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|---|---|---|---|---|
What percentage of State government workers preferred Barack Obama in 2012? | [
"55"
] | 0human
| Characteristic | Barack Obama | Mitt Romney <0x0A> All Government workers | 51% | 40% <0x0A> Federal Government workers | 46% | 46% <0x0A> State Government workers | 55% | 37% <0x0A> Local Government workers | 50% | 40% |
|
Which voter group have equal percentage of preference for Barack Obama and Mitt Romney? | [
"Federal Government workers"
] | 0human
| Characteristic | Barack Obama | Mitt Romney <0x0A> All Government workers | 51% | 40% <0x0A> Federal Government workers | 46% | 46% <0x0A> State Government workers | 55% | 37% <0x0A> Local Government workers | 50% | 40% |
|
What is the first data in the line graph? | [
"4.57"
] | 0human
| Characteristic | Female | Male <0x0A> 2019 | 5.53 | 5.54 <0x0A> 2018 | 5.48 | 5.49 <0x0A> 2017 | 5.45 | 5.46 <0x0A> 2016 | 5.41 | 5.42 <0x0A> 2015 | 5.36 | 5.36 <0x0A> 2014 | 5.28 | 5.28 <0x0A> 2013 | 5.18 | 5.18 <0x0A> 2012 | 5.06 | 5.06 <0x0A> 2011 | 4.92 | 4.9 <0x0A> 2010 | 4.76 | 4.75 <0x0A> 2009 | 4.57 | 4.57 |
|
What is the average of last three data ? | [
"5.48"
] | 0human
| Characteristic | Female | Male <0x0A> 2019 | 5.53 | 5.54 <0x0A> 2018 | 5.48 | 5.49 <0x0A> 2017 | 5.45 | 5.46 <0x0A> 2016 | 5.41 | 5.42 <0x0A> 2015 | 5.36 | 5.36 <0x0A> 2014 | 5.28 | 5.28 <0x0A> 2013 | 5.18 | 5.18 <0x0A> 2012 | 5.06 | 5.06 <0x0A> 2011 | 4.92 | 4.9 <0x0A> 2010 | 4.76 | 4.75 <0x0A> 2009 | 4.57 | 4.57 |
|
A zero value happened in one year, Find out that year? | [
"2003"
] | 0human
| Characteristic | Acceptable for you and others** | Acceptable for others but not you personally | Not acceptable for anyone | Don't know / refused <0x0A> 2013 | 47% | 38% | 14% | 1% <0x0A> 2012 | 46% | 35% | 16% | 2% <0x0A> 2011 | 44% | 38% | 15% | 3% <0x0A> 2010 | 45% | 36% | 17% | 2% <0x0A> 2009 | 43% | 38% | 17% | 1% <0x0A> 2008 | 49% | 35% | 14% | 2% <0x0A> 2007 | 52% | 30% | 16% | 3% <0x0A> 2006 | 50% | 29% | 18% | 3% <0x0A> 2005 | 54% | 27% | 15% | 4% <0x0A> 2004 | 54% | 27% | 16% | 3% <0x0A> 2003 | 57% | 28% | 16% | 0% <0x0A> 2002 | 51% | 28% | 16% | 5% |
|
What is the average of last four year in blue line chart ? | [
"45.5"
] | 0human
| Characteristic | Acceptable for you and others** | Acceptable for others but not you personally | Not acceptable for anyone | Don't know / refused <0x0A> 2013 | 47% | 38% | 14% | 1% <0x0A> 2012 | 46% | 35% | 16% | 2% <0x0A> 2011 | 44% | 38% | 15% | 3% <0x0A> 2010 | 45% | 36% | 17% | 2% <0x0A> 2009 | 43% | 38% | 17% | 1% <0x0A> 2008 | 49% | 35% | 14% | 2% <0x0A> 2007 | 52% | 30% | 16% | 3% <0x0A> 2006 | 50% | 29% | 18% | 3% <0x0A> 2005 | 54% | 27% | 15% | 4% <0x0A> 2004 | 54% | 27% | 16% | 3% <0x0A> 2003 | 57% | 28% | 16% | 0% <0x0A> 2002 | 51% | 28% | 16% | 5% |
|
Which year saw a drop in the gray line data? | [
"2020"
] | 0human
| Characteristic | Total number of stores | Other points of sale | Concept stores <0x0A> 2020 | 7092 | 4402 | 2690 <0x0A> 2019 | 7427 | 4657 | 2770 <0x0A> 2018 | 7728 | 5023 | 2705 <0x0A> 2017 | 7794 | 5348 | 2460 <0x0A> 2016 | 8131 | 5993 | 2138 |
|
Add up the median values of the data from each point of sale type. | [
"15456"
] | 0human
| Characteristic | Total number of stores | Other points of sale | Concept stores <0x0A> 2020 | 7092 | 4402 | 2690 <0x0A> 2019 | 7427 | 4657 | 2770 <0x0A> 2018 | 7728 | 5023 | 2705 <0x0A> 2017 | 7794 | 5348 | 2460 <0x0A> 2016 | 8131 | 5993 | 2138 |
|
What is the standardized men mortality rate in 2016 in the Netherlands? | [
"5.4"
] | 0human
| Characteristic | Total men and women | Men | Women <0x0A> 2019 | 5.7 | 5.1 | 6.1 <0x0A> 2018 | 5.9 | 5.2 | 6.4 <0x0A> 2017 | 5.9 | 5.3 | 6.4 <0x0A> 2016 | 6.0 | 5.4 | 6.4 <0x0A> 2015 | 6.1 | 5.5 | 6.5 <0x0A> 2014 | 5.9 | 5.3 | 6.2 <0x0A> 2013 | 6.1 | 5.6 | 6.4 <0x0A> 2012 | 6.3 | 5.7 | 6.5 <0x0A> 2011 | 6.2 | 5.7 | 6.5 <0x0A> 2010 | 6.4 | 5.9 | 6.6 <0x0A> 2009 | 6.4 | 6.1 | 6.6 <0x0A> 2008 | 6.6 | 6.2 | 6.9 |
|
In how many years, the standardized women mortality rate exceeds 6.2? | [
"4"
] | 0human
| Characteristic | Total men and women | Men | Women <0x0A> 2019 | 5.7 | 5.1 | 6.1 <0x0A> 2018 | 5.9 | 5.2 | 6.4 <0x0A> 2017 | 5.9 | 5.3 | 6.4 <0x0A> 2016 | 6.0 | 5.4 | 6.4 <0x0A> 2015 | 6.1 | 5.5 | 6.5 <0x0A> 2014 | 5.9 | 5.3 | 6.2 <0x0A> 2013 | 6.1 | 5.6 | 6.4 <0x0A> 2012 | 6.3 | 5.7 | 6.5 <0x0A> 2011 | 6.2 | 5.7 | 6.5 <0x0A> 2010 | 6.4 | 5.9 | 6.6 <0x0A> 2009 | 6.4 | 6.1 | 6.6 <0x0A> 2008 | 6.6 | 6.2 | 6.9 |
|
When did the total number of goods reach the peak? | [
"October"
] | 0human
| Characteristic | Total | Railway* | Road** | Pipeline | Maritime <0x0A> December | 48314 | 23516 | 19306 | 4752 | 653 <0x0A> November | 49464 | 20216 | 24470 | 3989 | 683 <0x0A> October | 50688 | 20351 | 25079 | 4436 | 708 <0x0A> September | 48804 | 19257 | 24315 | 4384 | 743 <0x0A> August | 45074 | 18499 | 21428 | 4440 | 612 <0x0A> July | 46257 | 18154 | 23114 | 4331 | 553 <0x0A> June | 44477 | 16935 | 22609 | 4082 | 754 <0x0A> May | 43174 | 16074 | 22277 | 4175 | 585 <0x0A> April | 43379 | 16281 | 22397 | 4130 | 511 <0x0A> March | 47838 | 18847 | 23753 | 4582 | 652 <0x0A> February | 45582 | 17572 | 23052 | 4378 | 574 <0x0A> January | 44483 | 16822 | 22449 | 4589 | 584 |
|
What's the sum of number of total goods from January to March? | [
"137903"
] | 0human
| Characteristic | Total | Railway* | Road** | Pipeline | Maritime <0x0A> December | 48314 | 23516 | 19306 | 4752 | 653 <0x0A> November | 49464 | 20216 | 24470 | 3989 | 683 <0x0A> October | 50688 | 20351 | 25079 | 4436 | 708 <0x0A> September | 48804 | 19257 | 24315 | 4384 | 743 <0x0A> August | 45074 | 18499 | 21428 | 4440 | 612 <0x0A> July | 46257 | 18154 | 23114 | 4331 | 553 <0x0A> June | 44477 | 16935 | 22609 | 4082 | 754 <0x0A> May | 43174 | 16074 | 22277 | 4175 | 585 <0x0A> April | 43379 | 16281 | 22397 | 4130 | 511 <0x0A> March | 47838 | 18847 | 23753 | 4582 | 652 <0x0A> February | 45582 | 17572 | 23052 | 4378 | 574 <0x0A> January | 44483 | 16822 | 22449 | 4589 | 584 |
|
When did the black line reach the peak? | [
"2019"
] | 0human
| Characteristic | Easier to compare prices between EU countries* | Has made travelling easier and less costly | Has reduced banking charges when travelling between EU countries <0x0A> 2019 | 80% | 52% | 39% <0x0A> 2018 | - | 50% | 36% <0x0A> 2017 | - | 50% | 35% <0x0A> 2016 | - | 47% | 32% <0x0A> 2015 | 74% | 51% | 30% <0x0A> 2014 | 74% | 50% | 30% <0x0A> 2013 | 73% | 48% | 29% <0x0A> 2012 | 74% | 48% | 29% <0x0A> 2011 | 74% | 50% | 31% <0x0A> 2010 | 64% | 48% | 29% |
|
For how many years is the black line above 50%? | [
"2"
] | 0human
| Characteristic | Easier to compare prices between EU countries* | Has made travelling easier and less costly | Has reduced banking charges when travelling between EU countries <0x0A> 2019 | 80% | 52% | 39% <0x0A> 2018 | - | 50% | 36% <0x0A> 2017 | - | 50% | 35% <0x0A> 2016 | - | 47% | 32% <0x0A> 2015 | 74% | 51% | 30% <0x0A> 2014 | 74% | 50% | 30% <0x0A> 2013 | 73% | 48% | 29% <0x0A> 2012 | 74% | 48% | 29% <0x0A> 2011 | 74% | 50% | 31% <0x0A> 2010 | 64% | 48% | 29% |
|
What is the percentage of share of retail sale of dog and cat food in 2008 by mass premium? | [
"33"
] | 0human
| Characteristic | 2008 | 2009 | 2010 <0x0A> Premium | 44% | 41% | 40% <0x0A> Mass Premium | 33% | 31% | 30% <0x0A> Superpremium | 11% | 9% | 10% <0x0A> Regular | 26% | 27% | 27% <0x0A> Value | 13% | 14% | 14% <0x0A> Treats | 17% | 18% | 18% |
|
What is the total percentage of share of retail sales of dog and cat food in regular ? | [
"80"
] | 0human
| Characteristic | 2008 | 2009 | 2010 <0x0A> Premium | 44% | 41% | 40% <0x0A> Mass Premium | 33% | 31% | 30% <0x0A> Superpremium | 11% | 9% | 10% <0x0A> Regular | 26% | 27% | 27% <0x0A> Value | 13% | 14% | 14% <0x0A> Treats | 17% | 18% | 18% |
|
What is the percentage in high income of high school student going to college in 1980 ? | [
"65"
] | 0human
| Characteristic | 1980 | 2009 <0x0A> Low-income | 32% | 55% <0x0A> Middle-income | 43% | 67% <0x0A> High-income | 65% | 84% <0x0A> All | 49% | 70% |
|
What is the difference of low income and middle income student going to college in 2009? | [
"12"
] | 0human
| Characteristic | 1980 | 2009 <0x0A> Low-income | 32% | 55% <0x0A> Middle-income | 43% | 67% <0x0A> High-income | 65% | 84% <0x0A> All | 49% | 70% |
|
Which age group is represented by the right most bar? | [
"65+"
] | 0human
| Characteristic | Share of respondents <0x0A> Under 18 years | 21% <0x0A> 18 to 34 years | 38% <0x0A> 34 to 54 years | 26% <0x0A> 55 to 64 years | 9% <0x0A> 65+ | 6% |
|
What's the sum of the lowest two shares of respondents? | [
"15"
] | 0human
| Characteristic | Share of respondents <0x0A> Under 18 years | 21% <0x0A> 18 to 34 years | 38% <0x0A> 34 to 54 years | 26% <0x0A> 55 to 64 years | 9% <0x0A> 65+ | 6% |
|
Which country is represented by the second bar from the left? | [
"China"
] | 0human
| Characteristic | Share of plants <0x0A> United States | 24% <0x0A> China | 19% <0x0A> Germany | 6% <0x0A> Russia | 6% <0x0A> South Korea | 5.5% <0x0A> Japan | 4.3% <0x0A> United Kingdom | 4% <0x0A> Other | 31.2% |
|
Which two adjacent bars have the largest difference in the distribution of pubg players? | [
"[United Kingdom, Other]"
] | 0human
| Characteristic | Share of plants <0x0A> United States | 24% <0x0A> China | 19% <0x0A> Germany | 6% <0x0A> Russia | 6% <0x0A> South Korea | 5.5% <0x0A> Japan | 4.3% <0x0A> United Kingdom | 4% <0x0A> Other | 31.2% |
|
Which year has the tallest bar? | [
"2021"
] | 0human
| Characteristic | Share of smartphone users <0x0A> 2021 | 46.9% <0x0A> 2020 | 45.3% <0x0A> 2019 | 45.2% <0x0A> 2018 | 45.1% <0x0A> 2017 | 44.2% <0x0A> 2016 | 43.5% <0x0A> 2015 | 43.3% <0x0A> 2014 | 42.3% |
|
Is the share of smartphone users that use an Apple iPhone in the US increasing over time? | [
"Yes"
] | 0human
| Characteristic | Share of smartphone users <0x0A> 2021 | 46.9% <0x0A> 2020 | 45.3% <0x0A> 2019 | 45.2% <0x0A> 2018 | 45.1% <0x0A> 2017 | 44.2% <0x0A> 2016 | 43.5% <0x0A> 2015 | 43.3% <0x0A> 2014 | 42.3% |
|
How many bars have values equal to or over 30? | [
"3"
] | 0human
| Characteristic | Number of players in millions <0x0A> May 2018 | 40 <0x0A> October 2017 | 35 <0x0A> April 2017 | 30 <0x0A> January 2017 | 25 <0x0A> October 2016 | 20 <0x0A> August 2016 | 15 <0x0A> May 2016 | 7 |
|
What's the earliest month reported in this graph? | [
"May 2016"
] | 0human
| Characteristic | Number of players in millions <0x0A> May 2018 | 40 <0x0A> October 2017 | 35 <0x0A> April 2017 | 30 <0x0A> January 2017 | 25 <0x0A> October 2016 | 20 <0x0A> August 2016 | 15 <0x0A> May 2016 | 7 |
|
Are the bars sorted from top to bottom in this graph? | [
"Yes"
] | 0human
| Characteristic | Number of followers in millions <0x0A> @barackobama | 129.78 <0x0A> @justinbieber | 114.04 <0x0A> @katyperry | 109.52 <0x0A> @rihanna | 102.22 <0x0A> @Cristiano | 91.39 <0x0A> @taylorswift13 | 88.51 <0x0A> @ladygaga | 83.98 <0x0A> @ArianaGrande | 82.3 <0x0A> @TheEllenShow | 79.22 <0x0A> @YouTube | 73.02 |
|
What's the difference in the number of followers between the most and least followed Twitter accounts in this graph? | [
"56.76"
] | 0human
| Characteristic | Number of followers in millions <0x0A> @barackobama | 129.78 <0x0A> @justinbieber | 114.04 <0x0A> @katyperry | 109.52 <0x0A> @rihanna | 102.22 <0x0A> @Cristiano | 91.39 <0x0A> @taylorswift13 | 88.51 <0x0A> @ladygaga | 83.98 <0x0A> @ArianaGrande | 82.3 <0x0A> @TheEllenShow | 79.22 <0x0A> @YouTube | 73.02 |
|
What's the bottom y-axis label in this graph? | [
"Running backs"
] | 0human
| Characteristic | Average career length in years <0x0A> Kickers | 4.87 <0x0A> Quarterbacks | 4.44 <0x0A> League average | 3.3 <0x0A> Cornerbacks | 2.94 <0x0A> Wide receivers | 2.81 <0x0A> Running backs | 2.57 |
|
What's the difference between the maximum and median playing career length in the National Football League? | [
"1.75"
] | 0human
| Characteristic | Average career length in years <0x0A> Kickers | 4.87 <0x0A> Quarterbacks | 4.44 <0x0A> League average | 3.3 <0x0A> Cornerbacks | 2.94 <0x0A> Wide receivers | 2.81 <0x0A> Running backs | 2.57 |
|
What's the top y-axis label in this chart? | [
"Instagram"
] | 0human
| Characteristic | Number of followers in millions <0x0A> Instagram | 388.94 <0x0A> Cristiano Ronaldo | 271.24 <0x0A> Ariana Grande | 227.28 <0x0A> Dwayne Johnson (The Rock) | 224.3 <0x0A> Kylie Jenner | 221.61 <0x0A> Selena Gomez | 217.19 <0x0A> Kim Kardashian West | 210.76 <0x0A> Leo Messi | 191.72 <0x0A> Beyonce | 169.55 <0x0A> Justin Bieber | 167.35 |
|
Which two instagram accounts have the closest number of followers in this graph? | [
"[Beyonce, Justin Bieber]"
] | 0human
| Characteristic | Number of followers in millions <0x0A> Instagram | 388.94 <0x0A> Cristiano Ronaldo | 271.24 <0x0A> Ariana Grande | 227.28 <0x0A> Dwayne Johnson (The Rock) | 224.3 <0x0A> Kylie Jenner | 221.61 <0x0A> Selena Gomez | 217.19 <0x0A> Kim Kardashian West | 210.76 <0x0A> Leo Messi | 191.72 <0x0A> Beyonce | 169.55 <0x0A> Justin Bieber | 167.35 |
|
Which two adjacent bars have the nearest number of subscribers? | [
"[WWE, Kids Diana Show]"
] | 0human
| Characteristic | Number of subscribers in millions <0x0A> T-Series | 183.0 <0x0A> YouTube Movies | 137.0 <0x0A> Music | 115.0 <0x0A> Cocomelon - Nursey Rhymes | 112.0 <0x0A> PewDiePie | 110.0 <0x0A> SET India | 105.0 <0x0A> Gaming | 89.2 <0x0A> Kids Diana Show | 79.1 <0x0A> WWE | 77.6 <0x0A> Sports | 75.0 |
|
What's the average number of subscribers of the most 3 popular Youtube channels ? | [
"145"
] | 0human
| Characteristic | Number of subscribers in millions <0x0A> T-Series | 183.0 <0x0A> YouTube Movies | 137.0 <0x0A> Music | 115.0 <0x0A> Cocomelon - Nursey Rhymes | 112.0 <0x0A> PewDiePie | 110.0 <0x0A> SET India | 105.0 <0x0A> Gaming | 89.2 <0x0A> Kids Diana Show | 79.1 <0x0A> WWE | 77.6 <0x0A> Sports | 75.0 |
|
Are the bars sorted from left to right in this graph? | [
"No"
] | 0human
| Characteristic | Number of incidents <0x0A> White | 66 <0x0A> Black | 21 <0x0A> Latino | 10 <0x0A> Asian | 8 <0x0A> Other | 5 <0x0A> Native American | 3 <0x0A> Unknown /unclear | 11 |
|
Which two ethnicities have the largest number of mass shooters in the US? | [
"[White, Black]"
] | 0human
| Characteristic | Number of incidents <0x0A> White | 66 <0x0A> Black | 21 <0x0A> Latino | 10 <0x0A> Asian | 8 <0x0A> Other | 5 <0x0A> Native American | 3 <0x0A> Unknown /unclear | 11 |
|
Are the bars sorted in this graph? | [
"Yes"
] | 0human
| Characteristic | Number of players in millions <0x0A> May 2020 | 350 <0x0A> March 2019 | 250 <0x0A> November 2018 | 200 <0x0A> June 2018 | 125 <0x0A> January 2018 | 45 <0x0A> December 2017 | 30 <0x0A> August 2017 | 1 |
|
How many Fortnite users registered after November 2018? | [
"150"
] | 0human
| Characteristic | Number of players in millions <0x0A> May 2020 | 350 <0x0A> March 2019 | 250 <0x0A> November 2018 | 200 <0x0A> June 2018 | 125 <0x0A> January 2018 | 45 <0x0A> December 2017 | 30 <0x0A> August 2017 | 1 |
|
What does the highest bar in this graph represent? | [
"Average deaths/day by all causes (2019)"
] | 0human
| Characteristic | Number of daily deaths <0x0A> Average deaths/day by all causes (2019) | 7969.7 <0x0A> Average deaths/day by influenza (2019-2020)** | 331.6 <0x0A> Average COVID-19 deaths/day since first case in U.S. | 1133.3 <0x0A> Number of COVID-19 deaths on January 19, 2021 | 2812.0 |
|
What's the percentage of the average deaths/day caused by influenza in the total number of deaths? | [
"4.1"
] | 0human
| Characteristic | Number of daily deaths <0x0A> Average deaths/day by all causes (2019) | 7969.7 <0x0A> Average deaths/day by influenza (2019-2020)** | 331.6 <0x0A> Average COVID-19 deaths/day since first case in U.S. | 1133.3 <0x0A> Number of COVID-19 deaths on January 19, 2021 | 2812.0 |
|
How many tiny bars exist in this chart? | [
"2"
] | 0human
| Characteristic | Number of cases <0x0A> Total confirmed cases | 734048 <0x0A> Discharged cases | 667709 <0x0A> Deaths | 4944 <0x0A> ICU | 886 |
|
What's the percentage of the discharged cases in the total confirmed cases in Malaysia? | [
"90.9"
] | 0human
| Characteristic | Number of cases <0x0A> Total confirmed cases | 734048 <0x0A> Discharged cases | 667709 <0x0A> Deaths | 4944 <0x0A> ICU | 886 |
|
Which month witnessed the steepest increase in the full-time employees numbers in the US? | [
"Jun '20"
] | 0human
| Characteristic | Number of employees in millions <0x0A> May '21 | 126.58 <0x0A> Apr '21 | 125.64 <0x0A> Mar '21 | 124.84 <0x0A> Feb '21 | 123.98 <0x0A> Jan '21 | 123.72 <0x0A> Dec '20 | 124.42 <0x0A> Nov '20 | 124.33 <0x0A> Oct '20 | 124.17 <0x0A> Sep '20 | 123.0 <0x0A> Aug '20 | 123.62 <0x0A> Jul '20 | 121.2 <0x0A> Jun '20 | 120.11 <0x0A> May '20 | 116.62 |
|
In which months there was a decrease in the number of full-time employees in the US compared to the previous month? | [
"[Sep '20, Jan '21]"
] | 0human
| Characteristic | Number of employees in millions <0x0A> May '21 | 126.58 <0x0A> Apr '21 | 125.64 <0x0A> Mar '21 | 124.84 <0x0A> Feb '21 | 123.98 <0x0A> Jan '21 | 123.72 <0x0A> Dec '20 | 124.42 <0x0A> Nov '20 | 124.33 <0x0A> Oct '20 | 124.17 <0x0A> Sep '20 | 123.0 <0x0A> Aug '20 | 123.62 <0x0A> Jul '20 | 121.2 <0x0A> Jun '20 | 120.11 <0x0A> May '20 | 116.62 |
|
What's the difference between each two consecutive y-axis ticks? | [
"1000"
] | 0human
| Characteristic | GDP in billion U.S. dollars <0x0A> 2026* | 4534.34 <0x0A> 2025* | 3884.73 <0x0A> 2024* | 3591.05 <0x0A> 2023* | 3312.95 <0x0A> 2022* | 3312.95 <0x0A> 2021* | 3094.34 <0x0A> 2020 | 2700.5 <0x0A> 2019 | 2870.5 <0x0A> 2018 | 2651.47 <0x0A> 2017 | 2294.12 <0x0A> 2016 | 2294.12 <0x0A> 2015 | 2103.59 <0x0A> 2014 | 2039.11 <0x0A> 2013 | 1830.64 <0x0A> 2012 | 1822.51 <0x0A> 2011 | 1708.46 <0x0A> 2010 | 1708.46 <0x0A> 2009 | 1238.7 <0x0A> 2008 | 1231.99 <0x0A> 2007 | 1238.7 <0x0A> 2006 | 1093.21 <0x0A> 2005 | 834.27 <0x0A> 2004 | 762.09 <0x0A> 2003 | 633.22 <0x0A> 2002 | 523.77 <0x0A> 2001 | 476.18 <0x0A> 2000 | 427.62 <0x0A> 1999 | 466.84 <0x0A> 1998 | 423.91 <0x0A> 1997 | 399.79 <0x0A> 1996 | 319.76 <0x0A> 1995 | 301. |
|
Is India GDP always increasing in this graph? | [
"No"
] | 0human
| Characteristic | GDP in billion U.S. dollars <0x0A> 2026* | 4534.34 <0x0A> 2025* | 3884.73 <0x0A> 2024* | 3591.05 <0x0A> 2023* | 3312.95 <0x0A> 2022* | 3312.95 <0x0A> 2021* | 3094.34 <0x0A> 2020 | 2700.5 <0x0A> 2019 | 2870.5 <0x0A> 2018 | 2651.47 <0x0A> 2017 | 2294.12 <0x0A> 2016 | 2294.12 <0x0A> 2015 | 2103.59 <0x0A> 2014 | 2039.11 <0x0A> 2013 | 1830.64 <0x0A> 2012 | 1822.51 <0x0A> 2011 | 1708.46 <0x0A> 2010 | 1708.46 <0x0A> 2009 | 1238.7 <0x0A> 2008 | 1231.99 <0x0A> 2007 | 1238.7 <0x0A> 2006 | 1093.21 <0x0A> 2005 | 834.27 <0x0A> 2004 | 762.09 <0x0A> 2003 | 633.22 <0x0A> 2002 | 523.77 <0x0A> 2001 | 476.18 <0x0A> 2000 | 427.62 <0x0A> 1999 | 466.84 <0x0A> 1998 | 423.91 <0x0A> 1997 | 399.79 <0x0A> 1996 | 319.76 <0x0A> 1995 | 301. |
|
Are the bars sorted in this graph? | [
"No"
] | 0human
| Annual household income in U.S. dollars | Percentage of U.S. households <0x0A> Under 15,000 | 9.1% <0x0A> 15,000 to 24,099 | 8% <0x0A> 25,000 to 34,099 | 8.3% <0x0A> 35,000 to 49,099 | 11.7% <0x0A> 50,000 to 74,099 | 16.5% <0x0A> 75,000 to 99,099 | 12.3% <0x0A> 100,000 to 149,099 | 15.5% <0x0A> 150,000 to 199,999 | 8.3% <0x0A> 200,000 and over | 10.3% |
|
What's the sum of the first two bars (from the left) percentages? | [
"17.1"
] | 0human
| Annual household income in U.S. dollars | Percentage of U.S. households <0x0A> Under 15,000 | 9.1% <0x0A> 15,000 to 24,099 | 8% <0x0A> 25,000 to 34,099 | 8.3% <0x0A> 35,000 to 49,099 | 11.7% <0x0A> 50,000 to 74,099 | 16.5% <0x0A> 75,000 to 99,099 | 12.3% <0x0A> 100,000 to 149,099 | 15.5% <0x0A> 150,000 to 199,999 | 8.3% <0x0A> 200,000 and over | 10.3% |
|
What's the title of the x-axis? | [
"Number of views in billions"
] | 0human
| Characteristic | Number of views in billions <0x0A> "Baby Shark Dance" - Pinkfong Kids' Songs & Stories | 7.91 <0x0A> "Despacito"- Luis Fonsi featuring Daddy Yankee | 7.2 <0x0A> "Shape of You" - Ed Sheeran | 5.18 <0x0A> "See You Again" - Wiz Khalifa featuring Charlie Puth | 4.96 <0x0A> "Johny Johny Yes Papa" - LooLoo Kids | 4.77 <0x0A> "Masha and the Bear - Recipe for Disaster" - Get Movies | 4.41 <0x0A> "Uptown Funk" - Mark Ronson featuring Bruno Mars | 4.09 <0x0A> "Gangnam Style" - Pyy | 3.97 <0x0A> "Learning Colors - Colorful Eggs on a Farm" - Miroshka TV | 3.69 <0x0A> "Bath Song" - Cocomelon - Nursery Rhymes | 3.64 |
|
What's the difference between the most and least popular YouTube videos number of views? | [
"4.27"
] | 0human
| Characteristic | Number of views in billions <0x0A> "Baby Shark Dance" - Pinkfong Kids' Songs & Stories | 7.91 <0x0A> "Despacito"- Luis Fonsi featuring Daddy Yankee | 7.2 <0x0A> "Shape of You" - Ed Sheeran | 5.18 <0x0A> "See You Again" - Wiz Khalifa featuring Charlie Puth | 4.96 <0x0A> "Johny Johny Yes Papa" - LooLoo Kids | 4.77 <0x0A> "Masha and the Bear - Recipe for Disaster" - Get Movies | 4.41 <0x0A> "Uptown Funk" - Mark Ronson featuring Bruno Mars | 4.09 <0x0A> "Gangnam Style" - Pyy | 3.97 <0x0A> "Learning Colors - Colorful Eggs on a Farm" - Miroshka TV | 3.69 <0x0A> "Bath Song" - Cocomelon - Nursery Rhymes | 3.64 |
|
Are the gray bar values decreasing over the years? | [
"Yes"
] | 0human
| Characteristic | Supercenters | Neighborhood markets | Discount stores <0x0A> 2021 | 3570 | 799 | 374 <0x0A> 2020 | 3571 | 809 | 376 <0x0A> 2019 | 3570 | 813 | 386 <0x0A> 2018 | 3561 | 800 | 400 <0x0A> 2017 | 3522 | 715 | 415 <0x0A> 2016 | 3465 | 667 | 442 <0x0A> 2015 | 3407 | 639 | 470 <0x0A> 2014 | 3288 | 407 | 508 <0x0A> 2013 | 3158 | 286 | 561 <0x0A> 2012 | 3029 | 210 | 629 |
|
In which year the increase in the number of Walmart stores in US was maximum? | [
"2015"
] | 0human
| Characteristic | Supercenters | Neighborhood markets | Discount stores <0x0A> 2021 | 3570 | 799 | 374 <0x0A> 2020 | 3571 | 809 | 376 <0x0A> 2019 | 3570 | 813 | 386 <0x0A> 2018 | 3561 | 800 | 400 <0x0A> 2017 | 3522 | 715 | 415 <0x0A> 2016 | 3465 | 667 | 442 <0x0A> 2015 | 3407 | 639 | 470 <0x0A> 2014 | 3288 | 407 | 508 <0x0A> 2013 | 3158 | 286 | 561 <0x0A> 2012 | 3029 | 210 | 629 |
|
Which gender has the smallest population in the topmost stacked bar? | [
"Female"
] | 0human
| Age in year | Male | Female <0x0A> Under 5 | 10.01 | 9.57 <0x0A> 5 to 9 | 10.32 | 9.87 <0x0A> 10 to 14 | 10.62 | 10.18 <0x0A> 15 to 19 | 10.75 | 10.31 <0x0A> 20 to 24 | 11.06 | 10.57 <0x0A> 25 to 29 | 12.0 | 11.5 <0x0A> 30 to 34 | 11.35 | 11.08 <0x0A> 35 to 39 | 10.88 | 10.85 <0x0A> 40 to 44 | 9.91 | 10.91 <0x0A> 45 to 49 | 10.09 | 10.31 <0x0A> 50 to 54 | 10.09 | 10.39 <0x0A> 55 to 59 | 10.64 | 11.23 <0x0A> 60 to 64 | 9.86 | 10.71 <0x0A> 65 to 69 | 5.2 | 9.46 <0x0A> 70 to 74 | 6.5 | 7.54 <0x0A> 75 to 79 | 4.32 | 5.33 <0x0A> 80 to 84 | 2.68 | 3.64 <0x0A> 85 and over | 2.38 | 4.23 |
|
Which age group has the largest total number of residents? | [
"25 to 29"
] | 0human
| Age in year | Male | Female <0x0A> Under 5 | 10.01 | 9.57 <0x0A> 5 to 9 | 10.32 | 9.87 <0x0A> 10 to 14 | 10.62 | 10.18 <0x0A> 15 to 19 | 10.75 | 10.31 <0x0A> 20 to 24 | 11.06 | 10.57 <0x0A> 25 to 29 | 12.0 | 11.5 <0x0A> 30 to 34 | 11.35 | 11.08 <0x0A> 35 to 39 | 10.88 | 10.85 <0x0A> 40 to 44 | 9.91 | 10.91 <0x0A> 45 to 49 | 10.09 | 10.31 <0x0A> 50 to 54 | 10.09 | 10.39 <0x0A> 55 to 59 | 10.64 | 11.23 <0x0A> 60 to 64 | 9.86 | 10.71 <0x0A> 65 to 69 | 5.2 | 9.46 <0x0A> 70 to 74 | 6.5 | 7.54 <0x0A> 75 to 79 | 4.32 | 5.33 <0x0A> 80 to 84 | 2.68 | 3.64 <0x0A> 85 and over | 2.38 | 4.23 |
|
Which country has the tallest black bar? | [
"Canada"
] | 0human
| Characteristic | Males | Females <0x0A> North America | 77 | 81 <0x0A> Canada | 80 | 84 <0x0A> USA | 76 | 81 |
|
What's the largest difference between the female and male average life expectancy in a single country? | [
"5"
] | 0human
| Characteristic | Males | Females <0x0A> North America | 77 | 81 <0x0A> Canada | 80 | 84 <0x0A> USA | 76 | 81 |
|
At which year the blue line peaked? | [
"2019"
] | 0human
| Characteristic | Nike** | Adidas Group | Puma <0x0A> 2020 | 33.6 | 19.8 | 5.2 <0x0A> 2019 | 35.13 | 23.64 | 5.5 <0x0A> 2018 | 31.35 | 21.92 | 4.65 <0x0A> 2017 | 30.57 | 21.22 | 4.14 <0x0A> 2016 | 29.1 | 18.48 | 3.63 <0x0A> 2015 | 27.51 | 16.92 | 3.39 <0x0A> 2014 | 24.99 | 14.53 | 2.97 <0x0A> 2013 | 22.75 | 14.2 | 2.99 <0x0A> 2012 | 20.97 | 14.88 | 3.27 <0x0A> 2011 | 18.08 | 13.32 | 3.17 <0x0A> 2010 | 16.47 | 11.99 | 2.86 <0x0A> 2009 | 16.65 | 10.38 | 2.61 <0x0A> 2008 | 16.74 | 10.8 | 2.77 <0x0A> 2007 | 14.68 | 10.3 | 2.74 <0x0A> 2006 | 13.44 | 10.08 | 2.76 |
|
What's the largest decrease in the global revenue of Addidas Group in a single year? | [
"3.84"
] | 0human
| Characteristic | Nike** | Adidas Group | Puma <0x0A> 2020 | 33.6 | 19.8 | 5.2 <0x0A> 2019 | 35.13 | 23.64 | 5.5 <0x0A> 2018 | 31.35 | 21.92 | 4.65 <0x0A> 2017 | 30.57 | 21.22 | 4.14 <0x0A> 2016 | 29.1 | 18.48 | 3.63 <0x0A> 2015 | 27.51 | 16.92 | 3.39 <0x0A> 2014 | 24.99 | 14.53 | 2.97 <0x0A> 2013 | 22.75 | 14.2 | 2.99 <0x0A> 2012 | 20.97 | 14.88 | 3.27 <0x0A> 2011 | 18.08 | 13.32 | 3.17 <0x0A> 2010 | 16.47 | 11.99 | 2.86 <0x0A> 2009 | 16.65 | 10.38 | 2.61 <0x0A> 2008 | 16.74 | 10.8 | 2.77 <0x0A> 2007 | 14.68 | 10.3 | 2.74 <0x0A> 2006 | 13.44 | 10.08 | 2.76 |
|
In which age group the black bar value was maximum? | [
"Under 21"
] | 0human
| Characteristic | Male | Female <0x0A> Under 21 | 178747 | 209375 <0x0A> 21 and over | 95096 | 59369 |
|
In which age group the number of NCIC missing persons files were closer between the two genders? | [
"Under 21"
] | 0human
| Characteristic | Male | Female <0x0A> Under 21 | 178747 | 209375 <0x0A> 21 and over | 95096 | 59369 |
|
What's the rightmost value written on top of the black line? | [
"13.67"
] | 0human
| Characteristic | Nike** | Adidas*** | Puma*** <0x0A> 2010 | 11.52 | 7.14 | 1.89 <0x0A> 2011 | 13.43 | 8.13 | 1.99 <0x0A> 2012 | 13.51 | 9.15 | 2.11 <0x0A> 2013 | 14.64 | 9.46 | 1.88 <0x0A> 2014 | 16.21 | 8.09 | 1.56 <0x0A> 2015 | 18.32 | 9.13 | 1.65 <0x0A> 2016 | 19.87 | 10.67 | 1.71 <0x0A> 2017 | 21.08 | 14.89 | 2.41 <0x0A> 2018 | 22.27 | 14.62 | 2.5 <0x0A> 2019 | 24.22 | 15.14 | 2.86 <0x0A> 2020 | 23.31 | 13.67 | 2.91 |
|
At which year the difference in revenue from footwear between Nike and Addidas was minimum? | [
"2012"
] | 0human
| Characteristic | Nike** | Adidas*** | Puma*** <0x0A> 2010 | 11.52 | 7.14 | 1.89 <0x0A> 2011 | 13.43 | 8.13 | 1.99 <0x0A> 2012 | 13.51 | 9.15 | 2.11 <0x0A> 2013 | 14.64 | 9.46 | 1.88 <0x0A> 2014 | 16.21 | 8.09 | 1.56 <0x0A> 2015 | 18.32 | 9.13 | 1.65 <0x0A> 2016 | 19.87 | 10.67 | 1.71 <0x0A> 2017 | 21.08 | 14.89 | 2.41 <0x0A> 2018 | 22.27 | 14.62 | 2.5 <0x0A> 2019 | 24.22 | 15.14 | 2.86 <0x0A> 2020 | 23.31 | 13.67 | 2.91 |
|
Which age group has the shortest blue bar? | [
"13-17"
] | 0human
| Characteristic | Female | Male <0x0A> 13-17 | 2.5% | 3.2% <0x0A> 18-24 | 9.5% | 14% <0x0A> 25-34 | 12.6% | 18.9% <0x0A> 35-44 | 7.7% | 9.9% <0x0A> 45-54 | 5% | 5.4% <0x0A> 55-64 | 3.3% | 3.1% <0x0A> 65+ | 2.6% | 2.3% |
|
What's smallest difference between the male and female share of active Facebook users in the same age group? | [
"0.2"
] | 0human
| Characteristic | Female | Male <0x0A> 13-17 | 2.5% | 3.2% <0x0A> 18-24 | 9.5% | 14% <0x0A> 25-34 | 12.6% | 18.9% <0x0A> 35-44 | 7.7% | 9.9% <0x0A> 45-54 | 5% | 5.4% <0x0A> 55-64 | 3.3% | 3.1% <0x0A> 65+ | 2.6% | 2.3% |
|
What's the sum of the two tallest blue bars? | [
"135"
] | 0human
| Characteristic | Males | Females <0x0A> Northern Africa | 72 | 75 <0x0A> Eastern Africa | 63 | 67 <0x0A> Africa (total) | 62 | 65 <0x0A> Southern Africa | 61 | 67 <0x0A> Middle Africa | 58 | 62 <0x0A> Western Africa | 57 | 59 |
|
In which african region both 2020 babies genders are expected to live longer? | [
"Northern Africa"
] | 0human
| Characteristic | Males | Females <0x0A> Northern Africa | 72 | 75 <0x0A> Eastern Africa | 63 | 67 <0x0A> Africa (total) | 62 | 65 <0x0A> Southern Africa | 61 | 67 <0x0A> Middle Africa | 58 | 62 <0x0A> Western Africa | 57 | 59 |
|
What's the value of the rightmost bar? | [
"49"
] | 0human
| Characteristic | Remain | Leave <0x0A> Male | 45% | 55% <0x0A> Female | 51% | 49% |
|
What's the difference between the tallest and shortest bars? | [
"10"
] | 0human
| Characteristic | Remain | Leave <0x0A> Male | 45% | 55% <0x0A> Female | 51% | 49% |
|
Is the value of the blue bar decreasing over the years? | [
"Yes"
] | 0human
| Characteristic | 0-14 years | 15-64 years | 65 years + <0x0A> 2020 | 26.16% | 67.27% | 6.57% <0x0A> 2019 | 26.62% | 67% | 6.38% <0x0A> 2018 | 27.05% | 66.77% | 6.18% <0x0A> 2017 | 27.48% | 66.54% | 5.98% <0x0A> 2016 | 27.93% | 66.27% | 5.79% <0x0A> 2015 | 28.44% | 65.94% | 9.61% <0x0A> 2014 | 28.93% | 65.6% | 5.48% <0x0A> 2013 | 29.43% | 65.21% | 5.36% <0x0A> 2012 | 29.93% | 64.81% | 5.27% <0x0A> 2011 | 30.4% | 64.43% | 5.17% <0x0A> 2010 | 30.81% | 64.11% | 5.08% |
|
Which two age groups are increasing over the years? | [
"[15-64 years, 65 years +]"
] | 0human
| Characteristic | 0-14 years | 15-64 years | 65 years + <0x0A> 2020 | 26.16% | 67.27% | 6.57% <0x0A> 2019 | 26.62% | 67% | 6.38% <0x0A> 2018 | 27.05% | 66.77% | 6.18% <0x0A> 2017 | 27.48% | 66.54% | 5.98% <0x0A> 2016 | 27.93% | 66.27% | 5.79% <0x0A> 2015 | 28.44% | 65.94% | 9.61% <0x0A> 2014 | 28.93% | 65.6% | 5.48% <0x0A> 2013 | 29.43% | 65.21% | 5.36% <0x0A> 2012 | 29.93% | 64.81% | 5.27% <0x0A> 2011 | 30.4% | 64.43% | 5.17% <0x0A> 2010 | 30.81% | 64.11% | 5.08% |
|
Which race has the tallest bars? | [
"White"
] | 0human
| Characteristic | Percentage of deaths in custody | Percentage of population* <0x0A> White | 85% | 86% <0x0A> Black | 8% | 3% <0x0A> Asian | 3% | 8% <0x0A> Mixed | 2% | 2% <0x0A> Other | 1% | 1% |
|
Which two race groups have percentages of deaths in police custody that's the same as their percentages in England and whales population? | [
"[Mixed, Other]"
] | 0human
| Characteristic | Percentage of deaths in custody | Percentage of population* <0x0A> White | 85% | 86% <0x0A> Black | 8% | 3% <0x0A> Asian | 3% | 8% <0x0A> Mixed | 2% | 2% <0x0A> Other | 1% | 1% |
|
Which year witnessed the smallest value of the blue bar? | [
"2018"
] | 0human
| Characteristic | Agriculture | Industry | Services <0x0A> 2020 | 7.7% | 37.8% | 54.5% <0x0A> 2019 | 7.1% | 39% | 53.9% <0x0A> 2018 | 7% | 39.7% | 53.3% <0x0A> 2017 | 7.5% | 39.9% | 52.7% <0x0A> 2016 | 8.1% | 39.6% | 52.4% <0x0A> 2015 | 8.4% | 40.8% | 50.8% <0x0A> 2014 | 8.6% | 43.1% | 48.3% <0x0A> 2013 | 8.9% | 44.2% | 46.9% <0x0A> 2012 | 9.1% | 45.4% | 45.5% <0x0A> 2011 | 9.2% | 46.5% | 44.3% <0x0A> 2010 | 9.3% | 46.5% | 44.2% |
|
Which ecenomic sector has the largest increase of its GDP share between 2010 and 2020? | [
"Services"
] | 0human
| Characteristic | Agriculture | Industry | Services <0x0A> 2020 | 7.7% | 37.8% | 54.5% <0x0A> 2019 | 7.1% | 39% | 53.9% <0x0A> 2018 | 7% | 39.7% | 53.3% <0x0A> 2017 | 7.5% | 39.9% | 52.7% <0x0A> 2016 | 8.1% | 39.6% | 52.4% <0x0A> 2015 | 8.4% | 40.8% | 50.8% <0x0A> 2014 | 8.6% | 43.1% | 48.3% <0x0A> 2013 | 8.9% | 44.2% | 46.9% <0x0A> 2012 | 9.1% | 45.4% | 45.5% <0x0A> 2011 | 9.2% | 46.5% | 44.3% <0x0A> 2010 | 9.3% | 46.5% | 44.2% |
|
In which year the increase of the blue line value compared to the previous year was maximum? | [
"2020"
] | 0human
| Characteristic | Male | Female <0x0A> 2020* | 723.34 | 688.44 <0x0A> 2019 | 715.27 | 684.78 <0x0A> 2018 | 713.51 | 681.87 <0x0A> 2017 | 711.37 | 678.71 <0x0A> 2016 | 708.15 | 674.56 <0x0A> 2015 | 704.14 | 670.48 <0x0A> 2014 | 700.79 | 667.03 <0x0A> 2013 | 697.28 | 663.44 <0x0A> 2012 | 693.95 | 660.09 <0x0A> 2011 | 690.68 | 656.67 <0x0A> 2010 | 687.48 | 653.43 |
|
Did the male population in China ever decrease in any year between 2010 and 2020? | [
"No"
] | 0human
| Characteristic | Male | Female <0x0A> 2020* | 723.34 | 688.44 <0x0A> 2019 | 715.27 | 684.78 <0x0A> 2018 | 713.51 | 681.87 <0x0A> 2017 | 711.37 | 678.71 <0x0A> 2016 | 708.15 | 674.56 <0x0A> 2015 | 704.14 | 670.48 <0x0A> 2014 | 700.79 | 667.03 <0x0A> 2013 | 697.28 | 663.44 <0x0A> 2012 | 693.95 | 660.09 <0x0A> 2011 | 690.68 | 656.67 <0x0A> 2010 | 687.48 | 653.43 |
|
In which time the two lines were closest to each other in this chart? | [
"Mar '21"
] | 0human
| Characteristic | Disapprove | Approve <0x0A> Jun '21 | 71% | 26% <0x0A> May '21 | 63% | 31% <0x0A> Apr '21 | 62% | 33% <0x0A> Mar '21 | 61% | 36% <0x0A> Feb '21 | 62% | 35% <0x0A> Jan '21 | 71% | 25% <0x0A> Dec '20 | 82% | 15% <0x0A> Nov '20 | 73% | 23% <0x0A> Oct '20 | 77% | 19% <0x0A> Sep '20 | 80% | 17% <0x0A> Aug '20 | 75% | 21% <0x0A> Jul '20 | 77% | 18% <0x0A> Jun '20 | 71% | 25% |
|
In which time the disagreement over the way the congress is handling its job was maximum? | [
"Dec '20"
] | 0human
| Characteristic | Disapprove | Approve <0x0A> Jun '21 | 71% | 26% <0x0A> May '21 | 63% | 31% <0x0A> Apr '21 | 62% | 33% <0x0A> Mar '21 | 61% | 36% <0x0A> Feb '21 | 62% | 35% <0x0A> Jan '21 | 71% | 25% <0x0A> Dec '20 | 82% | 15% <0x0A> Nov '20 | 73% | 23% <0x0A> Oct '20 | 77% | 19% <0x0A> Sep '20 | 80% | 17% <0x0A> Aug '20 | 75% | 21% <0x0A> Jul '20 | 77% | 18% <0x0A> Jun '20 | 71% | 25% |
|
What's the only region in Philippines which has a visible black bar in the graph? | [
"National Capital Region (NCR)"
] | 0human
| Characteristic | Under investigation | Confirmed cases <0x0A> National Capital Region (NCR) | 23 | 2 <0x0A> Central Luzon | 12 | 0 <0x0A> Central Visayas | 1 | 0 <0x0A> Cordillera Administrative Region (CAR) | 1 | 0 <0x0A> SOCCSKSARCEN | 1 | 0 <0x0A> Cagayan Valley | 1 | 0 <0x0A> CALABARZON | 1 | 0 <0x0A> Ilocos Region | 1 | 0 |
|
What's the average number of under investigation cases in the largest two regions? | [
"17.5"
] | 0human
| Characteristic | Under investigation | Confirmed cases <0x0A> National Capital Region (NCR) | 23 | 2 <0x0A> Central Luzon | 12 | 0 <0x0A> Central Visayas | 1 | 0 <0x0A> Cordillera Administrative Region (CAR) | 1 | 0 <0x0A> SOCCSKSARCEN | 1 | 0 <0x0A> Cagayan Valley | 1 | 0 <0x0A> CALABARZON | 1 | 0 <0x0A> Ilocos Region | 1 | 0 |
|
What's the sum of the two lines peaks? | [
"2103.23"
] | 0human
| Characteristic | Data breaches | Million records exposed <0x0A> 2020* | 1001 | 155.8 <0x0A> 2019 | 1473 | 64.68 <0x0A> 2018 | 1257 | 471.23 <0x0A> 2017 | 1632 | 197.6 <0x0A> 2016 | 1106 | 36.6 <0x0A> 2015 | 784 | 169.07 <0x0A> 2014 | 783 | 85.61 <0x0A> 2013 | 614 | 91.98 <0x0A> 2012 | 447 | 17.3 <0x0A> 2011 | 419 | 22.9 <0x0A> 2010 | 662 | 16.2 <0x0A> 2009 | 498 | 222.5 <0x0A> 2008 | 656 | 35.7 <0x0A> 2007 | 446 | 127.7 <0x0A> 2006 | 321 | 19.1 <0x0A> 2005 | 157 | 6.9 |
|
In which year the data breaches and records exposed had the closest values? | [
"2005"
] | 0human
| Characteristic | Data breaches | Million records exposed <0x0A> 2020* | 1001 | 155.8 <0x0A> 2019 | 1473 | 64.68 <0x0A> 2018 | 1257 | 471.23 <0x0A> 2017 | 1632 | 197.6 <0x0A> 2016 | 1106 | 36.6 <0x0A> 2015 | 784 | 169.07 <0x0A> 2014 | 783 | 85.61 <0x0A> 2013 | 614 | 91.98 <0x0A> 2012 | 447 | 17.3 <0x0A> 2011 | 419 | 22.9 <0x0A> 2010 | 662 | 16.2 <0x0A> 2009 | 498 | 222.5 <0x0A> 2008 | 656 | 35.7 <0x0A> 2007 | 446 | 127.7 <0x0A> 2006 | 321 | 19.1 <0x0A> 2005 | 157 | 6.9 |
|
What's the average of the red bars in the chart? | [
"62.68"
] | 0human
| Characteristic | Overall | Men | Women <0x0A> 2018 | 74.37% | 82.37% | 65.79% <0x0A> 2015 | 71.96% | 80.94% | 62.98% <0x0A> 2011 | 69.3% | 78.88% | 59.28% |
|
Which year witnessed the largest increase in the literacy rate among Men? | [
"2015"
] | 0human
| Characteristic | Overall | Men | Women <0x0A> 2018 | 74.37% | 82.37% | 65.79% <0x0A> 2015 | 71.96% | 80.94% | 62.98% <0x0A> 2011 | 69.3% | 78.88% | 59.28% |
|
Which gender is represented by the blue bars? | [
"Males"
] | 0human
| Characteristic | Males | Females <0x0A> Northern America | 77 | 81 <0x0A> Oceania | 77 | 81 <0x0A> Europe | 75 | 82 <0x0A> Latin America and the Caribbean | 72 | 79 <0x0A> Asia | 71 | 75 <0x0A> Worldwide | 70 | 75 <0x0A> Africa | 62 | 65 |
|
What is the largest sum of the average life expectancy of both genders in a single continent? | [
"158"
] | 0human
| Characteristic | Males | Females <0x0A> Northern America | 77 | 81 <0x0A> Oceania | 77 | 81 <0x0A> Europe | 75 | 82 <0x0A> Latin America and the Caribbean | 72 | 79 <0x0A> Asia | 71 | 75 <0x0A> Worldwide | 70 | 75 <0x0A> Africa | 62 | 65 |
|
What's the difference between the leftmost and rightmost bars values in this graph? | [
"5"
] | 0human
| Characteristic | Males | Females <0x0A> East Asia | 76 | 80 <0x0A> Western Asia | 73 | 77 <0x0A> Asia (total) | 71 | 75 <0x0A> Central Asia | 70 | 76 <0x0A> Southeast Asia | 69 | 75 <0x0A> South Asia | 68 | 71 |
|
Which asian region has the smallest difference in the average life expectancy between the two genders? | [
"South Asia"
] | 0human
| Characteristic | Males | Females <0x0A> East Asia | 76 | 80 <0x0A> Western Asia | 73 | 77 <0x0A> Asia (total) | 71 | 75 <0x0A> Central Asia | 70 | 76 <0x0A> Southeast Asia | 69 | 75 <0x0A> South Asia | 68 | 71 |
|
Which gender has invisible bars in this graph? | [
"Unknown"
] | 0human
| Characteristic | Male | Female | Unknown <0x0A> 2017 | 940 | 45 | 2 <0x0A> 2018 | 942 | 53 | 1 <0x0A> 2019 | 961 | 43 | 0 <0x0A> 2020 | 983 | 38 | 0 <0x0A> 2021* | 420 | 20 | 0 |
|
In which gender the number of people shot to death by police in the US was increasing over the years till 2020? | [
"Male"
] | 0human
| Characteristic | Male | Female | Unknown <0x0A> 2017 | 940 | 45 | 2 <0x0A> 2018 | 942 | 53 | 1 <0x0A> 2019 | 961 | 43 | 0 <0x0A> 2020 | 983 | 38 | 0 <0x0A> 2021* | 420 | 20 | 0 |
|
Which land drilling company has two bars of the same length? | [
"Precision Drilling Corporation"
] | 0human
| Characteristic | 2018 | 2019 <0x0A> Nabors Industries Ltd. | 3058 | 3043 <0x0A> Patterson-UTI Energy Inc. | 3327 | 2471 <0x0A> Helmerich & Payne, Inc. | 2487 | 2798 <0x0A> Precision Drilling Corporation | 1541 | 1541 |
|
Which land drilling company had the largest increase in revenue from 2018 to 2019? | [
"Helmerich & Payne, Inc."
] | 0human
| Characteristic | 2018 | 2019 <0x0A> Nabors Industries Ltd. | 3058 | 3043 <0x0A> Patterson-UTI Energy Inc. | 3327 | 2471 <0x0A> Helmerich & Payne, Inc. | 2487 | 2798 <0x0A> Precision Drilling Corporation | 1541 | 1541 |
|
Are the values of the yellow bar sorted from top to bottom? | [
"Yes"
] | 0human
| Characteristic | Gold | Silver | Bronze <0x0A> Norway | 102 | 125 | 111 <0x0A> United States | 102 | 112 | 85 <0x0A> Germany | 92 | 85 | 60 <0x0A> Soviet Union | 72 | 57 | 59 <0x0A> Canada | 73 | 64 | 62 <0x0A> Austria | 64 | 83 | 87 <0x0A> Sweden | 57 | 46 | 55 <0x0A> Switzerland | 55 | 45 | 52 <0x0A> Russia | 47 | 38 | 35 <0x0A> The Netherlands | 33 | 40 | 41 |
|
How many more medals does Norway have over the US overall? | [
"63"
] | 0human
| Characteristic | Gold | Silver | Bronze <0x0A> Norway | 102 | 125 | 111 <0x0A> United States | 102 | 112 | 85 <0x0A> Germany | 92 | 85 | 60 <0x0A> Soviet Union | 72 | 57 | 59 <0x0A> Canada | 73 | 64 | 62 <0x0A> Austria | 64 | 83 | 87 <0x0A> Sweden | 57 | 46 | 55 <0x0A> Switzerland | 55 | 45 | 52 <0x0A> Russia | 47 | 38 | 35 <0x0A> The Netherlands | 33 | 40 | 41 |
|
Which bar is missing in the PwC stacked bars? | [
"Other***"
] | 0human
| Characteristic | Audit/Assurance/AERS* | Advisory/Consulting** | Tax | Other*** <0x0A> Deloitte | 15.5 | 19.8 | 8.7 | 3.8 <0x0A> PwC | 17.6 | 14.68 | 10.75 | - <0x0A> EY | 12.8 | 10.5 | 9.8 | 4.1 <0x0A> KPMG | 11.07 | 11.67 | 6.48 | - |
|
Which firm has the second largest revenue in Consulting? | [
"PwC"
] | 0human
| Characteristic | Audit/Assurance/AERS* | Advisory/Consulting** | Tax | Other*** <0x0A> Deloitte | 15.5 | 19.8 | 8.7 | 3.8 <0x0A> PwC | 17.6 | 14.68 | 10.75 | - <0x0A> EY | 12.8 | 10.5 | 9.8 | 4.1 <0x0A> KPMG | 11.07 | 11.67 | 6.48 | - |
|
What's the value of the bottom-right bar? | [
"14"
] | 0human
| Characteristic | Salary | Endorsements <0x0A> LeBron James (Cleveland Cavaliers) | 31.4 | 64 <0x0A> Stephen Curry (Golden State Warriors) | 34.4 | 40 <0x0A> Kevin Durant (Golden State Warriors) | 31.2 | 34 <0x0A> Russell Westbrook (Oklahoma City Thunder) | 33.0 | 25 <0x0A> James Harden (Houston Rockets) | 33.0 | 17 <0x0A> Giannis Antetokounmpo (Milwaukee Bucks) | 22.0 | 27 <0x0A> Klay Thompson (Golden State Warriors) | 28.3 | 15 <0x0A> Kyrie Irving (Brooklyn Nets) | 26.0 | 17 <0x0A> Chris Paul (Los Angeles Clippers) | 33.1 | 7 <0x0A> Damian Lillard (Portland Trail Blazers) | 25.0 | 14 |
|
Which player has endorsements which are less than half the endorsements of Kyrie Irving? | [
"Chris Paul (Los Angeles Clippers)"
] | 0human
| Characteristic | Salary | Endorsements <0x0A> LeBron James (Cleveland Cavaliers) | 31.4 | 64 <0x0A> Stephen Curry (Golden State Warriors) | 34.4 | 40 <0x0A> Kevin Durant (Golden State Warriors) | 31.2 | 34 <0x0A> Russell Westbrook (Oklahoma City Thunder) | 33.0 | 25 <0x0A> James Harden (Houston Rockets) | 33.0 | 17 <0x0A> Giannis Antetokounmpo (Milwaukee Bucks) | 22.0 | 27 <0x0A> Klay Thompson (Golden State Warriors) | 28.3 | 15 <0x0A> Kyrie Irving (Brooklyn Nets) | 26.0 | 17 <0x0A> Chris Paul (Los Angeles Clippers) | 33.1 | 7 <0x0A> Damian Lillard (Portland Trail Blazers) | 25.0 | 14 |
Subsets and Splits