_America', 'Oceania', 'Africa']
Y_Axis['Number', 'of', 'CFPs']: ['600', '375', '169', '50', '37', '19']

gold: The statistic shows the number of crowdfunding platforms worldwide in 2014 , by region . In that year , there were 375 crowdfunding platforms in North America . Crowdfunding is a way of collecting money from various individuals interested in a given project .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In that year , there were templateYValue[1] templateTitle[1] templateTitle[2] in templateXValue[1] . templateTitle[1] is a way of collecting money from various individuals interested in a given project .

generated_template: This statistic shows the templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] as of templateTitleDate[0] , templateTitle[6] . According to the source , it was found that around templateYValue[max] templateScale of of the world templateTitle[2] templateYLabel[2] for templateTitle[1] templateTitle[2] templateYLabel[2] templateYLabel[3] in templateXValue[0] .
generated: This statistic shows the CFPs of the Number crowdfunding platforms worldwide as of 2014 , region .  According to the source , it was found that around 600 % of the world platforms CFPs for crowdfunding platforms CFPs in Europe .

Example 218:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Average order value of online food orders in the U.S. 2017
X_Axis['Response']: ['0$_no_expenses', 'Up_to_25$', 'Up_to_50$', 'Up_to_75$', 'Up_to_100$', 'Up_to_150$', 'Up_to_300$', 'More_than_300$']
Y_Axis['Share', 'of', 'respondents']: ['1', '26', '34', '12', '14', '6', '6', '0']

gold: This statistic displays the average order value of online food orders in the United States as of April 2017 . During the survey period , 26 percent of responding online food shoppers stated that their usual online food order amounted to up to 25 U.S. dollars .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2017 . During the survey period , templateYValue[1] templateScale of responding templateTitle[3] templateTitle[4] shoppers stated that their usual templateTitle[3] templateTitle[4] templateTitle[1] amounted to templateXValue[1] to 25 templateTitleSubject[0] dollars .

generated_template: This statistic shows the results of a survey about the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] templateTitle[7] . According to the source , templateYValue[max] templateScale of templateYLabel[1] stated that they used templateXValue[0] and templateXValue[0] by the templateXValue[1] .
generated: This statistic shows the results of a survey about the most value online food orders in U.S. 2017 .  According to the source , 34 % of respondents stated that they used 0$_no_expenses and by the Up_to_25$ .

Example 219:
titleEntities: {'Subject': ['New England Patriots'], 'Date': ['1960']}
title: Career touchdown leaders - New England Patriots 1960 to 2020
X_Axis['Month']: ['Rob_Gronkowski', 'Stanley_Morgan', 'Ben_Coates', 'Randy_Moss', 'Sam_Cunningham', 'Jim_Nance', 'Tony_Collins', 'Gino_Cappelletti', 'Irving_Fryar', 'Larry_Garron', 'Julian_Edelman', 'Jim_Colclough', 'Corey_Dillon', 'Curtis_Martin', 'Wes_Welker', 'Steve_Grogan', 'Troy_Brown', 'LeGarrette_Blount', 'Kevin_Faulk', 'James_White']
Y_Axis['Touchdowns', 'scored']: ['80', '68', '50', '50', '49', '46', '44', '42', '42', '42', '41', '39', '39', '37', '37', '36', '35', '35', '33', '32']

gold: The statistic shows New England Patriots players with the most touchdowns scored in franchise history . Rob Gronkowski is the career touchdown leader of the New England Patriots with 80 touchdowns .
gold_template: The statistic shows templateTitleSubject[0] Patriots players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] Patriots with templateYValue[max] templateYLabel[0] .

generated_template: The statistic shows templateTitleSubject[0] Patriots players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] templateTitle[4] with templateYValue[max] templateYLabel[0] .
generated: The statistic shows New England Patriots Patriots players with the most Touchdowns scored in franchise history .  Rob_Gronkowski is the Career touchdown leader of the New England Patriots England with 80 Touchdowns .

Example 220:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. winter heating oil prices 2005/06 - 2019/20
X_Axis['Winter', 'of']: ['2019/20', '2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'gallon']: ['3.02', '3.07', '2.78', '2.41', '2.06', '3.04', '3.88', '3.87', '3.73', '3.38', '2.85', '2.65', '3.33', '2.42', '2.44']

gold: The average price of heating oil in the United States in the winter between 2019 and 2020 is expected to reach 3.02 U.S. dollars per gallon . The number of heating degree days , days the average temperature is below 18° Celsius ( 65° Fahrenheit ) , helps quantify the energy demand required to heat a building . Heating oil basics Heating oil is a liquid petroleum product that is , among other things , used to heat residential buildings as a fuel oil in furnaces or boilers .
gold_template: The average templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[0] in the templateXLabel[0] between 2019 and 2020 is expected to reach templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The number of templateTitle[2] degree days , days the average temperature is below 18° Celsius ( 65° Fahrenheit ) , helps quantify the energy demand required to heat a building . templateTitle[2] templateTitle[3] basics templateTitle[2] templateTitle[3] is a liquid petroleum product that is , among other things , used to heat residential buildings as a fuel templateTitle[3] in furnaces or boilers .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of the templateTitle[1] from templateTitle[6] as of 2020 . As of the measured period , the mobile app had an average of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the U.S. winter heating oil of the winter from 2019/20 as of 2020 .  As of the measured period , the mobile app had an average of 3.88 million per gallon .

Example 221:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2018']}
title: Share of U.S. nickel imports by country 2015 to 2018
X_Axis['Country', 'of', 'origin']: ['Other', 'Finland', 'Australia', 'Norway', 'Canada']
Y_Axis['Share', 'of', 'nickel', 'imports']: ['32', '8', '8', '11', '41']

gold: This statistic shows the percentage of nickel imports to the United States over the period between 2014 and 2018 , by country of origin . In that period , some 41 percent of all nickel imports into the United States came from Canada .
gold_template: This statistic shows the templateScale of templateYLabel[1] templateYLabel[2] to the templateTitle[1] over the period between 2014 and templateTitleDate[max] , templateTitle[4] templateXLabel[0] of templateXLabel[1] . In that period , some templateYValue[max] templateScale of all templateYLabel[1] templateYLabel[2] into the templateTitle[1] came from templateXValue[last] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of internet users had been using templateTitle[0] templateTitle[1] .
generated: This statistic shows the Share U.S. nickel imports by in the United Kingdom ( U.S. ) in 2015 .  In 2015 , around 41 % of internet users had been using Share U.S. .

Example 222:
titleEntities: {'Subject': ['India'], 'Date': ['2019']}
title: Highest grossing domestic movies India 2019
X_Axis['Movie', 'Name']: ['War', 'Kabir_Singh', 'Uri-_The_Surgical_Strike', 'Bharat', 'Mission_Mangal', 'Kesari', 'Total_Dhamaal', 'Saaho', 'Chhichhore', 'Super_30']
Y_Axis['Box', 'office', 'gross', 'in', 'billion', 'Indian', 'rupees']: ['2.92', '2.76', '2.44', '1.97', '1.93', '1.52', '1.5', '1.49', '1.47', '1.47']

gold: The Bollywood movie 'War ' was the highest grossing domestic movie produced in India in 2019 with an all India net collection of almost three billion Indian rupees . This was followed by 'Kabir Singh ' at around 2.8 billion rupees worth box office collection that year .
gold_template: The Bollywood templateXLabel[0] 'War ' was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] produced in templateTitleSubject[0] in templateTitleDate[0] with an all templateTitleSubject[0] net collection of almost templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] . This was followed by 'Kabir templateXValue[1] ' at around templateYValue[1] templateScale templateYLabel[5] worth templateYLabel[0] templateYLabel[1] collection that year .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateXValue[0] as of fourth quarter templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of reporting templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] .
generated: This statistic shows the Box of Highest grossing domestic in the War as of fourth quarter 2019 , sorted 2019 Movie .  In 2019 , around 2.92 billion of reporting movies India 2019 .

Example 223:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Average planned spend on Christmas presents in selected European countries 2015
X_Axis['Country']: ['United_Kingdom', 'Luxembourg', 'France', 'Austria', 'Spain', 'Italy', 'Germany', 'Belgium', 'Czech_Republic', 'Romania', 'Poland', 'Netherlands']
Y_Axis['Median', 'amount', 'in', 'euros']: ['420', '300', '250', '250', '200', '200', '200', '150', '150', '110', '70', '40']

gold: This statistic displays the average amount consumers plan to spend on Christmas presents in 2015 in selected European countries . The United Kingdom ( UK ) had the highest spend , with consumers expecting to budget 420 euros for Christmas gifts .
gold_template: This statistic displays the templateTitle[0] templateYLabel[1] consumers plan to templateTitle[2] on templateTitle[3] templateTitle[4] in templateTitleDate[0] in templateTitle[5] templateTitleSubject[0] templateTitle[7] . The templateXValue[0] ( UK ) had the highest templateTitle[2] , with consumers expecting to budget templateYValue[max] templateYLabel[2] for templateTitle[3] gifts .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2020 , templateTitle[4] templateXLabel[0] . During the measured period , templateTitle[1] templateYLabel[2] in templateXValue[0] had the highest monthly active templateTitle[0] templateTitle[1] templateTitle[2] with a total of templateYValue[max] thousand .
generated: This statistic shows the Median of the Average planned spend Christmas as of 2020 , presents Country .  During the measured period , planned euros in United_Kingdom had the highest monthly active Average planned spend with a total of 420 thousand .

Example 224:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global online shopping order value 2019 , by platform
X_Axis['Platform']: ['Macintosh', 'Windows', 'iOS', 'Chrome_OS', 'Linux', 'Android', 'Windows_Phone']
Y_Axis['Order', 'value', 'in', 'U.S.', 'dollars']: ['132.6', '127.77', '93.52', '87.98', '85.72', '76.21', '66.06']

gold: This statistic provides information on the average order value of online shopping orders worldwide in the second quarter of 2019 , differentiated by platform . During that period , online orders which were placed through Android devices had an average value of 76.21 U.S. dollars .
gold_template: This statistic provides information on the average templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] orders worldwide in the second quarter of templateTitleDate[0] , differentiated templateTitle[6] templateXLabel[0] . During that period , templateTitle[1] orders which were placed through templateXValue[5] devices had an average templateYLabel[1] of templateYValue[5] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitle[5] as of 2018 , based on reach . During the survey period , it was found that templateYValue[max] templateScale of female users had a templateXValue[0] account .
generated: This statistic shows the Global online shopping order in Global 2019 as of 2018 , based on reach .  During the survey period , it was found that 132.6 % of female users had a Macintosh account .

Example 225:
titleEntities: {'Subject': ['Russia'], 'Date': []}
title: Weekend box office revenue in Russia and CIS January 2020 , by film
X_Axis['Month']: ['Kholop', 'Perfect_Man', 'Spies_in_Disguise', 'Bad_Boys_for_Life', 'Invasion', 'Marafon_Zhelaniy', 'Soyuz_Spaseniya', 'The_Grudge', 'Jumanji:_The_Next_Level', 'Richard_Jewell']
Y_Axis['Revenue', 'in', 'thousand', 'U.S.', 'dollars']: ['12530.82', '6603.13', '5899.09', '5092.75', '3106.25', '1927.94', '1856.38', '1376.52', '1098.84', '828.02']

gold: Over three weekends of January 2020 , the Russian comedy film `` Kholop , '' translated as `` Serf , '' had the largest aggregate gross box office in Armenia , Belarus , Kazakhstan , Moldova , and Russia , measuring at approximately 12.5 million U.S. dollars , which made it the leading movie of the month by revenue . The romantic comedy `` Perfect Man , '' where the main character was played by a popular Russian singer Egor Kreed , ranked second with the box office of over 6.6 million U.S. dollars .
gold_template: Over three weekends of templateTitle[6] templateTitleDate[0] , the Russian comedy templateTitle[9] `` templateXValue[0] , '' translated as `` Serf , '' had the largest aggregate gross templateTitle[1] templateTitle[2] in Armenia , Belarus , Kazakhstan , Moldova , and templateTitleSubject[0] , measuring at approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , which made it the leading movie of the templateXLabel[0] templateTitle[8] templateYLabel[0] . The romantic comedy `` templateXValue[1] , '' where the main character was played templateTitle[8] a popular Russian singer Egor Kreed , ranked second with the templateTitle[1] templateTitle[2] of over templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This graph shows the quarterly templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitle[6] as of time . As of the last reported period , around templateYValue[max] templateScale of reporting templateTitle[3] were templateXValue[0] .
generated: This graph shows the quarterly Revenue of Weekend box office revenue in Russia January as of time .  As of the last reported period , around 12530.82 thousand of reporting revenue were Kholop .

Example 226:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: COPD prevalence in the U.S. 2017 , by state
X_Axis['State']: ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'District_of_Columbia', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New_Hampshire', 'New_Jersey', 'New_Mexico', 'New_York', 'North_Carolina', 'North_Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode_Island', 'South_Carolina', 'South_Dakota', 'Tennessee', 'Texas', 'Total', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West_Virginia', 'Wisconsin', 'Wyoming']
Y_Axis['COPD', 'prevalence']: ['10.1', '6.3', '5.9', '9.3', '4.4', '4.2', '5.3', '7.3', '5.8', '7.1', '6.8', '3.4', '4.7', '6.4', '8', '5.9', '6.2', '11.3', '8.4', '6.5', '5.4', '5', '8', '4', '7.5', '7.9', '5.7', '5.3', '6.5', '6', '5.8', '5.6', '5', '7.3', '4.8', '7.6', '8.1', '4.9', '5.9', '7', '7.2', '4.4', '8.9', '4.8', '6.2', '4.1', '5.7', '6.6', '5.4', '13.8', '4.7', '6.1']

gold: This statistic shows the prevalence of Chronic Obstructive Pulmonary Disease ( COPD ) in the U.S. in 2017 , by state . As of that year , around 11.3 percent of adults in Kentucky suffered from COPD .
gold_template: This statistic shows the templateYLabel[1] of Chronic Obstructive Pulmonary Disease ( templateYLabel[0] ) in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . As of that year , around templateYValue[17] templateScale of adults in templateXValue[17] suffered from templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of reporting templateTitle[3] were produced templateTitle[5] templateXValue[1] .
generated: This statistic shows the COPD of the prevalence U.S. 2017 in the U.S. ( ) in 2017 , by State .  In 2017 , around 13.8 % of reporting 2017 were produced state Alaska .

Example 227:
titleEntities: {'Subject': ['Americans'], 'Date': ['2018']}
title: Americans ' moral stance towards pornography in 2018
X_Axis['Response']: ['Morally_acceptable', 'Morally_wrong', 'Depends', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['43', '55', '1', '1']

gold: This statistic shows the moral stance of Americans regarding pornography in 2018 . During the survey , 43 percent of respondents stated they think pornography is morally acceptable , while 1 percent stated it depends on the situation .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] in templateTitleDate[0] . During the survey , templateYValue[0] templateScale of templateYLabel[1] stated they think templateTitle[5] is templateXValue[0] , while templateYValue[min] templateScale stated it templateXValue[2] on the situation .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitleDate[0] . During this survey , templateYValue[max] templateScale of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] , while templateYValue[min] templateScale said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding pornography or 2018 in .  During this survey , 55 % of respondents stated they think pornography or 2018 are Morally_acceptable , while 1 % said it Depends on the situation .

Example 228:
titleEntities: {'Subject': ['BSI'], 'Date': ['2019']}
title: Top 10 strongest nation brands by BSI score 2019
X_Axis['Country']: ['Singapore', 'Switzerland', 'Netherlands', 'Germany', 'Luxembourg', 'United_Arab_Emirates', 'Finland', 'Japan', 'United_States', 'Denmark']
Y_Axis['Brand', 'Strength', 'Index', 'Score']: ['90.5', '89.9', '89.6', '88.2', '86.9', '86.6', '86.4', '85.8', '85.7', '85.6']

gold: The statistic depicts the top ten strongest nation brands of 2019 as measured by the Brand Strength Index ( BSI ) . In 2019 , Singapore received the highest BSI score of any nation with a score of 90.5 .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] as measured templateTitle[5] the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitleSubject[0] ) . In templateTitleDate[0] , templateXValue[0] received the highest templateTitleSubject[0] templateYLabel[3] of any templateTitle[3] with a templateYLabel[3] of templateYValue[max] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] of templateTitle[4] as of 2015 . During that year , the templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] with an templateTitle[2] of templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Top 10 the strongest nation of brands as of 2015 .  During that year , the Singapore was the Top 10 Country with an strongest of 90.5 Strength Index .

Example 229:
titleEntities: {'Subject': ['Utah'], 'Date': ['2019']}
title: Number of active physicians in Utah 2019 , by specialty area
X_Axis['Specialty', 'area']: ['Psychiatry', 'Surgery', 'Anesthesiologists', 'Emergency_medicine', 'Radiology', 'Cardiology', 'Oncology_(cancer)', 'Endocrinology_diabetes_&_metabolism', 'All_other_specialities', 'Total_specialty']
Y_Axis['Number', 'of', 'physicians']: ['304', '316', '439', '426', '311', '174', '106', '33', '1587', '3696']

gold: This statistic depicts the number of active physicians in Utah as of March 2019 , ordered by specialty area . At that time , there were 439 anesthesiologists active in Utah . In total , there were almost 4,000 physicians in the state .
gold_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of 2019 , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . In templateXValue[last] , there were almost 4,000 templateYLabel[1] in the state .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of 2019 , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . There are approximately 21,400 templateXValue[last] templateYLabel[1] templateTitle[1] in the state .
generated: This statistic depicts the Number of active physicians in Utah as of 2019 , ordered by Total_specialty area .  At that time , there were 439 Anesthesiologists active in Utah .  There are approximately 21,400 Total_specialty physicians active in the state .

Example 230:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: General practitioners practicing in Europe in 2017 , by country
X_Axis['Country']: ['France', 'Germany', 'United_Kingdom', 'Italy', 'Spain', 'Portugal', 'Netherlands', 'Belgium', 'Poland', 'Austria', 'Ireland', 'Greece', 'Slovenia', 'Estonia', 'Luxembourg']
Y_Axis['Number', 'of', 'employees']: ['60214', '58170', '49824', '43731', '35378', '24248', '14641', '12992', '8418', '6637', '3942', '3647', '1237', '937', '534']

gold: In 2017 , there were over 60 thousand general practitioners ( GP ) practicing in France , the highest number recorded in Europe , followed by Germany with approximately 58.1 thousand GPs and the United Kingdom with almost 49.8 thousand . These three countries having the highest number of GPs goes in direct correlation with their population sizes being the highest in Europe . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .
gold_template: In templateTitleDate[0] , there were over templateYValue[max] thousand templateTitle[0] templateTitle[1] ( GP ) templateTitle[2] in templateXValue[0] , the highest templateYLabel[0] recorded in templateTitleSubject[0] , followed templateTitle[5] templateXValue[1] with approximately 58.1 thousand GPs and the templateXValue[2] with almost templateYValue[2] thousand . These three countries having the highest templateYLabel[0] of GPs goes in direct correlation with their population sizes being the highest in templateTitleSubject[0] . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 templateScale of respondents consult with a physician at least once a year .

generated_template: In templateTitleDate[0] , there were over templateYValue[max] thousand templateTitle[2] templateTitle[0] in templateXValue[0] , the highest templateYLabel[0] recorded in templateTitleSubject[0] . Followed templateTitle[5] templateXValue[1] with almost templateYValue[1] thousand templateTitle[0] and then templateXValue[2] with around 43.1 thousand . As these countries have some of the highest populations in the European Union , it is follows that they have the highest templateYLabel[0] of templateTitle[2] templateTitle[0] .
generated: In 2017 , there were over 60214 thousand practicing General in France , the highest Number recorded in Europe .  Followed by Germany with almost 58170 thousand General and then United_Kingdom with around 43.1 thousand .  As these countries have some of the highest populations in the European Union , it is follows that they have the highest Number of practicing General .

Example 231:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Leading companies in Norway 2019 , by number of employees
X_Axis['Month']: ['Helse_Sør-Øst_RHF', 'Telenor_ASA', 'Aker_ASA', 'Equinor_ASA_/_Statoil', 'Posten_Norge_AS', 'Orkla_ASA', 'Yara_International_ASA', 'Aker_Solutions_ASA', 'Tallyman_AS', 'Norges_Statsbaner_AS', 'Norsk_Hydro_ASA', 'Marine_Harvest_ASA', 'Strawberry_Holding_AS', 'Nordic_Choice_Hospitality_Group_AS', 'Kongsberg_Automotive_ASA', 'DNB_ASA', 'Hfn_Group_AS', 'Evry_ASA', 'Hospitality_Invest_AS', 'Nokas_AS']
Y_Axis['Number', 'of', 'employees']: ['60368', '31000', '20753', '20245', '18327', '18154', '14736', '14300', '13760', '13006', '12911', '12717', '10412', '10320', '9791', '9561', '9172', '9100', '9001', '8273']

gold: This statistic shows the 20 biggest companies in Norway as of March 2019 , by number of employees . Helse Sør-Øst RHF was ranked first with over 60 thousand employees , while Telenor ASA was ranked second with 31 thousand employees .
gold_template: This statistic shows the 20 biggest templateTitle[1] in templateTitleSubject[0] as of 2019 , templateTitle[4] templateYLabel[0] of templateYLabel[1] . templateXValue[0] RHF was ranked first with over templateYValue[max] thousand templateYLabel[1] , while templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] as of 2018 . As of the measured period , templateXValue[0] had the biggest templateYLabel[0] of templateYValue[max] templateYLabel[1] .
generated: The statistic shows the Leading companies Norway 2019 by number as of 2018 .  As of the measured period , Helse_Sør-Øst_RHF had the biggest Number of 60368 employees .

Example 232:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009']}
title: Golf industry in the U.S. : total economic output by state 2009
X_Axis['State']: ['California', 'Florida', 'Texas', 'New_York', 'North_Carolina', 'Georgia', 'Ohio', 'Illinois', 'Michigan', 'Arizona', 'Virginia', 'New_Jersey', 'Massachusetts', 'Oregon', 'Hawaii', 'Washington', 'Wisconsin', 'Minnesota', 'Pennsylvania', 'South_Carolina', 'Colorado', 'Indiana', 'Connecticut', 'New_Mexico', 'Louisiana', 'Iowa', 'Kentucky']
Y_Axis['Total', 'economic', 'output', '(in', 'billion', 'U.S.', 'dollars)']: ['15.1', '13.8', '7.4', '5.3', '5.3', '5.1', '4.8', '4.8', '4.2', '3.4', '3.1', '2.8', '2.8', '2.5', '2.5', '2.5', '2.4', '2.4', '2.3', '2.3', '1.7', '1.7', '1.1', '0.99', '0.81', '0.77', '0.71']

gold: This graph depicts the total economic output of the golf industry in the U.S. by state as of 2009 . In New Mexico , the total economic output was at 985 million U.S. dollars in 2006 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[0] templateTitle[1] in the templateYLabel[5] templateTitle[6] templateXLabel[0] as of templateTitleDate[0] . In templateXValue[3] templateXValue[23] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at 985 templateScale templateYLabel[5] dollars in 2006 .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . As of templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of templateYValue[max] templateScale of any templateXLabel[0] .
generated: This statistic shows the Total of the industry U.S. total in the United Kingdom ( U.S. ) in 2009 , state .  As of 2009 , California had the highest Total of 15.1 billion of any State .

Example 233:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018', '2018']}
title: Murder in the U.S. : number of offenders by age 2018
X_Axis['Age', 'of', 'offender', 'in', 'years']: ['Infant_(<1)', '1_to_4', '5_to_8', '9_to_12', '13_to_16', '17_to_19', '20_to_24', '25_to_29', '30_to_34', '35_to_39', '40_to_44', '45_to_49', '50_to_54', '55_to_59', '60_to_64', '65_to_69', '70_to_74', '75+', 'Unknown']
Y_Axis['Number', 'of', 'offenders']: ['0', '1', '1', '8', '496', '1479', '2254', '1998', '1440', '1161', '651', '495', '439', '346', '186', '106', '82', '93', '5099']

gold: 2,254 murderers in the United States in 2018 were individuals between the ages of 20 and 24 . In the same year , the youngest murder offender was between the ages of one and four , and there were 93 murder offenders over the age of 75 . Murder rate in the United States Despite some feeling that violent crime in the United States is on the rise , perhaps due to sensationalized media coverage , the murder and nonnegligent manslaughter rate has declined steeply since 1990 .
gold_template: templateYValue[6] murderers in the templateTitle[1] in templateTitleDate[0] were individuals between the ages of templateXValue[6] and templateXValue[6] . In the same year , the youngest templateTitle[0] templateXLabel[1] was between the ages of templateXValue[1] and templateXValue[1] , and there were templateYValue[17] templateTitle[0] templateYLabel[1] over the templateXLabel[0] of 75 . templateTitle[0] rate in the templateTitle[1] Despite some feeling that violent crime in the templateTitle[1] is on the rise , perhaps due to sensationalized media coverage , the templateTitle[0] and nonnegligent manslaughter rate has declined steeply since 1990 .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[6] as of 2019 , sorted templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[max] templateScale of templateTitle[1] templateYLabel[2] templateYLabel[3] in templateXValue[3] templateXLabel[0] .
generated: This statistic shows the Number of Murder U.S. number offenders by in the 2018 as of 2019 , sorted 2018 Age .  In 2018 , around 5099 % of U.S. offenders in 9_to_12 Age .

Example 234:
titleEntities: {'Subject': ['UK'], 'Date': ['2014', '2019']}
title: Monthly hours of sunlight in UK 2014 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sep_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16", "Dec_'15", "Nov_'15", "Oct_'15", "Sep_'15", "Aug_'15", "Jul_'15", "Jun_'15", "May_'15", "Apr_'15", "Mar_'15", "Feb_'15", "Jan_'15", "Dec_'14", "Nov_'14", "Oct_'14", "Sep_'14", "Aug_'14", "Jul_'14", "Jun_'14", "May_'14", "Apr_'14", "Mar_'14", "Feb_'14", "Jan_'14"]
Y_Axis['Number', 'of', 'hours']: ['46.2', '48.1', '87.9', '144.0', '173.6', '173.2', '160.8', '188.5', '168.9', '115.6', '100.6', '47.9', '37.6', '63.0', '113.2', '134.1', '147.4', '237.6', '239.9', '246.0', '132.6', '85.0', '95.6', '48.7', '45.3', '71.1', '72.7', '109.0', '155.5', '168.7', '155.7', '208.3', '158.0', '119.7', '55.0', '55.1', '40.7', '74.7', '105.3', '119.9', '181.7', '156.4', '136.5', '209.6', '160.8', '117.3', '84.9', '37.1', '29.2', '35.6', '91.2', '157.8', '148.8', '160.6', '189.7', '174.4', '212.9', '121.9', '76.0', '58.5', '57.1', '51.9', '82.8', '123.3', '171.0', '223.0', '178.4', '149.6', '144.9', '126.7', '75.0', '42.8']

gold: In the period of consideration , the total monthly hours of sunlight in the UK followed a similar pattern each year . The most notable change occurred in 2018 , when the hours of sunlight shot up in May , June and July to 246 , 240 and 238 hours respectively . Unsurprisingly it was the end of each year when sunlight hours were lowest .
gold_template: In the period of consideration , the total templateTitle[0] templateYLabel[1] of templateTitle[2] in the templateTitleSubject[0] followed a similar pattern each year . The most notable change occurred in 2018 , when the templateYLabel[1] of templateTitle[2] shot up in templateXValue[7] , and to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to 2019 . As of 2020 , there were templateYValue[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of the Monthly hours sunlight UK in the United Kingdom ( UK ) from 2014 to 2019 .  As of 2020 , there were 46.2 hours .

Example 235:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the lowest access to electricity 2017
X_Axis['Country']: ['Burundi', 'Chad', 'Malawi', 'Dem._Republic_of_the_Congo', 'Niger', 'Liberia', 'Uganda', 'Sierra_Leone', 'Madagascar', 'South_Sudan', 'Burkina_Faso', 'Guinea-Bissau', 'Mozambique', 'Central_African_Republic', 'Tanzania', 'Somalia', 'Lesotho', 'Rwanda', 'Guinea', 'Zambia', 'Global_average']
Y_Axis['Access', 'rate']: ['9.3', '10.9', '12.7', '19.1', '20', '21.5', '22', '23.4', '24.1', '25.4', '25.5', '26', '27.4', '30', '32.8', '32.9', '33.7', '34.1', '35.4', '40.3', '88.8']

gold: This statistic shows the countries with the lowest access to electricity in 2017 based on access rate . As of that time , about 12.7 percent of the population in Malawi had access to electricity .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] to templateTitle[4] in templateTitleDate[0] based on templateYLabel[0] templateYLabel[1] . As of that time , about templateYValue[2] templateScale of the population in templateXValue[2] had templateYLabel[0] to templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2020 , sorted by templateXLabel[0] . In that year , templateTitle[0] templateTitle[1] templateTitle[2] averaged templateYValue[min] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Access of the Countries lowest access electricity as of 2020 , sorted by Country .  In that year , Countries lowest access averaged 9.3 rate .

Example 236:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014', '2019']}
title: Number of cash machines in the United Kingdom ( UK ) Q1 2014 -Q3 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14"]
Y_Axis['Number', 'of', 'cash', 'machines']: ['60534', '61967', '62581', '63360', '64362', '65379', '67419', '69603', '70045', '70114', '70045', '70020', '70254', '70682', '70330', '70270', '70018', '69876', '70006', '69382', '69120', '68819', '68135']

gold: This statistic illustrates the number of cash machines in the United Kingdom ( UK ) from the first quarter of 2014 to the third quarter of 2019 . Automated transaction machines ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total number of cash machines increased between the first quarter of 2014 and the second quarter of 2016 , reaching a total of more than 70.1 thousand as of the second quarter of 2016 .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the first templateXLabel[0] of templateTitleDate[min] to the third templateXLabel[0] of templateTitleDate[max] . Automated transaction templateYLabel[2] ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] templatePositiveTrend between the first templateXLabel[0] of templateTitleDate[min] and the second templateXLabel[0] of 2016 , reaching a total of more than templateYValue[9] thousand as of the second templateXLabel[0] of 2016 .

generated_template: This statistic gives information on the templateYLabel[0] of templateTitleSubject[0] templateYLabel[2] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . As of the fourth templateXLabel[0] of templateTitleDate[max] , the company 's templateYLabel[1] templateYLabel[2] amounted to have fourth templateXLabel[0] of 2018 .
generated: This statistic gives information on the Number of United Kingdom machines from the first Quarter of 2014 to the fourth Quarter of 2019 .  As of the fourth Quarter of 2019 , the company 's cash machines amounted to have fourth Quarter of 2018 .

Example 237:
titleEntities: {'Subject': ['CPG'], 'Date': ['2016']}
title: Global operating margin of CPG companies 2016 , by company
X_Axis['Company']: ['Kraft_Heinz', 'Kimberly-Clark', 'General_Mills', 'PepsiCo', 'Nestlé']
Y_Axis['Operating', 'margin']: ['21.9', '18.2', '15.9', '15.6', '14.7']

gold: This statistic shows the operating margins of consumer packaged goods ( CPG ) companies worldwide in 2016 , sorted by company . In that year , Kraft Heinz had an operating margin of 21.9 percent , the highest among the referenced CPG companies .
gold_template: This statistic shows the templateYLabel[0] margins of consumer packaged goods ( templateTitleSubject[0] ) templateTitle[4] worldwide in templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . In that year , templateXValue[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale , the highest among the referenced templateTitleSubject[0] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . As of that year , the templateXValue[0] had a templateYLabel[0] of around templateYValue[max] templateScale templateTitle[2] templateTitle[3] .
generated: This statistic shows the Operating of the operating margin CPG companies in 2016 .  As of that year , the Kraft_Heinz had a Operating of around 21.9 % margin CPG .

Example 238:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Cities with the largest parkland percentage in the U.S. 2018
X_Axis['State']: ['Anchorage', 'Fremont', 'Irvine', 'Scottsdale', 'North_Las_Vegas', 'Chesapeake', 'New_Orleans', 'Albuquerque', 'New_York', 'Washington_D.C.', 'San_Francisco', 'Las_Vegas', 'El_Paso', 'San_Diego', 'Jersey_City']
Y_Axis['Share', 'of', 'parkland']: ['84.2', '49.4', '27.4', '26.9', '26.2', '26', '25.9', '23.2', '21.7', '21.1', '19.6', '19.4', '19.2', '19.1', '18.1']

gold: This statistic shows the cities with the largest parkland percentage of the city area in the United States in 2018 . In Anchorage , Alaska , 84.2 percent of the city 's area was comprised of parkland in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] templateScale of the templateXValue[last] area in the templateTitle[5] in templateTitleDate[0] . In templateXValue[0] , Alaska , templateYValue[max] templateScale of the templateXValue[last] 's area was comprised of templateYLabel[1] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In that year , there were almost templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[1] .
generated: This statistic shows the Share of the largest parkland percentage U.S. in 2018 , State .  In that year , there were almost 49.4 parkland in Fremont .

Example 239:
titleEntities: {'Subject': ['Armour'], 'Date': ['2014']}
title: Product quality rating of Under Armour footwear United States 2014
X_Axis['Response']: ['Extremely_positive', 'Somewhat_positive', 'Neutral', 'Somewhat_negative', 'Extremely_Negative']
Y_Axis['Share', 'of', 'respondents']: ['72', '22', '5', '-', '-']

gold: This statistic shows how consumers rate the product quality of Under Armour footwear . 72 % of respondents rated Under Armour 's quality as extremely positive .
gold_template: This statistic shows how consumers rate the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitleSubject[0] templateTitle[5] . templateYValue[0] templateScale of templateYLabel[1] rated templateTitle[3] templateTitleSubject[0] 's templateTitle[1] as templateXValue[0] .

generated_template: The statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books , which of the following templateTitle[0] templateXValue[last] . According to the chose `` templateXValue[1] '' and templateXValue[2] were second and third .
generated: The statistic shows the results of a survey about the rating Under of several individual Product Extremely_Negative in Armour 2015 , phrased Somewhat_positive the question : `` except reading books , which of the following Product Extremely_Negative .  According to the chose `` Somewhat_positive '' and Neutral were second and third .

Example 240:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020']}
title: Public opinion on the most important problem facing the U.S. 2020
X_Axis['Response']: ['Dissatisfaction_with_government/Poor_leadership', 'Immigration', 'Healthcare', 'Ethics/moral/religious/family_decline', 'Unifying_the_country', 'Poverty/Hunger/Homelessness', 'Lack_of_respect_for_each_other', 'Environment/Pollution/Climate_change', 'Race_relations/Racism', 'Situation_in_Iraq/ISIS', 'Foreign_policy/Foreign_aid/Focus_overseas', 'Economy_in_general', 'Guns/Gun_control', 'Gap_between_rich_and_poor', 'Education', 'Wars/War_(nonspecific)/Fear_of_war']
Y_Axis['Share', 'of', 'respondents']: ['28', '6', '6', '5', '5', '5', '4', '4', '3', '2', '2', '2', '2', '2', '2', '2']

gold: This statistic represents American adults ' view of the most important problem facing the United States . In January 2020 , 28 percent of the participants stated that poor leadership and a general dissatisfaction with the government were the most important problems facing the U.S .
gold_template: This statistic represents American adults ' view of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] the templateTitle[6] . In 2020 , templateYValue[max] templateScale of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: The statistic shows the results of a survey about the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] in templateTitleSubject[1] as of 2018 . The survey found that templateXValue[0] was templateTitleSubject[1] 's templateTitle[0] loved templateTitleSubject[0] princess with templateYValue[min] out of templateYValue[max] British adults choosing her as their favorite . templateXValue[1] and templateXValue[2] were second and third templateTitle[0] templateTitle[1] at templateYValue[2] and templateYValue[1] templateScale .
generated: The statistic shows the results of a survey about the Public opinion U.S. important in U.S. as of 2018 .  The survey found that Dissatisfaction_with_government/Poor_leadership was U.S. 's Public loved U.S. princess with 2 out of 28 British adults choosing her as their favorite .  Immigration and Healthcare were second and third Public opinion at 6 and percent .

Example 241:
titleEntities: {'Subject': ['Volcanic'], 'Date': ['2016']}
title: Volcanic eruptions - people affected worldwide up to 2016
X_Axis['Location', 'and', 'Date']: ['Volcanic_eruption_in_the_Philippines_(June_9_1991)', 'Volcano_eruption_in_Ecuador_(August_14_2015)', 'Volcanic_eruption_in_Nicaragua_(April_9_1992)', 'Volcano_eruption_in_Ecuador_(August_14_2006)', 'Volcano_eruption_in_Indonesia_(April_5_1982)', 'Volcano_eruption_in_Indonesia_(1969)', 'Volcanic_eruption_in_Comoros_(November_24_2005)', 'Volcanic_eruption_in_the_Philippines_(Feb._6_1993)', 'Volcanic_eruption_in_Papua_New_Guinea_(September_19_1994)', 'Volcanic_eruption_in_Indonesia_(October_24_2002)']
Y_Axis['Number', 'of', 'victims']: ['1036065', '800000', '300075', '300013', '300000', '250000', '245000', '165009', '152002', '137140']

gold: The statistic shows the number of people , who were affected by the world 's most significant volcanic eruptions from 1900 to 2016  . In 1991 , total 1,036,035 were affected due to volcanic eruption in Philippines .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[2] , who were templateTitle[3] by the world 's most significant templateXValue[0] templateTitle[1] from 1900 to templateTitleDate[0] . In 1991 , total 1,036,035 were templateTitle[3] due to templateXValue[0] in templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of October templateTitleDate[0] . As of the third quarter of templateTitleDate[0] , templateXValue[0] had an average of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Volcanic eruptions people affected worldwide up 2016 as of October 2016 .  As of the third quarter of 2016 , Volcanic_eruption_in_the_Philippines_(June_9_1991) had an average of 1036065 million victims .

Example 242:
titleEntities: {'Subject': ['Black Friday'], 'Date': ['2017']}
title: U.S. consumer sentiments towards Black Friday shopping 2017
X_Axis['Response']: ['It_is_a_great_opportunity_to_buy_gifts_for_the_holidays', "It's_a_tradition", 'I_like_it_even_more_now_that_I_can_shop_online', 'It_is_the_best_opportunity_to_buy_expensive_items_at_a_discount', 'It_is_when_you_find_promotions_that_are_not_available_at_any_other_time_of_year', 'It_is_a_good_way_to_spend_quality_time_with_friends/family', 'I_will_wait_until_Cyber_Monday_to_do_most_of_my_shopping', 'Promotions_are_never_on_products_I_am_interested_in', 'Retailers_just_discount_their_worst_brands', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['42', '39', '37', '33', '31', '19', '18', '16', '9', '5']

gold: This statistic shows the results of a 2017 survey in which U.S. consumers were asked about their attitude towards Black Friday shopping . According to the survey , 42 percent of respondents said that Black Friday is a great opportunity to buy gifts for the holidays .
gold_template: This statistic shows the results of a templateTitleDate[0] survey in which templateTitle[0] consumers were asked about templateXValue[8] attitude templateTitle[3] templateTitleSubject[0] shopping . According to the survey , templateYValue[max] templateScale of templateYLabel[1] said templateXValue[2] templateTitleSubject[0] is a templateXValue[0] to templateXValue[0] for the templateXValue[0] .

generated_template: The statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books . As of templateTitleDate[0] , templateYValue[max] templateScale of templateYLabel[1] felt templateXValue[last] followed by the templateXValue[0] and templateXValue[2] were followed by templateXValue[1] .
generated: The statistic shows the results of a survey about the sentiments towards of several individual U.S. None_of_these in Black Friday 2015 , phrased It's_a_tradition the question : `` except reading books .  As of 2017 , 42 % of respondents felt None_of_these followed by the It_is_a_great_opportunity_to_buy_gifts_for_the_holidays and I_like_it_even_more_now_that_I_can_shop_online were followed by It's_a_tradition .

Example 243:
titleEntities: {'Subject': ['YouTube'], 'Date': []}
title: All-time most viewed YouTube channel owners 2020
X_Axis['Month']: ["Ryan's_World", 'PewDiePie', 'Like_Nastya_Vlog', '✿_Kids_Diana_Show', 'DanTDM_(TheDiamondMinecart)', 'Fun_Toys_Collector_Disney', 'Vlad_and_Nikita', 'FGTeeV', 'Family_Fun_Pack', 'CookieSwirlC', 'Markiplier']
Y_Axis['All-time', 'channel', 'views', 'in', 'billions']: ['35.18', '24.44', '22.68', '17.01', '16.01', '14.86', '14.07', '13.11', '12.66', '12.42', '12.29']

gold: As of January 2020 , Ryan from Ryan 's World ( formerly known as Ryan ToysReview ) had reached almost 35.2 billion lifetime video views , making the elementary schooler the most viewed YouTube channel owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name PewDiePie . Ryan has been uploading YouTube videos since March 2015 , and mainly features in videos where he is playing with and reviews toys `` for kids , by a kid '' .
gold_template: As of 2020 , Ryan from Ryan 's templateXValue[0] ( formerly known as Ryan ToysReview ) had reached almost templateYValue[max] templateScale lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitleSubject[0] videos since 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[6] as of 2019 . At that templateXLabel[0] , there were a total of templateYValue[max] templateScale of the templateXValue[2] and templateXValue[2] .
generated: This statistic shows the All-time most of YouTube 2020 as of 2019 .  At that Month , there were a total of 35.18 billions of the Like_Nastya_Vlog and .

Example 244:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2018']}
title: Brazil : most popular music genres 2018
X_Axis['Response']: ['Pop', 'Brazilian_pop', 'Sertanejo', 'Rock', 'Samba/pagode', 'Electronic/dance_music', 'Dance', 'Gospel', 'Hip_hop', 'Reggae', 'Forró', 'Funk/soul', 'Blues', 'Latin', 'Rap', 'Country', 'Metal', 'Techno/EDM', 'R&B/soul', 'Jazz', 'Heavy_metal', 'Classical/opera', 'Reggaeton', 'Easy_listening', 'Punk', 'Folk', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['55.5', '54', '50.3', '48.8', '38.1', '37.9', '35.6', '35.1', '31.8', '31', '30.2', '25.2', '24.7', '23.4', '23.2', '22.6', '17.6', '17.4', '17.3', '16.8', '14.5', '14.3', '13.2', '10.7', '10.4', '8.9', '4.8']

gold: This statistic shows the results of a Deezer survey on music listening habits among adults in Brazil as of 2018 . That year , 55.5 percent of Brazilian respondents claimed to listen to pop music , whereas 54 percent said they listened to Brazilian pop .
gold_template: This statistic shows the results of a Deezer survey on templateXValue[5] templateXValue[23] habits among adults in templateTitleSubject[0] as of templateTitleDate[0] . That year , templateYValue[max] templateScale of templateXValue[1] templateYLabel[1] claimed to listen to templateXValue[0] templateXValue[5] , whereas templateYValue[1] templateScale said they listened to templateXValue[1] templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2013 . During the survey , templateYValue[1] templateScale of the templateYLabel[1] stated that they would choose templateXValue[1] .
generated: This statistic shows the Brazil most popular music genres 2018 in the 2018 as of 2013 .  During the survey , 54 % of the respondents stated that they would choose Brazilian_pop .

Example 245:
titleEntities: {'Subject': ['Daily'], 'Date': ['2018']}
title: Daily online video usage in selected countries 2018
X_Axis['Country']: ['Saudi_Arabia', 'Turkey', 'Brazil', 'New_Zealand', 'Australia', 'Mexico', 'Spain', 'Canada', 'United_States', 'South_Korea', 'France', 'Philippines', 'India', 'Germany', 'Japan', 'China', 'Indonesia', 'South_Africa', 'Nigeria']
Y_Axis['Share', 'of', 'respondents']: ['64', '64', '62', '61', '60', '56', '53', '50', '50', '44', '42', '34', '33', '32', '32', '30', '21', '19', '16']

gold: This statistic gives information on the share of internet users in selected countries who watch online videos every day as of January 2018 . During the survey , it was found that 50 percent of U.S. internet users watched online video content on a daily basis . Additionally , more than half of the internet users in Mexico watched online videos every day .
gold_template: This statistic gives information on the templateYLabel[0] of internet users in templateTitle[4] templateTitle[5] who watch templateTitle[1] videos every day as of 2018 . During the survey , it was found that templateYValue[7] templateScale of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitleSubject[0] basis . Additionally , more than half of the internet users in templateXValue[5] watched templateTitle[1] videos every day .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] templateTitle[3] rate of online users in selected templateTitleSubject[0] markets as of 2018 . During the survey period it was found that templateYValue[2] templateScale of templateYLabel[1] from templateXValue[2] stated they went online on a templateTitle[1] basis .
generated: This statistic gives information on the online video usage rate of online users in selected Daily markets as of 2018 .  During the survey period it was found that 62 % of respondents from Brazil stated they went online on a online basis .

Example 246:
titleEntities: {'Subject': ['Latin American'], 'Date': ['2018', '2018']}
title: 2018 FIFA World Cup : average age of Latin American soccer teams
X_Axis['Country']: ['Costa_Rica', 'Argentina', 'Mexico', 'Panama', 'Brazil', 'Colombia', 'Uruguay', 'Peru']
Y_Axis['Average', 'age', 'in', 'years']: ['29.8', '29.6', '29.3', '28.9', '28.6', '28.4', '28.2', '27.5']

gold: The statistic presents the average age of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . Costa Rica was the Latin American soccer team with the oldest average age ( 29.8 years ) , followed by Argentina with team players averaging 29.6 years old .
gold_template: The statistic presents the templateYLabel[0] templateYLabel[1] of all templateTitleSubject[0] soccer templateTitle[9] participating in the templateTitleDate[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . templateXValue[0] was the templateTitleSubject[0] soccer team with the oldest templateYLabel[0] templateYLabel[1] ( templateYValue[max] templateYLabel[2] ) , followed by templateXValue[1] with team players averaging templateYValue[1] templateYLabel[2] old .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] of active templateTitle[0] templateTitle[3] in selected countries . As of templateTitleSubject[0] templateTitleDate[0] , templateXValue[17] had the second-youngest templateTitle[0] user base with an templateYLabel[0] templateYLabel[1] of templateYValue[17] templateYLabel[2] . In templateXValue[0] , active templateTitle[0] templateTitle[3] averaged templateYValue[max] templateYLabel[2] .
generated: This statistic gives information on the Average age of active 2018 Cup in selected countries .  As of Latin American 2018 , Peru had the second-youngest 2018 user base with an Average age of 27.5 years .  In Costa_Rica , active 2018 Cup averaged 29.8 years .

Example 247:
titleEntities: {'Subject': ['PlayStation'], 'Date': ['2014']}
title: Suggested retail price of a PlayStation 4 in 2014 , by country
X_Axis['Country']: ['Brazil', 'Argentina', 'India', 'Indonesia', 'Thailand', 'South_Africa', 'United_Kingdom', 'Philippines', 'Germany', 'Malaysia', 'Russia', 'Singapore', 'South_Korea', 'Australia', 'United_Arab_Emirates', 'Canada', 'Hong_Kong', 'Taiwan', 'United_States', 'Japan']
Y_Axis['Price', 'in', 'U.S.', 'dollars']: ['1702.43', '1387.9', '653.54', '619.76', '614.77', '585.79', '580.94', '559.51', '557.07', '550.76', '523.85', '505.7', '466.82', '492.84', '462.56', '451.42', '435.23', '427.83', '399.99', '392.38']

gold: The ranking shows the suggested retail price of a PlayStation 4 in selected countries worldwide as of March 2014 . Brazil ranked first with a suggested retail price of more than 1,702 U.S. dollars , almost four times as much as the price in the United States ( 399.99 dollars ) . Global unit sales data from 2014 and 2015 shows that PlayStation 4 was the highest selling platform worldwide in those years .
gold_template: The ranking shows the templateTitle[0] templateTitle[1] templateYLabel[0] of a templateTitleSubject[0] templateTitle[4] in selected countries worldwide as of 2014 . templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateYLabel[0] of more than templateYValue[max] templateYLabel[1] templateYLabel[2] , almost templateTitle[4] times as much as the templateYLabel[0] in the templateXValue[6] templateXValue[18] ( templateYValue[18] templateYLabel[2] ) . Global unit sales data from templateTitleDate[0] and 2015 shows that templateTitleSubject[0] templateTitle[4] was the highest selling platform worldwide in those years .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleDate[0] . In that year , the templateXValue[0] produced some templateYValue[max] templateScale templateYLabel[2] , followed by templateXValue[1] and templateXValue[2] .
generated: This statistic shows the PlayStation retail price PlayStation 4 2014 by country in 2014 .  In that year , the Brazil produced some 1702.43 million dollars , followed by Argentina and India .

Example 248:
titleEntities: {'Subject': ['Manufacturing'], 'Date': ['2016']}
title: Manufacturing costs in pharmaceutical industry by country 2016
X_Axis['Country']: ['Mexico', 'Canada', 'Netherlands', 'Italy', 'United_Kingdom', 'Australia', 'France', 'Germany', 'Japan', 'United_States']
Y_Axis['Manufacturing', 'costs', 'index', '(U.S.', '=', '100)']: ['82.9', '88.8', '89.9', '90.3', '90.8', '91.3', '91.8', '93.4', '93.6', '100']

gold: This statistic compares the manufacturing costs of the pharmaceutical industry in selected countries with costs in the United States in 2016 , based on a cost index . Manufacturing costs in all selected countries were less than in the United States , with costs in Mexico being 17.1 percent less than in the United States .
gold_template: This statistic compares the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] in selected countries with templateYLabel[1] in the templateXValue[4] templateXValue[last] in templateTitleDate[0] , based on a cost templateYLabel[2] . templateYLabel[0] templateYLabel[1] in all selected countries were less than in the templateXValue[4] templateXValue[last] , with templateYLabel[1] in templateXValue[0] being 17.1 templateScale less than in the templateXValue[4] templateXValue[last] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2020 . During the survey period , it was found that templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[3] .
generated: This statistic shows the Manufacturing of the Manufacturing costs pharmaceutical industry in the by as of 2020 .  During the survey period , it was found that 100 % of Manufacturing industry .

Example 249:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. binge drinking among adults by state 2018
X_Axis['State']: ['District_of_Columbia', 'North_Dakota', 'Wisconsin', 'Iowa', 'Nebraska', 'Illinois', 'Minnesota', 'Alaska', 'Montana', 'Hawaii', 'Colorado', 'Ohio', 'Missouri', 'Massachusetts', 'New_Hampshire', 'Pennsylvania', 'Louisiana', 'Rhode_Island', 'Michigan', 'Wyoming', 'Maine', 'Nevada', 'Texas', 'California', 'New_York', 'South_Dakota', 'United_States', 'Vermont', 'Kansas', 'New_Jersey', 'Indiana', 'Oregon', 'Virginia', 'Kentucky', 'Washington', 'South_Carolina', 'North_Carolina', 'Connecticut', 'Maryland', 'Arizona', 'Florida', 'Idaho', 'Arkansas', 'Delaware', 'New_Mexico', 'Oklahoma', 'Tennessee', 'Georgia', 'Mississippi', 'Alabama', 'Utah', 'West_Virginia']
Y_Axis['Percentage', 'of', 'binge', 'drinkers']: ['25.9', '23.3', '22.7', '21.1', '20.6', '20.3', '20', '19.6', '19.5', '19.5', '18.9', '18.9', '18.8', '18.8', '18.7', '18.2', '18.1', '18.1', '18.1', '18', '17.9', '17.9', '17.8', '17.6', '17.5', '17.4', '17.4', '17.4', '17.2', '16.7', '16.6', '16.1', '16', '15.8', '15.6', '15.5', '15.4', '15.4', '15.3', '15.2', '15.1', '15.1', '15.1', '14.8', '14.7', '13.4', '13.1', '12.9', '12.6', '12.4', '11.5', '11.5']

gold: This statistic represents the percentage of binge in the United States of America as of 2018 , in the last 30 days by state . As of that year , 17.8 percent of adults in Texas consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .
gold_template: This statistic represents the templateScale of templateYLabel[1] in the templateXValue[26] of America as of templateTitleDate[0] , in the last 30 days templateTitle[5] templateXLabel[0] . As of that year , templateYValue[22] templateScale of templateTitle[4] in templateXValue[22] consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateYLabel[0] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[1] templateYLabel[2] in templateXValue[4] had a templateYLabel[0] of templateYValue[4] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the U.S. binge of Percentage in the drinking in 2018 , adults State .  In 2018 , the binge drinkers in Nebraska had a Percentage of 20.6 binge drinkers .

Example 250:
titleEntities: {'Subject': ['first Bundesliga', 'Germany'], 'Date': ['2020']}
title: Market value of first Bundesliga football clubs in Germany in 2020
X_Axis['Club', 'Name']: ['FC_Bayern_München', 'Borussia_Dortmund', 'RasenBallsport_Leipzig', 'Bayer_04_Leverkusen', 'Borussia_Mönchengladbach', 'FC_Schalke_04', 'TSG_1899_Hoffenheim', 'Hertha_BSC', 'VfL_Wolfsburg', 'Eintracht_Frankfurt', 'SV_Werder_Bremen', '1._FSV_Mainz_05', 'SC_Freiburg', 'FC_Augsburg', '1._FC_Köln', 'Fortuna_Düsseldorf', '1._FC_Union_Berlin', 'SC_Paderborn']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['933.15', '637.4', '594.4', '445.75', '312.0', '242.75', '238.23', '233.2', '230.95', '215.8', '189.75', '147.4', '145.4', '131.15', '102.2', '93.15', '43.05', '31.25']

gold: This statistic shows the market value of the first Bundesliga football clubs in Germany as of February 11 , 2020 . The market value of FC Bayern Munich was highest at 933.15 million euros , followed by 637.4 million euros for Borussia Dortmund and 594.4 million euros for RB Leipzig .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] football templateTitle[5] in templateTitleSubject[1] as of 11 , templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateXValue[0] Munich was highest at templateYValue[max] templateScale templateYLabel[3] , followed by templateYValue[1] templateScale templateYLabel[3] for templateXValue[1] and templateYValue[2] templateScale templateYLabel[3] for RB templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] templateTitle[6] as of 2017 . As of the third quarter of templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] at templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Market value first Bundesliga of first Bundesliga clubs Germany as of 2017 .  As of the third quarter of 2020 , FC_Bayern_München had the highest Market value of first Bundesliga at 933.15 million euros .

Example 251:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Facebook reactions on top shared content 2017
X_Axis['Response']: ['Love', 'Haha', 'Wow', 'Sad', 'Angry']
Y_Axis['Share', 'of', 'reactions']: ['41', '28', '15', '12', '5']

gold: This statistic presents the reaction usage in top shared posts on Facebook in September 2017 . During the measured period , Love was the most popular Facebook reaction on top shared posts on the social network .
gold_template: This statistic presents the reaction usage in templateTitle[2] templateTitle[3] posts on templateTitleSubject[0] in 2017 . During the measured period , templateXValue[0] was the most popular templateTitleSubject[0] reaction on templateTitle[2] templateTitle[3] posts on the social network .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the Facebook who were using Facebook as of 2019 , sorted 2017 .  During that period of time , 41 % of female reactions stated that they used the social networking site .

Example 252:
titleEntities: {'Subject': ['Sales'], 'Date': ['2013']}
title: Sales of the leading toy companies worldwide 2013
X_Axis['Company']: ['Mattel', 'Lego', 'Hasbro', 'MGA_Entertainment', 'Playmobil', 'Jakks_Pacific', 'LeapFrog', 'MEGA_Bloks', 'Melissa_&_Doug']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['6300', '4500', '4000', '2000', '790', '700', '580', '400', '325']

gold: This statistic shows the sales of the leading toy companies worldwide in 2013 . In that year , Mattel was the largest global toy company with estimated sales that amounted to 6.3 billion U.S. dollars . Lego and Hasbro rounded off the leading three toy companies .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the largest global templateTitle[2] templateXLabel[0] with estimated templateYLabel[0] that amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateXValue[1] and templateXValue[2] rounded off the templateTitle[1] three templateTitle[2] templateTitle[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[2] had a templateYLabel[0] of templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Sales of the Sales leading toy companies in the 2013 in .  In that year , Hasbro had a Sales of 4500 million U.S. dollars .

Example 253:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Revenue share of various apparel decorating services in the U.S. 2014
X_Axis['Decorating', 'service']: ['Embroidery', 'Screen_printing', 'Heat_transfers', 'Vinyl_(cut)_letters/designs', 'Digitizing/artwork_services', 'Sublimation_printing', 'Emblems/patches', 'Direct-to-garment_printing', 'Rhinestones/crystals', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['45', '26', '7', '6', '5', '3', '3', '3', '1', '1']

gold: This statistic depicts the revenue share of various apparel decorating services in the United States in 2014 . The survey revealed that some 45 percent of the respondents felt that embroidery decorating services for apparel generated the most revenue .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateXLabel[0] templateXValue[4] in the templateTitle[6] in templateTitleDate[0] . The survey revealed that some templateYValue[max] templateScale of the templateYLabel[1] felt that templateXValue[0] templateXLabel[0] templateXValue[4] for templateTitle[3] generated the most templateTitle[0] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] templateTitle[3] as of templateTitleDate[0] . templateYValue[2] templateScale of templateYLabel[1] stated templateXValue[2] was currently the templateTitle[0] templateXLabel[0] in templateTitleSubject[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Revenue nation in U.S. apparel as of 2014 .  7 % of respondents stated Heat_transfers was currently the Revenue Decorating in U.S. .

Example 254:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. company data loss prevention methods 2017
X_Axis['Response']: ['Training_and_awareness_programs', 'Expanded_use_of_encryption', 'Endpoint_security_solutions', 'Identity_and_access_management_solutions', 'Additional_manual_procedures_and_controls', 'Data_loss_prevention_(DLP)_solutions', 'Security_intelligence_solutions', 'Other_system_control_practices', 'Security_certification_or_audit', 'Strenghtening_of_perimeter_controls']
Y_Axis['Share', 'of', 'respondents']: ['60', '55', '49', '44', '39', '36', '35', '26', '19', '16']

gold: This statistic presents a ranking of common data loss prevention controls and activities of organizations in the United States in 2017 . During the survey period , it was found that 35 percent of U.S. companies had implemented security intelligence solutions .
gold_template: This statistic presents a ranking of common templateXValue[5] prevention templateXValue[4] and activities of organizations in the templateTitle[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[6] templateScale of templateTitleSubject[0] companies had implemented templateXValue[2] templateXValue[6] templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2015 . During a survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[0] and templateXValue[0] .
generated: This statistic shows the U.S. company in data loss on prevention methods in the 2017 as of 2015 .  During a survey , 60 % of the respondents stated that they used Training_and_awareness_programs and .

Example 255:
titleEntities: {'Subject': ['European'], 'Date': ['2016']}
title: Selected European countries ranked by retail banking customer satisfaction 2016
X_Axis['Country']: ['Netherlands', 'Czech_Republic', 'Austria', 'Switzerland', 'Portugal', 'Germany', 'Poland', 'Sweden', 'Italy', 'United_Kingdom', 'Finland', 'Belgium', 'Denmark', 'Norway', 'France', 'Spain']
Y_Axis['Share', 'of', 'customers', 'with', 'positive', 'experience']: ['70.6', '67', '66.8', '64.8', '63', '62.3', '61.6', '60.7', '59.5', '58.4', '58.2', '56.7', '55.9', '53.9', '52.3', '35.7']

gold: This statistic illustrates the share of customers with a positive retail banking experience in the leading selected European banking systems ( countries ) as of 2016 . Approximately 70.6 percent of surveyed bank customers in the Netherlands indicated high levels of satisfaction , ranking the country highest among European banking locations in 2016 . This was followed by the Czech Republic , with 67 percent of bank customers with a positive experience throughout the year .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateTitle[5] templateTitle[6] templateYLabel[4] in the leading templateTitle[0] templateTitleSubject[0] templateTitle[6] systems ( templateTitle[2] ) as of templateTitleDate[0] . Approximately templateYValue[max] templateScale of surveyed bank templateYLabel[1] in the templateXValue[0] indicated high levels of templateTitle[8] , ranking the templateXLabel[0] highest among templateTitleSubject[0] templateTitle[6] locations in templateTitleDate[0] . This was followed templateTitle[4] the templateXValue[1] , templateYLabel[2] templateYValue[1] templateScale of bank templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateYLabel[4] throughout the year .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] as of 2020 . In that year , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] was templateXValue[0] , with an templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] of 66 templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Share of the customers positive ranked by retail banking customer satisfaction as of 2020 .  In that year , Selected European countries ranked was Netherlands , with an Share of 70.6 customers positive of 66 experience .

Example 256:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Homicide - number of murders by U.S. state in 2018
X_Axis['State']: ['California', 'Texas', 'Florida', 'Illinois', 'Pennsylvania', 'Georgia', 'North_Carolina', 'Missouri', 'Ohio', 'New_York', 'Michigan', 'Louisiana', 'Tennessee', 'Maryland', 'Indiana', 'South_Carolina', 'Virginia', 'Alabama', 'Arizona', 'New_Jersey', 'Kentucky', 'Washington', 'Arkansas', 'Colorado', 'Oklahoma', 'Nevada', 'Wisconsin', 'Mississippi', 'New_Mexico', 'District_of_Columbia', 'Massachusetts', 'Kansas', 'Minnesota', 'Connecticut', 'Oregon', 'West_Virginia', 'Utah', 'Iowa', 'Delaware', 'Alaska', 'Nebraska', 'Hawaii', 'Idaho', 'Montana', 'Maine', 'New_Hampshire', 'North_Dakota', 'Rhode_Island', 'Wyoming', 'South_Dakota', 'Vermont']
Y_Axis['Number', 'of', 'murder', 'victims']: ['1739', '1322', '1107', '884', '784', '642', '628', '607', '564', '562', '551', '530', '498', '490', '438', '392', '391', '383', '369', '286', '244', '236', '216', '210', '206', '202', '176', '171', '167', '160', '136', '113', '106', '83', '82', '67', '60', '54', '48', '47', '44', '36', '35', '34', '24', '21', '18', '16', '13', '12', '10']

gold: This statistic displays the number of murders in the United States by state . Data includes murder and nonnegligent manslaughter . In 2018 , the number of murders in California amounted to 1,739 victims .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[2] in the templateTitle[4] templateTitle[3] templateXLabel[0] . Data includes templateYLabel[1] and nonnegligent manslaughter . In templateTitleDate[0] , the templateYLabel[0] of templateTitle[2] in templateXValue[0] amounted to templateYValue[max] templateYLabel[2] .

generated_template: This graph displays the templateTitle[0] templateTitle[1] of templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[1] templateScale of templateYLabel[1] templateYLabel[2] were produced in templateXValue[2] .
generated: This graph displays the Homicide number of murders in the by in 2018 , state .  In 2018 , around 1322 % of murder victims were produced in Florida .

Example 257:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['1946', '2020']}
title: National Basketball Association all-time triple double leaders 1946 to 2020
X_Axis['Player']: ['Oscar_Robertson', 'Russell_Westbrook', 'Magic_Johnson', 'Jason_Kidd', 'LeBron_James', 'Wilt_Chamberlain', 'Larry_Bird', 'James_Harden', 'Fat_Lever', 'Nikola_Jokić', 'Bob_Cousy', 'Rajon_Rondo', 'John_Havlicek']
Y_Axis['Number', 'of', 'triple', 'doubles']: ['181', '146', '138', '107', '92', '78', '59', '45', '43', '39', '33', '32', '31']

gold: Which player has the most triple doubles ? Oscar Robertson - nicknamed ‘ The Big O ' _ , is the all-time leader in triple doubles in the National Basketball Assocation . He compiled 181 triple doubles during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active player is Russell Westbrook of the Oklahoma City Thunder with 144 triple doubles in second place .
gold_template: Which templateXLabel[0] has the most templateYLabel[1] templateYLabel[2] ? templateXValue[0] - nicknamed ‘ The Big O ' _ , is the templateTitle[3] leader in templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] Assocation . He compiled templateYValue[max] templateYLabel[1] templateYLabel[2] during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active templateXLabel[0] is templateXValue[1] of the Oklahoma City Thunder with 144 templateYLabel[1] templateYLabel[2] in second place .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2019 , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , there were templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[0] .
generated: The statistic shows the Number of the National Basketball Association all-time in the triple as of 2019 , leaders Player .  In 1946 , there were 181 triple doubles in Oscar_Robertson .

Example 258:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used paint brands in the U.S. 2018
X_Axis['Brand']: ['Sherwin-Williams', 'Benjamin_Moore', 'Behr_Paint_Cooperation', 'Kelly_Moore', 'Valspar', 'PPG_Pittsburgh_Paints', 'Zar_(United_Gilsonite_Labs)', 'Devoe_&_Raynolds', 'Dutch_Boy', 'Olympic', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['49.5', '22.4', '12.1', '2.8', '1.9', '1.9', '1.9', '0.9', '0.9', '0.9', '4.7']

gold: This statistic depicts paints used the most by U.S. construction firms in 2018 . The survey revealed that 49.5 percent of the respondents used Sherwin-Williams brand paints the most .
gold_template: This statistic depicts templateXValue[5] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateXValue[5] the templateTitle[0] .

generated_template: This statistic displays a ranking of the templateTitle[0] by templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] . The templateXLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateXValue[0] was the templateTitle[0] basis .
generated: This statistic displays a ranking of the Most by U.S. brands in 2018 .  The Brand respondents of the Most used paint brands in 2018 , Sherwin-Williams was the Most basis .

Example 259:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Sales growth of the top U.S. cosmetic brands 2014
X_Axis['Brand/Segment']: ['Neutrogena/_makeup_remover_implements', 'CoverGirl_Last_Blast/_mascara', 'Revlon_Super_Lustrous/_lipstick', "L'Oréal_Voluminous/_mascara", "Maybelline_Volum'Express_Falsies/_mascara", 'CoverGirl_Clean/_powder', 'Revlon_ColorStay/_foundation', 'Maybelline_Great_Lash/_mascara', 'CoverGirl_Outlast/_lipstick', 'CoverGirl_Clean/_foundation', 'Revlon_ColorStay/_eyeliner', "L'Oréal_True_Match/_foundation", "L'Oréal_Colour_Riche/_lipstick", 'CoverGirl_Perfect_Point_Plus/_eyeliner', "Maybelline_Volum'Express_Colossal/_mascara", 'Maybelline_Color_Sensational/_lipstick', "L'Oréal_True_Match/_powder", 'Maybelline_Expert_Wear/_eyeshadow', "Maybelline_Volum'Express_Rocket/_mascara", 'CoverGirl_Eye_Enhancers/_eyeshadow']
Y_Axis['Percent', 'sales', 'change']: ['23.7', '-14.1', '9.9', '2.9', '-17.2', '-2.4', '9.9', '-9.6', '2', '-3.3', '-6.5', '-0.6', '-2.8', '-0.4', '5.4', '14.9', '5.4', '-7.5', '144', '-8.7']

gold: The statistic shows the sales growth of the leading cosmetic brands in 2014 . Neutrogena 's makeup remover implements saw a 23.7 percent sales increase while Maybelline 's Volum'Express Rocket mascara experienced a 144 percent increase compared to last year .
gold_template: The statistic shows the templateYLabel[1] templateTitle[1] of the leading templateTitle[4] templateTitle[5] in templateTitleDate[0] . Neutrogena 's templateXValue[0] implements saw a templateYValue[0] templateScale templateYLabel[1] templatePositiveTrend while templateXValue[4] 's templateXValue[4] Rocket templateXValue[1] experienced a templateYValue[max] templateScale templatePositiveTrend compared to templateXValue[1] year .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of 2019 . As of that year , there were a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] .
generated: The statistic shows the Percent of the top U.S. cosmetic growth top U.S. cosmetic brands 2014 as of 2019 .  As of that year , there were a Percent of 144 percent change .

Example 260:
titleEntities: {'Subject': ['Viki'], 'Date': ['2012', '2015']}
title: Viki : number of monthly active users 2012 to 2015
X_Axis['Month']: ["Mar_'12", "Jun_'12", "Sep_'12", "Dec_'12", "Mar_'13", "Jun_'13", "Sep_'13", "Dec_'13", "Mar_'14", "Jun_'14", "Sep_'14", "Dec_'14", "Mar_'15", "Jun_'15"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['3.8', '4.9', '7.3', '14.9', '15.9', '19.9', '23.9', '28.2', '29.3', '31.9', '35.2', '39.4', '40.1', '39.5']

gold: This statistic presents the number of monthly active Viki video platform users as of June 2015 . As of that month , the video portal had 39.5 million monthly active users worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitleSubject[0] video platform templateYLabel[3] as of 2015 . As of that templateXLabel[0] , the video portal had templateYValue[last] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in 2013 .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitleSubject[0] templateYLabel[3] worldwide as of 2019 . As of that templateXLabel[0] , the mobile messaging app announced more than templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from over templateYValue[1] templateScale MAU in 2016 . The service is one of the most popular mobile apps worldwide .
generated: This statistic shows the Viki Number of the Viki monthly active Viki users worldwide as of 2019 .  As of that Month , the mobile messaging app announced more than 40.1 millions monthly active users , up from over 4.9 millions MAU in 2016 .  The service is one of the most popular mobile apps worldwide .

Example 261:
titleEntities: {'Subject': ['March'], 'Date': ['2', '2020']}
title: COVID-19 cases worldwide as of March 2 , 2020 , by country
X_Axis['Country']: ['Total_(worldwide)', 'China', 'Republic_of_Korea', 'Italy', 'Iran_(Islamic_Republic_of)', 'Cases_on_an_international_conveyance_(Japan)', 'Japan', 'Germany', 'Singapore', 'France', 'United_States_of_America', 'Kuwait', 'Bahrain', 'Spain', 'Thailand', 'United_Kingdom', 'Australia', 'Malaysia', 'Switzerland', 'United_Arab_Emirates', 'Norway', 'Iraq', 'Canada', 'Viet_Nam', 'Sweden', 'Netherlands', 'Lebanon', 'Austria', 'Israel', 'Croatia', 'Greece', 'Oman', 'Finland', 'Mexico', 'Pakistan', 'Denmark', 'India', 'Czechia', 'Romania', 'Georgia', 'Philippines', 'Azerbaijan', 'Qatar', 'Indonesia', 'Iceland', 'Egypt', 'Brazil', 'Russian_Federation', 'Armenia', 'Ecuador', 'Dominican_Republic', 'Estonia', 'Ireland', 'Lithuania', 'Luxembourg', 'Monaco', 'Algeria', 'New_Zealand', 'Cambodia', 'North_Macedonia', 'San_Marino', 'Nepal', 'Sri_Lanka', 'Afghanistan', 'Nigeria', 'Belarus', 'Belgium']
Y_Axis['Number', 'of', 'cases']: ['88948', '80174', '4212', '1689', '978', '706', '254', '129', '106', '100', '62', '56', '47', '45', '42', '36', '27', '24', '24', '21', '19', '19', '19', '16', '14', '13', '10', '10', '7', '7', '7', '6', '6', '5', '4', '4', '3', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: As of March 2 , 2020 , the outbreak of the coronavirus disease ( COVID-19 ) had been confirmed in 65 countries , with the overwhelming majority of cases reported in China . The virus had infected 88,948 people worldwide , and the number of deaths had totaled 3,043 . The most severely affected countries outside of China were the Republic of Korea and Italy .
gold_template: As of templateTitleSubject[0] templateYValue[43] , templateTitleDate[0] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[3] templateTitle[4] of templateYLabel[1] templateTitle[1] templateTitle[2] from 2000 to templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[14] unique templateTitle[4] of templateTitle[0] templateTitle[1] were templateTitle[3] templateTitle[4] .
generated: This statistic shows the Number of March 2 of cases worldwide from 2000 to 2 , 2020 Country .  In 2 , about 42 unique 2 of COVID-19 cases were March 2 .

Example 262:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Most followed sports leagues in the U.S. 2019
X_Axis['Response']: ['NFL', 'MLB', 'NBA', 'NHL', 'MLS', "I_don't_follow_any_of_these_leagues"]
Y_Axis['Share', 'of', 'respondents']: ['33', '16', '10', '5', '3', '32']

gold: There are widely considered to be four major professional men 's sports leagues in the United States and Canada - NFL , NBA , MLB , and NHL . The professional soccer league ( MLS ) has also achieved some popularity in the United States in recent years . During a 2019 survey , 33 percent of respondents stated that the National Football League , NFL , was their favorite men 's U.S. professional sports league to follow .
gold_template: There are widely considered to be four major professional men 's templateTitle[2] templateXValue[last] in the templateTitle[4] and Canada - templateXValue[0] , templateXValue[2] , templateXValue[1] , and templateXValue[3] . The professional soccer league ( templateXValue[4] ) has also achieved some popularity in the templateTitle[4] in recent years . During a templateTitleDate[0] survey , templateYValue[max] templateScale of templateYLabel[1] stated that the National Football League , templateXValue[0] , was their favorite men 's templateTitleSubject[0] professional templateTitle[2] league to templateXValue[last] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitle[6] as of 2013 . During the survey , templateYValue[1] templateScale of templateYLabel[1] stated that they templateXValue[0] a templateXValue[1] and templateXValue[2] .
generated: This statistic shows the Most followed sports leagues U.S. in the 2019 as of 2013 .  During the survey , 16 % of respondents stated that they NFL a MLB and NBA .

Example 263:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Pinterest usage reach in the United States 2019 , by household income
X_Axis['Annual', 'household', 'income']: ['Under_$30000', '$30000-$74999', '$75000+']
Y_Axis['Reach']: ['18', '27', '41']

gold: This statistic shows the share of adults in the United States who were using Pinterest as of February 2019 , sorted by income . During that period of time , 18 percent of respondents earning 30,000 U.S. dollars or less used the social networking site .
gold_template: This statistic shows the share of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateXLabel[2] . During that period of time , templateYValue[min] templateScale of respondents earning 30,000 templateTitle[4] dollars or less used the social networking site .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , templateTitle[6] templateTitle[7] templateTitle[8] . During the survey , templateYValue[max] templateScale of templateYLabel[1] stated they used the templateXLabel[0] templateXLabel[1] templateXLabel[2] or less said that they smoke templateTitle[4] .
generated: This statistic shows the Reach of adults in the United States who were using Pinterest as of 2019 , by household income .  During the survey , 41 % of Reach stated they used the Annual household income or less said that they smoke States .

Example 264:
titleEntities: {'Subject': ['Market'], 'Date': ['2016', '2019']}
title: Market capitalization of leading 100 banks worldwide 2016 to 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16"]
Y_Axis['Market', 'capitalization', 'in', 'trillion', 'Euros']: ['5.3', '5.2', '5.2', '4.8', '5.3', '5.2', '5.4', '5.6', '5.4', '5.3', '5.4', '5.2', '4.4', '4.1', '4.2']

gold: This statistic presents the market capitalization of leading 100 banks worldwide from the first quarter 2016 to the third quarter 2019 . The market cap of top 100 global banks amounted to 5.3 trillion Euros in the third quarter of 2019 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the first templateXLabel[0] templateTitleDate[min] to the third templateXLabel[0] templateTitleDate[max] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateScale templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[max] .

generated_template: This statistic shows the templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , the templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] templateScale templateYLabel[3] ( e.g ) ) .
generated: This statistic shows the capitalization leading 100 Market from the first Quarter of 2016 to the fourth Quarter of 2019 .  In the fourth Quarter of 2019 , the capitalization trillion amounted to 5.6 trillion Euros ( e.g ) .

Example 265:
titleEntities: {'Subject': ['American'], 'Date': ['2013']}
title: Frequency of American families having dinner together at home 2013
X_Axis['Response']: ['0_to_3_nights', '4_to_5_nights', '6_to_7_nights']
Y_Axis['Share', 'of', 'respondents']: ['21', '28', '53']

gold: This statistic shows the results of a survey , conducted in 2013 , among adult Americans on the frequency of having dinner at home as a family . In December 2013 , 53 percent of the respondents answered that their family eat dinner together at home on 6 to 7 nights a week .
gold_template: This statistic shows the results of a survey , conducted in templateTitleDate[0] , among adult Americans on the templateTitle[0] of templateTitle[3] templateTitle[4] at templateTitle[6] as a family . In 2013 , templateYValue[max] templateScale of the templateYLabel[1] answered that their family eat templateTitle[4] templateTitle[5] at templateTitle[6] on templateXValue[last] to templateXValue[last] templateXValue[0] a week .

generated_template: As of early templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] that templateXValue[0] , followed by templateXValue[1] and templateXValue[2] . Of templateYLabel[1] stated that they templateXValue[6] templateYValue[max] templateScale watched templateXValue[1] at templateYValue[1] templateScale British pounds each templateXValue[1] .
generated: As of early 2013 , 0_to_3_nights and 4_to_5_nights were the most families having dinner together home 2013 that 0_to_3_nights , followed by 4_to_5_nights and 6_to_7_nights .  Of respondents stated that they 6_to_7_nights 53 % watched 4_to_5_nights at 28 million British pounds each 4_to_5_nights .

Example 266:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Preferred modes of transportation when taking a family vacation in the U.S. 2015
X_Axis['Preferred', 'mode', 'of', 'travel']: ['Car', 'Plane', 'RV', 'Train', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['63', '31', '3', '2', '1']

gold: The statistic shows the preferred ways to travel when taking a family vacation in the United States in 2015 . The survey revealed that 63 percent of respondents prefer to travel by car .
gold_template: The statistic shows the templateXLabel[0] ways to templateXLabel[2] templateTitle[3] templateTitle[4] a templateTitle[5] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of templateYLabel[1] prefer to templateXLabel[2] by templateXValue[0] .

generated_template: This statistic presents the templateYLabel[0] of internet users in the templateTitle[0] who were using templateTitleSubject[0] as of 2020 . During the survey period of time , templateYValue[max] templateScale of templateYLabel[1] stated they have a very popular social networking site .
generated: This statistic presents the Share of internet users in the Preferred who were using U.S. as of 2020 .  During the survey period of time , 63 % of respondents stated they have a very popular social networking site .

Example 267:
titleEntities: {'Subject': ['Electronic Arts'], 'Date': ['2010', '2020']}
title: Quarterly revenue of Electronic Arts from Q3 2010 to Q2 2020
X_Axis['Quarter']: ["Q2_'20", "Q1_'20", "Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1348', '1209', '1238', '1289', '1286', '1137', '1582', '1160', '959', '1449', '1527', '1149', '898', '1271', '1308', '1070', '815', '1203', '1185', '1126', '990', '1214', '1123', '808', '695', '949', '1209', '922', '711', '955', '1368', '1061', '715', '999', '1090', '1053', '631', '815', '979', '1243']

gold: This time series depicts the quarterly revenue of Electronic Arts from the third quarter of the fiscal year 2010 to the second quarter of the fiscal year 2020 . In the second fiscal quarter of 2020 , which ended on September 30 , 2019 , Electronic Arts generated a net revenue of 1.35 billion U.S. dollars . Here you can find information about EA 's quarterly net income .
gold_template: This time series depicts the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] from the third templateXLabel[0] of the fiscal year templateTitleDate[min] to the second templateXLabel[0] of the fiscal year templateTitleDate[max] . In the second fiscal templateXLabel[0] of templateTitleDate[max] , which ended on 30 , 2019 , templateTitleSubject[0] generated a net templateYLabel[0] of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: This statistic contains data on the global templateYLabel[0] of templateTitle[0] templateTitle[4] the fourth templateXLabel[0] of 2012 to the third templateXLabel[0] of templateTitleDate[max] . In the third templateXLabel[0] of templateTitleDate[max] , the social gaming company generated a total templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic contains data on the global Revenue of Quarterly from the fourth Quarter of 2012 to the third Quarter of 2020 .  In the third Quarter of 2020 , the social gaming company generated a total Revenue of 1582 million U.S. dollars .

Example 268:
titleEntities: {'Subject': ['Leading'], 'Date': ['2014']}
title: Leading global travel booking sites by number of page visits 2014
X_Axis['Month']: ['Booking.com', 'TripAdvisor_Family', 'Expedia_Family', 'Hotels.com', 'Priceline.com', 'Agoda.com', 'Hotelurbano', 'Kayak.com', 'Travel.yahoo.com', 'Cheapoair.com', 'Makemytrip.com', 'Orbitz.com', 'Travelocity', 'Hotwire.com', 'Airbnb.com', 'Travelzoo.com', 'Decolar.com', 'Slyscanner.com', 'Ctrip.com', 'HomeAway.com']
Y_Axis['Number', 'of', 'site', 'visits', 'in', 'millions']: ['166.0', '159.9', '59.3', '34.5', '31.3', '30.7', '25.5', '24.4', '24.1', '20.2', '17.5', '17.2', '15.0', '13.2', '12.4', '12.2', '11.3', '9.6', '8.6', '7.4']

gold: This statistic shows the number of visits to travel booking sites worldwide in January 2014 . Booking.com had the most visits in January 2014 , with an estimated number of visits of 166 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to templateTitle[2] templateTitle[3] templateTitle[4] worldwide in 2014 . templateXValue[0] had the most templateYLabel[2] in 2014 , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] templateScale .

generated_template: This statistic shows the templateYLabel[0] of times templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] as of 2018 . As of that templateXLabel[0] , the website had a templateYLabel[0] of templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of times Leading global travel booking in Leading as of 2018 .  As of that Month , the website had a Number of 159.9 site visits .

Example 269:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : most important issues facing women and girls in 2019
X_Axis['Response']: ['Sexual_harassment', 'Sexual_violence', 'Physical_violence', 'Domestic_abuse', 'Equal_pay', 'Workplace_discrimination', 'Gender_stereotyping', 'Sexualization_of_women_and_girls_in_the_media', 'Access_to_employment', 'Balancing_work_and_caring_responsibilities', 'Lack_of_women_in_leadership_roles_in_business_and_public_life', 'Abuse_on_social_media', 'Support_for_pregnant_women_and_new_mothers', 'The_amount_of_unpaid_work_that_women_do_(e.g._cooking_cleaning_childcare)', 'Lack_of_financial/economic_independence']
Y_Axis['Share', 'of', 'respondents']: ['40', '37', '32', '19', '19', '18', '16', '15', '9', '8', '7', '7', '6', '6', '5']

gold: The statistic presents the results of a survey conducted in December 2018 and January 2019 to find out about the situation of women and gender ( in ) equality across 27 countries . When asked which were the main issues that women and girls were facing in Mexico , 40 percent of respondents answered sexual harassment .
gold_template: The statistic presents the results of a survey conducted in 2018 and 2019 to find out about the situation of templateXValue[7] and templateXValue[6] ( in ) equality across 27 countries . When asked which were the main templateTitle[3] templateXValue[13] templateXValue[7] and templateXValue[7] were templateTitle[4] in templateTitleSubject[0] , templateYValue[max] templateScale of templateYLabel[1] answered templateXValue[0] .

generated_template: The statistic shows the results of a survey about the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in 2017 , followed by templateXValue[1] and templateXValue[1] . At that time , templateXValue[2] stated that they have a very or somewhat templateXValue[1] .
generated: The statistic shows the results of a survey about the most important issues facing women in 2017 , followed by Sexual_violence and .  At that time , Physical_violence stated that they have a very or somewhat Sexual_violence .

Example 270:
titleEntities: {'Subject': ['Instagram', 'United States'], 'Date': ['2019']}
title: Instagram usage reach in the United States 2019 , by age group
X_Axis['Age', 'group']: ['18-29', '30-49', '50-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['67', '47', '23', '8']

gold: As of February 2019 , 67 percent of U.S. adults aged between 18 and 29 years used the photo sharing app Instagram . Furthermore , it was found that 43 percent of female adults in the United States used Instagram compared to only 31 percent of adult men . Instagram usage in the United StatesInstagram is one of the most popular social networks in the United States with a 37 percent usage reach among the adult population .
gold_template: As of 2019 , templateYValue[max] templateScale of templateTitle[4] adults aged between 18 and 29 years used the photo sharing app templateTitleSubject[0] . Furthermore , it was found that 43 templateScale of female adults in the templateTitleSubject[1] used templateTitleSubject[0] compared to only 31 templateScale of adult men . templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] StatesInstagram is one of the most popular social networks in the templateTitleSubject[1] with a 37 templateScale templateTitle[1] templateTitle[2] among the adult population .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the Instagram who were using Instagram as of 2019 , sorted by age .  During that period of time , 67 % of female respondents stated that they used the social networking site .

Example 271:
titleEntities: {'Subject': ['European'], 'Date': []}
title: European football clubs average attendance 2013/14
X_Axis['Club', 'Name']: ['Boussia_Dortmund', 'Manchester_United', 'Barcelona', 'Real_Madrid', 'Bayern_Munich', 'Schalke_04', 'Arsenal', 'Borussia_Mönchengladbach', 'Hertha_BSC', 'Hamburger_SV', 'Ajax_Amsterdam', 'VfB_Stuttgart', 'Newcastle_United', 'Manchester_City', 'Eintracht_Frankfurt', 'Celtic_FC', 'FC_Internazionale', 'Atletico_Madrid', 'FC_Köln', 'Feyenoord', 'Hannover_96', 'Paris_Saint_Germain', 'Liverpool', 'SL_Benfica', 'Rangers_FC']
Y_Axis['Average', 'attendance']: ['80295', '75205', '72115', '71565', '71000', '61750', '60015', '52240', '51890', '51825', '50905', '50500', '50395', '47075', '47055', '46810', '46245', '46245', '46235', '45755', '45635', '45420', '44670', '43615', '42935']

gold: The statistic shows the European football clubs with the highest average per game attendance in the 2013/14 season . Germany 's Borussia Dortmund had the highest average attendance throughout Europe , with an average of over 80,000 fans attending each of their home games .
gold_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] with the highest templateYLabel[0] per game templateYLabel[1] in the templateTitle[5] season . Germany 's templateXValue[7] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] throughout Europe , with an templateYLabel[0] of over 80,000 fans attending each of their home games .

generated_template: This graph shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from the fourth quarter of the templateTitle[6] as of templateTitleDate[0] . As of the third quarter templateTitleDate[0] , templateXValue[0] had the highest active templateXValue[1] .
generated: This graph shows the European football clubs average from the fourth quarter of the 2013/14 as of .  As of the third quarter , Boussia_Dortmund had the highest active Manchester_United .

Example 272:
titleEntities: {'Subject': ['Iran'], 'Date': ['2011']}
title: Iran 's oil exports 2011
X_Axis['Country']: ['China', 'European_Union_(total)', 'Japan', 'India', 'South_Korea', 'Italy', 'Turkey', 'Spain', 'France', 'Netherlands', 'Germany', 'United_Kingdom']
Y_Axis['Oil', 'imports', 'in', 'thousand', 'barrels', 'per', 'day']: ['543', '450', '341', '328', '244', '183', '182', '137', '49', '33', '17', '11']

gold: This statistic depicts the volume of crude oil imported from Iran by its leading destination countries between January and June 2011 . The European Union imported a total of around 450,000 barrels of oil per day from Iran during that period . Iran has stopped oil exports to France , where crude oil is the second most important energy source and Britain , where crude oil production has been declining since 2002 .
gold_template: This statistic depicts the volume of crude templateYLabel[0] imported from templateTitleSubject[0] by its leading destination countries between and 2011 . The templateXValue[1] imported a total of around templateYValue[1] templateYLabel[3] of templateYLabel[0] templateYLabel[4] templateYLabel[5] from templateTitleSubject[0] during that period . templateTitleSubject[0] has stopped templateYLabel[0] templateTitle[3] to templateXValue[8] , where crude templateYLabel[0] is the second most important energy source and Britain , where crude templateYLabel[0] production has been declining since 2002 .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] worldwide as of 2019 , sorted by templateXLabel[0] . As of that year , the templateXValue[0] had a templateYLabel[0] of templateYValue[max] templateScale of templateTitle[6] .
generated: This statistic shows the Oil of the imports thousand worldwide as of 2019 , sorted by Country .  As of that year , the China had a Oil of 543 thousand of 2011 .

Example 273:
titleEntities: {'Subject': ['Samsung Electronics'], 'Date': ['2009', '2019']}
title: Samsung Electronics ' operating profit 2009 - 2019 , by quarter
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09"]
Y_Axis['Operating', 'profit', 'in', 'trillion', 'South', 'Korean', 'won']: ['7.16', '7.78', '6.6', '6.23', '10.8', '17.57', '14.87', '15.64', '15.15', '14.53', '14.07', '9.9', '9.22', '5.2', '8.14', '6.68', '6.14', '7.39', '6.9', '5.98', '5.29', '4.06', '7.2', '8.5', '8.3', '10.2', '9.5', '8.8', '8.8', '8.1', '6.5', '5.7', '4.7', '4.3', '3.8', '2.8', '3.0', '4.9', '5.0', '4.4', '3.4', '4.2', '2.7', '0.6']

gold: In the fourth quarter of 2019 , Korean consumer electronics company Samsung Electronics reported an operating profit of nearly 7.16 trillion Korean Won or around 6.5 billion U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third quarter of 2019 , but growing competition throughout the consumer electronics industry meant that profitability fell . Samsung Samsung ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer electronics products .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateYLabel[4] consumer templateTitleSubject[0] company templateTitleSubject[0] reported an templateYLabel[0] templateYLabel[1] of nearly templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] or around templateYValue[30] templateScale U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third templateXLabel[0] of templateTitleDate[max] , but templatePositiveTrend competition throughout the consumer templateTitleSubject[0] industry meant that profitability templateNegativeTrend . templateTitleSubject[0] ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer templateTitleSubject[0] products .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , the mobile mobile internet users had an templateTitle[1] templateYLabel[0] of templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Electronics ' operating of Samsung Electronics from the first Quarter of 2009 to the fourth Quarter of 2019 .  In the fourth Quarter of 2019 , the mobile internet users had an Electronics Operating of 7.16 trillion South Korean .

Example 274:
titleEntities: {'Subject': ['Bitcoins'], 'Date': ['2012', '2019']}
title: Number of Bitcoins in circulation 2012 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12"]
Y_Axis['Number', 'of', 'Bitcoins', 'in', 'millions']: ['18.13', '17.97', '17.79', '17.62', '17.45', '17.3', '17.12', '16.95', '16.78', '16.6', '16.42', '16.25', '16.08', '15.9', '15.72', '15.38', '15.03', '14.67', '14.33', '14.0', '13.67', '13.33', '12.97', '12.59', '12.2', '11.77', '11.35', '10.97', '10.61']

gold: In the fourth quarter of 2019 , there were 18.13 million Bitcoins in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , there were templateYValue[max] templateScale templateYLabel[1] in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] from the fourth templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] was a 3.88 templateScale of Japanese e-commerce company .
generated: This statistic shows the Number of Bitcoins circulation from the fourth Quarter of 2012 to the fourth Quarter of 2019 .  In the fourth Quarter of 2019 , Bitcoins 's millions was a 3.88 millions of Japanese e-commerce company .

Example 275:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2019']}
title: NCAA division I men 's basketball attendance leaders 2019
X_Axis['State']: ['Syracuse', 'Kentucky', 'North_Carolina', 'Tennessee', 'Wisconsin', 'Louisville', 'Kansas', 'Creighton', 'Marquette', 'Nebraska', 'Arkansas', 'Indiana', 'Michigan_St.', 'Perdue', 'Iowa_St.', 'Virginia', 'Memphis', 'Maryland', 'Ohio_St.', 'NC_State', 'Arizona', 'Dayton', 'Iowa', 'Michigan', 'Illinois', 'Texas_Tech', 'BYU', 'South_Carolina', 'Cincinnati', 'New_Mexico']
Y_Axis['Average', 'attendance']: ['21992', '21695', '19715', '19034', '17170', '16601', '16236', '15980', '15611', '15341', '15278', '15206', '14797', '14467', '14099', '14087', '14065', '14009', '13922', '13897', '13744', '12957', '12869', '12505', '12456', '12098', '11958', '11472', '11256', '11107']

gold: While the players on the court might still be college students , the National Collegiate Athletic Association men 's basketball top division still draws in big crowds . The Syracuse Orange men 's basketball , which represents Syracuse University in New York , attracted the highest average attendance during the 2019 season . The team , traditionally known as the Syracuse Orangemen , had an average home audience of almost 22 thousand in 2019 .
gold_template: While the players on the court might still be college students , the National Collegiate Athletic Association templateTitle[3] templateTitle[4] templateTitle[5] top templateTitle[1] still draws in big crowds . The templateXValue[0] Orange templateTitle[3] templateTitle[4] templateTitle[5] , which represents templateXValue[0] University in templateXValue[last] York , attracted the highest templateYLabel[0] templateYLabel[1] during the templateTitleDate[0] season . The team , traditionally known as the templateXValue[0] Orangemen , had an templateYLabel[0] home audience of almost templateYValue[max] thousand in templateTitleDate[0] .

generated_template: This statistic presents the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[7] as of 2017 , by templateXLabel[0] . As of templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale .
generated: This statistic presents the NCAA division I men in the leaders as of 2017 , by State .  As of 2019 , Syracuse had the highest Average attendance , followed by Kentucky with 21695 % .

Example 276:
titleEntities: {'Subject': ['Croatian'], 'Date': []}
title: Leading Croatian national team players at FIFA World Cup 2018 , by market value
X_Axis['Month']: ['Ivan_Rakitic', 'Ivan_Perisic', 'Mateo_Kovacic', 'Andrej_Kramaric', 'Marcelo_Brozovic', 'Luka_Modric', 'Sime_Vrsaljko', 'Dejan_Lovren', 'Mario_Mandzukic', 'Milan_Badelj', 'Marko_Pjaca', 'Nikola_Kalinic', 'Ante_Rebic', 'Duje_Caleta–Car', 'Domagoj_Vida', 'Lovre_Kalinic', 'Tin_Jedvaj', 'Danijel_Subasic', 'Vedran_Corluka', 'Ivan_Strinic', 'Filip_Bradaric', 'Josip_Pivaric', 'Dominik_Livakovic']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['50.0', '40.0', '30.0', '27.0', '27.0', '25.0', '25.0', '20.0', '18.0', '15.0', '15.0', '14.0', '10.0', '10.0', '7.0', '6.5', '5.0', '4.5', '4.0', '4.0', '3.5', '2.0', '1.5']

gold: The statistic displays the leading players of the national football team of Croatia at FIFA World Cup as of June 2018 , by market value . The most valuable player was Ivan Rakitic , with a market value of 50 million euros .
gold_template: The statistic displays the templateTitle[0] templateTitle[4] of the templateTitle[2] football templateTitle[3] of Croatia at templateTitle[5] templateTitle[6] templateTitle[7] as of 2018 , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] from 1900 to 2017 . As of the last reported period , the number of templateTitleSubject[0] had a player templateTitle[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateYLabel[0] templateYLabel[2] had templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic shows the Market value of the national team players Croatian from 1900 to 2017 .  As of the last reported period , the number of Croatian had a player Croatian Market value million players Market million had 50.0 million euros .

Example 277:
titleEntities: {'Subject': ['Distribution'], 'Date': ['2018']}
title: Distribution of consumer transactions worldwide 2018 , by payment channel
X_Axis['Response']: ['In-store', 'Other_online', 'Buy_buttons', 'Other_mobile_transfers', 'P2P_transfer', 'Mobile_messenger_apps', 'QR_codes', 'Other_in-app_payments', 'Smart_home_device', 'Wearables_/_contactless', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['41', '14', '9', '8', '7', '7', '5', '4', '2', '2', '2']

gold: This statistic presents the most popular payment methods for everyday transactions according to internet users worldwide as of June 2018 . When asked to think about they payment methods for their ten most recent transactions , it was found that seven percent were made via P2P transfer . In-store still accounted for the single largest share of everyday transactions with 41 percent .
gold_template: This statistic presents the most popular templateTitle[6] methods for everyday templateTitle[2] according to internet users templateTitle[3] as of 2018 . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] templateScale were made via templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] templateScale .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2013 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] named watching templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] for templateTitleSubject[0] .
generated: The statistic shows the Distribution consumer transactions worldwide 2018 by in the payment as of 2013 .  During the survey , 41 % of the respondents named watching In-store as their Distribution preferred activity during transactions for Distribution .

Example 278:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: Solar PV capacity - new installations worldwide by country 2018
X_Axis['Country']: ['China', 'India', 'US', 'Japan', 'Australia', 'Germany', 'Mexico', 'Republic_of_Korea', 'Turkey', 'Netherlands']
Y_Axis['Percentage', 'of', 'newly', 'installed', 'capacity']: ['45', '11', '11', '7', '4', '3', '3', '2', '2', '1']

gold: This statistic shows the share of new installed solar PV capacity worldwide in 2018 , by country . In 2018 , new solar PV capacity installations in China accounted for around 45 percent of the world 's total new installed grid-connected PV capacity .
gold_template: This statistic shows the share of templateTitle[3] templateYLabel[2] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateTitle[3] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[4] in templateXValue[0] accounted for around templateYValue[max] templateScale of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitleSubject[0] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] as of October templateTitleDate[0] , templateTitle[4] templateXLabel[0] . During that period , there were templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[0] .
generated: This statistic shows the Percentage of the PV newly capacity new as of October 2018 , installations Country .  During that period , there were 11 newly installed in China .

Example 279:
titleEntities: {'Subject': ['China'], 'Date': ['2013', '2018']}
title: China smartphone unit shipments 2013 to 2018
X_Axis['Quarter']: ['Q1_2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Shipments', 'in', 'million', 'units']: ['109.6', '454.4', '448.5', '385.3', '392.8', '359.0']

gold: The statistic shows the smartphone unit shipments in China from 2013 to Q1 2018 . In Q1 2018 , 109.6 million smartphones were shipped in China .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[min] templateScale smartphones were shipped in templateTitleSubject[0] .

generated_template: This statistic gives information on the templateYLabel[0] of templateTitleSubject[0] templateYLabel[2] templateTitle[1] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the last reported period , there were templateYValue[0] templateScale templateYLabel[3] in the fourth templateXLabel[0] of templateXValue[1] .
generated: This statistic gives information on the Shipments of China units smartphone from the first Quarter of 2013 to the fourth Quarter of 2018 .  In the last reported period , there were 109.6 million units in the fourth Quarter of 2017 .

Example 280:
titleEntities: {'Subject': ['eBay'], 'Date': ['2014', '2019']}
title: eBay : quarterly classifieds revenue 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['269', '265', '271', '256', '263', '254', '259', '246', '244', '235', '219', '199', '201', '197', '207', '186', '183', '178', '180', '162', '180']

gold: eBay 's classifieds revenue in the fourth quarter of 2019 amounted to 269 million U.S. dollars . This represents a three percent year-on-year change . The classifieds revenue is counted towards the company 's marketing services and other revenues segment .
gold_template: templateTitleSubject[0] 's templateTitle[2] templateYLabel[0] in the fourth templateXLabel[0] of templateTitleDate[max] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . This represents a three templateScale year-on-year change . The templateTitle[2] templateYLabel[0] is counted towards the company 's marketing services and other revenues segment .

generated_template: As of the fourth templateXLabel[0] of templateTitleDate[max] , the chat app company templateTitleSubject[0] had generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . In templateTitleDate[max] , templateTitleSubject[0] 's annual templateYLabel[0] amounted to close to 1.7 templateScale templateYLabel[2] templateYLabel[3] , up from 1.18 templateScale in the preceding year .
generated: As of the fourth Quarter of 2019 , the chat app company eBay had generated 271 million U.S. dollars in Revenue , up from 265 million U.S. dollars in the previous Quarter .  In 2019 , eBay 's annual Revenue amounted to close 1.7 million U.S. dollars , up from 1.18 million in the preceding year .

Example 281:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest population decline rate 2017
X_Axis['Country']: ['Cook_Islands', 'Puerto_Rico', 'American_Samoa', 'Lebanon', 'Saint_Pierre_and_Miquelon', 'Latvia', 'Lithuania', 'Moldova', 'Bulgaria', 'Estonia', 'Federated_States_of_Micronesia', 'Northern_Mariana_Islands', 'Croatia', 'Serbia', 'Ukraine', 'Romania', 'Slovenia', 'Cuba', 'Montenegro', 'Virgin_Islands']
Y_Axis['Population', 'decline', 'compared', 'to', 'the', 'previous', 'year']: ['2.79', '1.74', '1.3', '1.1', '1.08', '1.08', '1.08', '1.05', '0.61', '0.57', '0.52', '0.51', '0.5', '0.46', '0.41', '0.33', '0.31', '0.29', '0.28', '0.25']

gold: This statistic shows the 20 countries with the highest population decline rate in 2017 . In the Cook Islands , the population decreased by about 2.8 percent compared to the previous year , making it the country with the highest population decline rate in 2017 . The population decline of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the country to cross into surrounding countries such as Turkey .
gold_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In the templateXValue[0] , the templateYLabel[0] templateNegativeTrend by about templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the templateXLabel[0] to cross into surrounding templateTitleSubject[0] such as Turkey .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateYLabel[2] in templateTitle[3] as of 2020 , sorted by templateXLabel[0] . In that year , templateTitle[1] templateYLabel[2] in templateXValue[0] was the templateTitle[2] templateXLabel[0] .
generated: This statistic shows the Population of the highest compared in decline as of 2020 , sorted by Country .  In that year , highest compared in Cook_Islands was the population Country .

Example 282:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Percentage of U.S. companies using self-insured health plans for employees 2010
X_Axis['Number', 'of', 'employees']: ['3_to_49', '50_to_199', '200_to_999', '1000_and_more']
Y_Axis['Share', 'of', 'companies']: ['8', '20', '48', '80']

gold: This statistic shows the percentage of U.S. companies using self-insured health plans for employees in 2010 , by the number of employees . 80 percent of companies with 1,000 and more employees used self-insured health plans in 2010 .
gold_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] . In that period of time , templateYValue[max] templateScale of female templateYLabel[1] templateYLabel[2] had attained a templateXValue[1] basis .
generated: This statistic shows the Share of adults in the U.S. who were using Percentage as of 2019 , sorted plans Number .  In that period of time , 80 % of female companies had attained a 50_to_199 basis .

Example 283:
titleEntities: {'Subject': ['U.S. Instagram'], 'Date': ['2015', '2015']}
title: Share of U.S. teenagers who use Instagram 2015 , by gender and age
X_Axis['Response']: ['Boys_13-14', 'Boys_15-17', 'Girls_13-14', 'Girls_15-17']
Y_Axis['Share', 'of', 'respondents']: ['33', '51', '56', '64']

gold: This statistic shows the share of teenagers in the United States who were Instagram users as of March 2015 , sorted by gender and age group . During that period of time , 64 percent of female U.S. teens aged 15 to 17 years used the social networking app .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of 2015 , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] templateScale of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .

generated_template: This statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books . Approximately templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateTitle[6] .
generated: This statistic shows the results of a survey about the teenagers who of several individual Share Girls_15-17 in U.S. Instagram 2015 , phrased Boys_15-17 the question : `` except reading books .  Approximately 64 % of the respondents stated that they used 2015 .

Example 284:
titleEntities: {'Subject': ['EU-28'], 'Date': ['2017']}
title: Proportion of individuals who have tried waterpipe , shisha or hooka in EU-28 2017
X_Axis['Response']: ['Yes', 'Never', 'Spontaneous']
Y_Axis['Share', 'of', 'respondents']: ['13', '87', '0']

gold: This statistic displays the proportion of individuals who have tried water pipe , shisha or hookah in EU-28 countries in 2017 . A majority of 87 percent of respondents said they have never tried water pipe , shisha or hookah products . Additionally , the proportion of individuals who have tried oral , nasal or chewing tobacco can be found at the following .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] water pipe , templateTitle[6] or hookah in templateTitleSubject[0] countries in templateTitleDate[0] . A majority of templateYValue[max] templateScale of templateYLabel[1] said they templateTitle[3] templateXValue[1] templateTitle[4] water pipe , templateTitle[6] or hookah products . Additionally , the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] oral , nasal or chewing tobacco can be found at the following .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] in the templateTitle[7] as of 2013 . During the survey , templateYValue[1] templateScale of templateTitleSubject[0] stated they used templateXValue[0] a templateXValue[1] and templateXValue[2] .
generated: This statistic presents the Proportion individuals who have of EU-28 in the hooka as of 2013 .  During the survey , 87 % of EU-28 stated they used Yes a Never and Spontaneous .

Example 285:
titleEntities: {'Subject': ['The'], 'Date': ['2018']}
title: The 20 worst terrorist attacks by number of fatalities 2018
X_Axis['City,', 'country', '(date),', 'organisation']: ['Ghazni_Afghanistan_(Taliban)_(8/10/2018)', 'Farah_Afghanistan_(Taliban)_(5/15/2018)', 'Darengarh_Pakistan_(Khorasan_Chapter_of_the_Islamic_State)_(7/13/2018)', 'Kabul_Afghanistan_(Taliban)_(1/27/2018)', 'Dila_District_Afghanistan_(Taliban)_(10/12/2018)', 'Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(4/22/2018)', 'Muhmand_Dara_District_Afghanistan_(Unknown)_(9/11/2018)', 'Day_Mirdad_District_Afghanistan_(Taliban)_(9/9/2018)', 'Maywand_District_Afghanistan_(Taliban)_(9/11/2018)', 'Farah_Afghanistan_(Taliban)_(5/12/2018)', 'Gwaska_Nigeria_(Attributed_to_"Fulani_Extremists")_(5/5/2018)', 'Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(11/20/2018)', 'Sari_Pul_Afghanistan_(Taliban)_(9/10/2018)', 'Chora_District_Afghanistan_(Taliban)_(8/3/2018)', 'Pur_Chaman_District_Afghanistan_(Taliban)_(6/12/2018)', 'Albu_Kamal_Syria_(ISIL)_(6/8/2018)', 'Azra_District_Afghanistan_(Taliban)_(8/6/2018)', 'Kabul_Afghanistan_(Taliban)_(12/24/2018)', 'Oshan_Afghanistan_(Taliban)_(5/11/2018)', 'Tagbara_Central_African_Republic_(Anti-Balaka_Militia)_(4/3/2018)']
Y_Axis['Number', 'of', 'fatalities']: ['466', '330', '150', '104', '77', '70', '69', '62', '61', '61', '58', '56', '56', '51', '51', '51', '50', '47', '46', '44']

gold: The statistic shows the 20 worst terrorist attacks of 2018 , by number of fatalities . The worst terrorist attack in 2018 occurred on August 10 , 2018 , was carried out by the Taliban in Ghazni , Afghanistan , and caused 466 fatalities .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . The templateTitle[1] templateTitle[2] attack in templateTitleDate[0] occurred on 10 , templateTitleDate[0] , was carried out templateTitle[4] the Taliban in templateXValue[0] , templateXValue[0] , and caused templateYValue[max] templateYLabel[1] .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateYLabel[1] templateYLabel[2] templateYLabel[3] . In the templateTitle[6] templateXLabel[0] templateXValue[last] , there were templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in the previous year .
generated: This statistic shows the terrorist attacks of fatalities .  In the fatalities City, Tagbara_Central_African_Republic_(Anti-Balaka_Militia)_(4/3/2018) , there were 466 % fatalities in the previous year .

Example 286:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading men 's hair coloring brands in the U.S. 2019
X_Axis['Brand']: ['Just_For_Men', 'Just_For_Men_Autostop', 'Just_For_Men_Control_GX', 'Just_For_Men_Touch_of_Gray', 'Softsheen-Carson_Dark_&_Natural', 'Private_label', 'Grecian_Formula_16', 'Just_For_Men_Original_Formula', 'Creme_of_Nature', 'Grecian_5']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['138.0', '27.2', '18.2', '9.7', '5.7', '2.8', '2.3', '0.5', '0.3', '0.1']

gold: In 2019 , Just For Men was the leading men 's hair coloring brand in the United States with sales of approximately 138 million U.S. dollars . Ranked second , the Just For Men Autostop brand generated sales of around 27.2 million U.S. dollars that year .
gold_template: In templateTitleDate[0] , templateXValue[0] Men was the templateTitle[0] templateXValue[0] templateTitle[2] templateTitle[3] templateTitle[4] templateXLabel[0] in the templateTitle[6] with templateYLabel[0] of approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Ranked second , the templateXValue[0] Men templateXValue[1] templateXLabel[0] generated templateYLabel[0] of around templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] that year .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateXValue[7] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the country 's top selling templateXValue[7] templateTitle[2] templateXLabel[0] with templateYLabel[0] that amounted to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Sales of the Leading Just_For_Men_Original_Formula 's hair in the coloring in 2019 .  In that year , Just_For_Men was the country 's top selling Just_For_Men_Original_Formula 's Brand with Sales that amounted to about 138.0 million U.S. dollars .

Example 287:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Birth rate in Italy 2018 , by region
X_Axis['Month']: ['Trentino-South_Tyrol', 'Campania', 'Sicily', 'Calabria', 'Lombardy', 'Emilia-Romagna', 'Lazio', 'Apulia', 'Aosta_Valley', 'Veneto', 'Abruzzo', 'Piedmont', 'Tuscany', 'Marche', 'Umbria', 'Basilicata', 'Friuli-Venezia_Giulia', 'Molise', 'Liguria', 'Sardinia']
Y_Axis['Birth', 'rate', 'per', 'thousand', 'inhabitants']: ['9.0', '8.3', '8.1', '7.8', '7.5', '7.3', '7.2', '7.2', '7.2', '7.2', '6.8', '6.7', '6.7', '6.7', '6.6', '6.6', '6.4', '6.2', '5.8', '5.7']

gold: In 2018 , Trentino-South Tyrol was the region in Italy with the highest birth rate nationwide , with nine births per every 1,000 inhabitants . The following three positions of the ranking were occupied by Southern regions : Campania , Sicily , and Calabria . Indeed , South-Italy was the macro-region with the largest birth-rate in Italy .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[5] in templateTitleSubject[0] with the highest templateYLabel[0] templateYLabel[1] nationwide , with templateYValue[max] births templateYLabel[2] every 1,000 templateYLabel[4] . The following three positions of the ranking were occupied templateTitle[4] Southern regions : templateXValue[1] , templateXValue[2] , and templateXValue[3] . Indeed , South-Italy was the macro-region with the largest birth-rate in templateTitleSubject[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] as of 2019 . In templateTitleDate[0] , the most recent templateYLabel[0] of templateTitleSubject[0] was templateXValue[0] , where a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Birth rate Italy in as of 2019 .  In 2018 , the most recent Birth of Italy was Trentino-South_Tyrol , where a Birth of 9.0 rate per .

Example 288:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2018']}
title: Ad blocker usage in the United Kingdom ( UK ) 2018
X_Axis['Response']: ['Use_ad_blocker', "Don't_use_ad_blocker", "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['41', '53', '6']

gold: This statistic shows the survey on ad blocker usage in the United Kingdom in 2018 . According to the survey , 41 percent of the respondents used an ad blocker , while 53 percent did not . Six percent of respondents said they did n't know if they used ad blocking software .
gold_template: This statistic shows the survey on templateXValue[0] usage in the templateTitleSubject[0] in templateTitleDate[0] . According to the survey , templateYValue[0] templateScale of the templateYLabel[1] used an templateXValue[0] , while templateYValue[max] templateScale did not . templateYValue[min] templateScale of templateYLabel[1] said they did n't templateXValue[last] if they used templateXValue[0] blocking software .

generated_template: This statistic shows the results of a survey about the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] as of 2018 . During the survey , templateYValue[max] templateScale of templateYLabel[1] stated that they believed that they had abandoned shopping templateTitle[5] .
generated: This statistic shows the results of a survey about the most usage United Kingdom UK in 2018 as of 2018 .  During the survey , 53 % of respondents stated that they believed that they had abandoned shopping UK .

Example 289:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Travelers in the U.S. who find family vacation planning stressful in 2014 , by gender
X_Axis['Response']: ['Women', 'Men']
Y_Axis['Share', 'of', 'respondents']: ['74', '67']

gold: This statistic shows the share of travelers who find family vacation planning stressful in the United States as of May 2014 , by gender . During the survey , 74 percent of women said that they found family vacation planning stressful .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[1] as of 2014 , templateTitle[9] templateTitle[10] . During the survey , templateYValue[max] templateScale of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the U.S. who were using Travelers as of 2019 , sorted planning stressful .  During that period of time , 74 % of female respondents stated that they used the social networking site .

Example 290:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global exports of broiler meat 2019 , by country
X_Axis['Country']: ['Brazil', 'United_States', 'EU', 'Thailand', 'China', 'Turkey', 'Ukraine', 'Belarus', 'Russia', 'Argentina', 'Canada', 'Others']
Y_Axis['Export', 'volume', 'in', 'thousand', 'metric', 'tons']: ['3775', '3248', '1500', '900', '475', '400', '350', '185', '180', '145', '130', '331']

gold: This statistic depicts the export volume of broiler meat worldwide in 2019 , by leading country , in thousand metric tons . The broiler meat exports of the United States amounted to approximately 3.25 million metric tons in that year .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] worldwide in templateTitleDate[0] , templateTitle[5] leading templateXLabel[0] , in thousand templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[1] of the templateXValue[1] amounted to approximately templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in that year .

generated_template: This statistic depicts the forecast templateYLabel[0] templateYLabel[1] of templateTitle[1] worldwide in templateTitleDate[0] , templateTitle[4] leading templateXLabel[0] . The estimated templateTitle[1] templateTitle[2] of the templateXValue[1] were amounted to approximately templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic depicts the forecast Export volume of exports worldwide in 2019 , leading Country .  The estimated exports broiler of the United_States were amounted to approximately 3248 thousand metric tons in 2019 .

Example 291:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. rice exports - top destination country 2017
X_Axis['Country']: ['Mexico', 'Haiti', 'Japan', 'Venezuela', 'Canada', 'Costa_Rica', 'Korea_South', 'Jordan', 'Honduras', 'Saudi_Arabia']
Y_Axis['Exports', 'in', 'metric', 'tons']: ['894043', '508527', '302752', '240063', '221833', '164114', '152098', '146558', '137420', '124913']

gold: This statistic shows the major nations to which the U.S. exported rice ( milled basis ) in 2017 . Some 894,043 metric tons were exported to Mexico that year . Thus , Mexico was ranked first among the most important destinations for U.S. rice exports in 2017 .
gold_template: This statistic shows the major nations to which the templateTitleSubject[0] exported templateTitle[1] ( milled basis ) in templateTitleDate[0] . Some templateYValue[max] templateYLabel[1] templateYLabel[2] were exported to templateXValue[0] that year . Thus , templateXValue[0] was ranked first among the most important destinations for templateTitleSubject[0] templateTitle[1] templateYLabel[0] in templateTitleDate[0] .

generated_template: This statistic depicts the 15 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] . According to the report , templateXValue[1] accounted for templateYValue[1] templateScale of templateTitleDate[0] .
generated: This statistic depicts the 15 U.S. rice the exports top destination country 2017 in .  According to the report , Haiti accounted for 508527 % of 2017 .

Example 292:
titleEntities: {'Subject': ['Tesla'], 'Date': ['2019', '2019']}
title: Tesla 's vehicle deliveries by quarter 2019
X_Axis['Quarter']: ['Q4_2019', 'Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015']
Y_Axis['Number', 'of', 'deliveries', 'in', 'units']: ['112000', '97000', '95200', '63000', '90700', '83500', '40740', '29980', '29870', '26150', '22000', '25000', '22200', '24500', '14370', '14820', '17400']

gold: How many Tesla vehicles were delivered in 2019 ? Annual deliveries rose by almost 50 percent between 2018 and 2019 . Year-to-date deliveries increased to between 367,000 and 368,000 units in 2019 , and Tesla delivered around 112,000 vehicles during the fourth quarter of 2019 alone . The quarterly figure represents a new record following the electric carmaker 's previous quarter which set the record at 97,000 deliveries worldwide .
gold_template: How many templateTitleSubject[0] vehicles were delivered in templateXValue[0] ? Annual templateYLabel[1] templatePositiveTrend templateTitle[4] almost 50 templateScale between templateXValue[4] and templateXValue[0] . Year-to-date templateYLabel[1] templatePositiveTrend to between 367,000 and 368,000 templateYLabel[2] in templateXValue[0] , and templateTitleSubject[0] delivered around templateYValue[max] vehicles during the fourth templateXLabel[0] of templateXValue[0] alone . The quarterly figure represents a new record following the electric carmaker templateTitle[1] previous templateXLabel[0] which set the record at templateYValue[1] templateYLabel[1] worldwide .

generated_template: The timeline shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitle[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] templateYValue[max] out of recent times times .
generated: The timeline shows the Tesla 's vehicle of Tesla from the first Quarter of 2019 to the fourth Quarter of Q4_2019 .  In the fourth Quarter of 2019 , Tesla 's deliveries units 112000 out of recent times .

Example 293:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Important features of music streaming services in the U.S. 2018
X_Axis['Response']: ['The_variety_of_music_available', 'Low_price_point', 'The_ability_to_listen_on_multiple_divices', 'Clean_user_interface', 'Good_algorithms_to_find_new_music', 'The_ability_to_combine_your_music_library_with_your_streaming_service_library', 'The_ability_to_stream_on_smart_home_devices', 'Curated_playlists', 'Artist_exclusives']
Y_Axis['Share', 'of', 'respondents']: ['81', '80', '68', '66', '58', '64', '57', '52', '46']

gold: This statistic presents data on the most important features of music streaming services among adults in the United States as of March 2018 . During a survey , 81 percent of respondents stated that the variety of music available was the most important feature of music streaming services .
gold_template: This statistic presents data on the most templateTitle[0] templateTitle[1] of templateXValue[0] templateXValue[5] templateTitle[4] among adults in the templateTitle[5] as of 2018 . During a survey , templateYValue[max] templateScale of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among Americans aged 16 and older regarding the templateTitle[6] as of 2018 . Some templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[0] and templateXValue[0] . Overall , followed by templateXValue[1] and templateXValue[2] had templateXValue[1] at templateYValue[1] and templateYValue[2] templateScale of surveyed adults .
generated: This statistic shows the results of a 2018 survey among Americans aged 16 and older regarding the 2018 as of 2018 .  Some 81 % of the respondents stated that they used The_variety_of_music_available and .  Overall , followed by Low_price_point and The_ability_to_listen_on_multiple_divices had Low_price_point at 80 and 68 % of surveyed adults .

Example 294:
titleEntities: {'Subject': ['U.S. April'], 'Date': ['2014', '2014']}
title: Methods of ordering food for takeout or delivery in the U.S. as of April 2014
X_Axis['Response']: ['By_phone', 'In_person', 'Online', 'Via_mobile_app', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['50.5', '43.5', '32.1', '3.9', '0.2']

gold: This statistic shows the methods which consumers used to order food for takeout or delivery in the United States as of April 2014 . During the survey , 32.1 percent of respondents said they ordered food for takeout or delivery online .
gold_template: This statistic shows the templateTitle[0] which consumers used to order templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] in the templateTitle[6] as of templateTitleSubject[0] templateTitleDate[0] . During the survey , templateYValue[2] templateScale of templateYLabel[1] said they ordered templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2015 . During the survey , templateYValue[1] templateScale of templateYLabel[1] stated they would choose templateXValue[1] templateTitle[1] .
generated: This statistic shows the Methods ordering food of for By_phone takeout delivery in the U.S. as of 2015 .  During the survey , 43.5 % of respondents stated they would choose In_person ordering .

Example 295:
titleEntities: {'Subject': ['United States'], 'Date': ['2012']}
title: Reasons for opposing same-sex marriage in the United States in 2012
X_Axis['Month']: ['Religion/Bible_says_it_is_wrong', 'Marriage_should_be_between_a_man_and_a_woman', 'Morally_wrong/Have_traditional_beliefs', 'Civil_unions_are_sufficient', 'Unnatural/Against_laws_of_nature', 'Undermines_traditional_family_structure/Mother_and_father', 'Other', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['47', '20', '16', '6', '5', '5', '7', '4']

gold: This statistic shows the results of a 2012 survey among American adults opposing legal same-sex marriage . They were asked to give reasons for this decision . 47 percent of respondents stated that they oppose same-sex marriage because their religion and/or the Bible says it 's wrong .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among American adults templateTitle[2] legal templateTitle[3] templateXValue[1] . They were asked to give templateTitle[0] templateTitle[1] this decision . templateYValue[max] templateScale of templateYLabel[1] stated that they oppose templateTitle[3] templateXValue[1] because their religion and/or the Bible templateXValue[0] it 's templateXValue[0] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitleSubject[0] . As of templateTitleDate[0] , templateYValue[max] templateScale of templateYLabel[1] stated templateXValue[2] was currently the templateTitle[0] templateXLabel[0] in templateTitleSubject[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Reasons nation in United States .  As of 2012 , 47 % of respondents stated Morally_wrong/Have_traditional_beliefs was currently the Reasons Month in United States .

Example 296:
titleEntities: {'Subject': ['Muslims', 'Spain'], 'Date': ['2018']}
title: Muslims in Spain 2018 , by nationality
X_Axis['Country']: ['Spain', 'Morocco', 'Pakistan', 'Senegal', 'Algeria', 'Nigeria', 'Mali', 'Gambia', 'Bangladesh', 'Guinea', 'Others']
Y_Axis['Number', 'of', 'Muslims']: ['847801', '769050', '82738', '66046', '60820', '39374', '23685', '19381', '15979', '10186', '58615']

gold: This statistic presents the number of Muslims in Spain in 2018 , broken down by nationality . That year , there were a total of approximately two million Muslims in Spain . Almost 848 thousand had Spanish nationality , followed by Muslims with a Moroccan nationality with figures that almost reached 770 thousand individuals .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] in templateXValue[0] in templateTitleDate[0] , broken down templateTitle[3] templateTitle[4] . That year , there were a total of approximately two templateScale templateYLabel[1] in templateXValue[0] . Almost templateYValue[max] thousand had Spanish templateTitle[4] , followed templateTitle[3] templateYLabel[1] with a Moroccan templateTitle[4] with figures that almost reached 770 thousand individuals .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] in templateTitle[3] as of 2018 , sorted by templateYLabel[0] of templateYLabel[2] . As of templateTitleDate[0] , had the highest of templateTitle[1] templateTitle[3] of templateTitle[3] .
generated: This statistic shows the Number of Muslims Spain in by as of 2018 , sorted by Number of Muslims .  As of 2018 , had the highest of Spain by of .

Example 297:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading U.S. consumer e-mail providers 2016
X_Axis['Response']: ['Google_(Gmail)', 'Yahoo', 'Outlook_(Hotmail)', 'AOL', 'Other', 'iCloud', 'Comcast']
Y_Axis['Share', 'of', 'respondents']: ['53', '18', '14', '8', '4', '2', '1']

gold: This statistic shows the most popular e-mail providers according to consumers in the United States as of 2016 . During the consumer survey , 53 percent of respondents stated that they used Gmail as their primary e-mail provider . Yahoo was ranked second with 18 percent .
gold_template: This statistic shows the most popular templateTitle[3] templateTitle[4] according to consumers in the templateTitle[1] as of templateTitleDate[0] . During the templateTitle[2] survey , templateYValue[max] templateScale of templateYLabel[1] stated that they used Gmail as their primary templateTitle[3] provider . templateXValue[1] was ranked second with templateYValue[1] templateScale .

generated_template: templateXValue[0] was the most popular among templateTitleSubject[0] templateTitle[1] in templateTitleSubject[1] as of 2018 . During the survey , templateYValue[max] templateScale of survey period it was found that they used templateXValue[0] social or their favorite of templateYLabel[1] had either templateXValue[0] . templateXValue[1] and templateXValue[2] , with templateYValue[1] templateScale pointed to templateXValue[1] .
generated: Google_(Gmail) was the most popular among U.S. in as of 2018 .  During the survey , 53 % of survey period it was found that they used Google_(Gmail) social or their favorite of respondents had either Google_(Gmail) .  Yahoo and Outlook_(Hotmail) , with 18 % pointed to Yahoo .

Example 298:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global sulfur production by country 2019
X_Axis['Country']: ['China', 'United_States', 'Russia', 'Saudi_Arabia', 'Canada', 'Other', 'Kazakhstan', 'United_Arab_Emirates', 'India', 'Japan', 'South_Korea', 'Iran', 'Qatar', 'Chile', 'Poland', 'Finland', 'Kuwait', 'Australia', 'Germany', 'Venezuela', 'Italy', 'Netherlands', 'Brazil']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'ton']: ['17400', '8800', '7100', '6600', '5300', '3900', '3600', '3400', '3400', '3400', '3100', '2200', '2100', '1500', '1230', '940', '900', '900', '870', '700', '550', '520', '500']

gold: In 2019 , China produced around 17.4 megatons of sulfur , which makes China the world 's leading sulfur producer . China 's sulfur production includes byproduct elemental sulfur recovered from natural gas and petroleum , the estimated sulfur content of byproduct sulfuric acid from metallurgy , and the sulfur content of sulfuric acid from pyrite .
gold_template: In templateTitleDate[0] , templateXValue[0] produced around 17.4 megatons of templateTitle[1] , which makes templateXValue[0] the world 's leading templateTitle[1] producer . templateXValue[0] 's templateTitle[1] templateTitle[2] includes byproduct elemental templateTitle[1] recovered from natural gas and petroleum , the estimated templateTitle[1] content of byproduct sulfuric acid from metallurgy , and the templateTitle[1] content of sulfuric acid from pyrite .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateYLabel[2] worldwide templateTitle[3] as of 2020 , sorted by templateXLabel[0] . In that year , templateTitle[0] templateTitle[1] templateTitle[2] averaged templateYValue[min] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Price of Global sulfur dollars worldwide by as of 2020 , sorted by Country .  In that year , Global sulfur production averaged 500 U.S. dollars .

Example 299:
titleEntities: {'Subject': ['Luxottica'], 'Date': ['2018']}
title: Share of global net sales of Luxottica by geographical area 2018
X_Axis['Geographical', 'area']: ['North_America', 'Europe', 'Asia-Pacific', 'Latin_America', 'Rest_of_the_world']
Y_Axis['Share', 'of', 'net', 'sales']: ['58', '21', '13', '6', '2']

gold: This statistic depicts the share of net sales of Luxottica worldwide in 2018 , by geographical area . In that year , 58 percent of Luxottica 's global net sales came from North America . Founded in 1961 in Agordo , Italy , the Luxottica Group S.p.A. is the world 's largest eyewear company .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] templateScale of templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] came from templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitleSubject[0] Group S.p.A. is the templateXValue[last] 's largest eyewear company .

generated_template: This graph depicts the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] with a total of templateYValue[max] times .
generated: This graph depicts the Share of global net sales in 2018 , Luxottica Geographical .  In that year , North_America was the Share global net Geographical with a total of 58 times .

Example 300:
titleEntities: {'Subject': ['American'], 'Date': []}
title: American teenagers ' belief in existence of a God
X_Axis['Response']: ['Absolutely_certain_that_there_is_a_God', 'Somewhat_certain_that_there_is_a_God', 'Somewhat_certain_that_there_is_no_God', 'Absolutely_certain_that_there_is_no_God', 'Not_sure_whether_or_not_there_is_a_God']
Y_Axis['Share', 'of', 'respondents']: ['54', '15', '7', '9', '16']

gold: This survey , conducted by Harris Poll across the United States in February 2014 , shows the share of American teenagers who are certain or uncertain about the existence of a God . 54 percent of American teenagers are absolutely certain that there is a God .
gold_template: This survey , conducted by Harris Poll across the templateTitle[0] in 2014 , shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] who are templateXValue[0] or uncertain about the templateTitle[4] of a templateXValue[0] . templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[1] are templateXValue[0] that templateXValue[0] is a templateXValue[0] .

generated_template: A 2017 survey of templateTitleSubject[0] adults found that approximately templateYValue[max] templateScale of American templateXValue[0] and templateYValue[min] templateScale of American templateXValue[last] currently smoke templateTitle[4] . Impact of Legalizing Cannabis in the templateTitle[0] . Since Washington and Colorado legalized recreational templateTitle[4] in 2012 , several more states have followed suit .
generated: A 2017 survey of American adults found that approximately 54 % of American Absolutely_certain_that_there_is_a_God and 7 % of American Not_sure_whether_or_not_there_is_a_God currently smoke existence .  Impact of Legalizing Cannabis in the American .  Since Washington and Colorado legalized recreational existence in 2012 , several more states have followed suit .

Example 301:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Refugees arriving by age U.S. 2018
X_Axis['Age', 'in', 'years']: ['Under_1_year', '1_to_4_years', '5_to_9_years', '10_to_14_years', '15_to_19_years', '20_to_24_years', '25_to_29_years', '30_to_34_years', '35_to_39_years', '40_to_44_years', '45_to_49_years', '50_to_54_years', '55_to_59_years', '60_to_64_years', '65_to_69_years', '70_to_74_years', '75_years_and_over']
Y_Axis['Number', 'of', 'persons']: ['50', '2442', '2914', '2706', '2692', '2383', '1952', '1910', '1418', '1073', '872', '621', '447', '334', '269', '159', '163']

gold: This statistic shows the number of refugees arriving in the United States in 2018 , by age . In 2018 , about 163 refugees arrived in the United States aged 75 years or over . The total number of refugee arrivals amounted to 22,405 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[4] in templateTitleDate[0] , templateTitle[2] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[last] templateTitle[0] arrived in the templateTitle[4] aged templateXValue[last] templateXValue[1] or templateXValue[last] . The total templateYLabel[0] of refugee arrivals amounted to 22,405 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] from templateXValue[last] templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[0] , the templateYLabel[0] of of of templateXValue[5] had an average of templateYValue[1] templateScale templateYLabel[3] .
generated: This statistic shows the Refugees arriving by of U.S. from 75_years_and_over 2018 to .  In 2018 , the Number of 20_to_24_years had an average of 2442 million persons .

Example 302:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2019']}
title: Cyber bullying : common types of bullying 2019
X_Axis['Response']: ['I_have_been_cyber_bullied', 'Mean_or_hurtful_comments_online', 'Rumors_online', 'Threatened_to_hurt_me_through_a_cell_phone_text', 'Posted_mean_names_or_comments_online_about_me_with_a_sexual_meaning', 'Threatened_to_hurt_me_online', 'Posted_a_mean_or_hurtful_picture_online_of_me', 'Pretended_to_be_me_online', 'Posted_mean_names_or_comments_about_my_race_or_color', 'Posted_a_mean_or_hurtful_video_online_of_me', 'Posten_mean_names_or_comments_online_about_my_religion', 'Created_a_mean_or_hurtful_web_page_about_me', 'One_or_more_of_above_two_or_more_times']
Y_Axis['Share', 'of', 'respondents']: ['17.4', '24.9', '22.2', '12.2', '12', '11.7', '10.8', '10.1', '9.5', '7.1', '6.7', '6.4', '30.1']

gold: This statistic presents the percentage of middle and high school students in the United States who were cyber bullied , divided by the type of cyber bullying endured . During the April 2019 survey , 10.1 percent of cyber bullying victims had been impersonated online during the last 30 days . Cyber bullying includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information online .
gold_template: This statistic presents the templateScale of middle and high school students in the country who were templateXValue[0] , divided by the type of templateXValue[0] templateTitle[1] endured . During the 2019 survey , templateYValue[7] templateScale of templateXValue[0] templateTitle[1] victims had templateXValue[0] impersonated templateXValue[1] during the last templateYValue[max] days . templateXValue[0] templateTitle[1] includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information templateXValue[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] the templateTitle[6] as of 2015 . As of the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they planned to watch templateXValue[0] a very or templateXValue[1] .
generated: The statistic shows the Cyber bullying common of types I_have_been_cyber_bullied the 2019 as of 2015 .  As of the survey , 30.1 % of the respondents stated that they planned to watch I_have_been_cyber_bullied a very or Mean_or_hurtful_comments_online .

