sail/symbolic-instruction-tuning huggingface dataset:
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

what were the tournaments? what surfaces were they played on? how many times were the games held on a surface other than a hard surface or grass? which one of these wasn't in stuttgart?

Answer: Swiss Open, Switzerland
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

what are all of the tournaments?

Answer: Kremlin Cup, Russia, UNICEF Open, Netherlands, International Tennis Championships, United States, Aegon International, United Kingdom, Malaysian Open, Malaysia, Kremlin Cup, Russia, St. Petersburg Open, Russia, Chennai Open, India, Stuttgart Open, Germany, Swiss Open, Switzerland, Chennai Open, India
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

what are all of the tournaments? what type of surface was used?

Answer: Hard (i), Grass, Hard, Grass, Hard (i), Hard (i), Hard (i), Hard, Clay, Clay, Hard
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

what are all of the tournaments? what type of surface was used? stuttgart open, germany shared its surface type with which other tournament?

Answer: Swiss Open, Switzerland
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

how many different types of surfaces are there?

Answer: 4.
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

how many different types of surfaces are there? what type of surfaces are there?

Answer: Hard (i), Grass, Hard, Grass, Hard (i), Hard (i), Hard (i), Hard, Clay, Clay, Hard
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

how many different types of surfaces are there? what type of surfaces are there? how many tournaments play on a clay surface?

Answer: 2.
Please answer a question about the following table 

header: Outcome | No. | Date | Tournament | Surface | Opponent | Score row 1 : Runner-up | 1.0 | 25 October 2009 | Kremlin Cup, Russia | Hard (i) | Mikhail Youzhny | 7-6(7-5), 0-6, 
row 2 : Runner-up | 2.0 | 19 June 2010 | UNICEF Open, Netherlands | Grass | Sergiy Stakhovsky | 3-6, 0-6
row 3 : Runner-up | 3.0 | 27 February 2011 | International Tennis Championships, United States | Hard | Juan Martin del Potro | 4-6, 4-6
row 4 : Runner-up | 4.0 | 18 June 2011 | Aegon International, United Kingdom | Grass | Andreas Seppi | 6-7(5-7), 6-3, 3-5
row 5 : Winner | 1.0 | 2 October 2011 | Malaysian Open, Malaysia | Hard (i) | Marcos Baghdatis | 6-4, 7-5
row 6 : Winner | 2.0 | 23 October 2011 | Kremlin Cup, Russia | Hard (i) | Viktor Troicki | 6-4, 6-2
row 7 : Runner-up | 5.0 | 30 October 2011 | St. Petersburg Open, Russia | Hard (i) | Marin Cilic | 3-6, 6-3, 2-6
row 8 : Runner-up | 6.0 | 8 January 2012 | Chennai Open, India | Hard | Milos Raonic | 7-6(7-4), 6-7(4
row 9 : Winner | 3.0 | 15 July 2012 | Stuttgart Open, Germany | Clay | Juan Monaco | 6-4, 5-7, 6-3
row 10 : Runner-up | 7.0 | 22 July 2012 | Swiss Open, Switzerland | Clay | Thomaz Bellucci | 7-6(8-6), 4-6, 2
row 11 : Winner | 4.0 | 6 January 2013 | Chennai Open, India | Hard | Roberto Bautista-Agut | 3-6, 6-1, 6-3

how many different types of surfaces are there? what type of surfaces are there? how many tournaments play on a clay surface? besides the tournament in germany, what other tournament has a clay surface?

Answer: Swiss Open, Switzerland
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what are all of the countries?

Answer: Canada/United States, China, Japan, United Kingdom, France, South Korea, India, Germany, Russia, Australia, Italy, Brazil, World
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what are all of the countries? for which years was this data recorded?

Answer: 2012, 2013, 2013, 2012, 2012, 2013, 2012, 2012, 2012, 2012, 2013, 2013, 2012
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what are all of the countries? for which years was this data recorded? and what was their box office revenue?

Answer: $10.8 billion, $3.6 billion, $1.88 billion, $1.7 billion, $1.7 billion, $1.47 billion, $1.4 billion, $1.3 billion, $1.2 billion, $1.2 billion, $0.84 billion, $0.72 billion, $34.7 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what are all of the countries? for which years was this data recorded? and what was their box office revenue? what about just in 2012?

Answer: $10.8 billion, $1.7 billion, $1.7 billion, $1.4 billion, $1.3 billion, $1.2 billion, $1.2 billion, $34.7 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what are all of the countries? for which years was this data recorded? and what was their box office revenue? what about just in 2012? now, which country had the smallest of those revenues?

Answer: Brazil
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what were the total box office revenues made by the countries?

Answer: $10.8 billion, $3.6 billion, $1.88 billion, $1.7 billion, $1.7 billion, $1.47 billion, $1.4 billion, $1.3 billion, $1.2 billion, $1.2 billion, $0.84 billion, $0.72 billion, $34.7 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what were the total box office revenues made by the countries? what were the revenues in 2013?

Answer: $3.6 billion, $1.88 billion, $1.47 billion, $0.84 billion, $0.72 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what were the total box office revenues made by the countries? what were the revenues in 2013? of these amounts, which was the least?

Answer: $0.72 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

what were the total box office revenues made by the countries? what were the revenues in 2013? of these amounts, which was the least? what country made this amount?

Answer: Brazil
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

how much are all of the amounts of money made buy each country?

Answer: $10.8 billion, $3.6 billion, $1.88 billion, $1.7 billion, $1.7 billion, $1.47 billion, $1.4 billion, $1.3 billion, $1.2 billion, $1.2 billion, $0.84 billion, $0.72 billion, $34.7 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

how much are all of the amounts of money made buy each country? which is the least amount?

Answer: $0.72 billion
Please answer a question about the following table 

header: Rank | Country | Box Office | Year | Box office from national films row 1 : 1 | Canada/United States | $10.8 billion | 2012 | -
row 2 : 2 | China | $3.6 billion | 2013 | 59% (2013)
row 3 : 3 | Japan | $1.88 billion | 2013 | 61% (2013)
row 4 : 4 | United Kingdom | $1.7 billion | 2012 | 36.1% (2011)
row 5 : 5 | France | $1.7 billion | 2012 | 33.3% (2013)
row 6 : 6 | South Korea | $1.47 billion | 2013 | 59.7% (2013)
row 7 : 7 | India | $1.4 billion | 2012 | -
row 8 : 8 | Germany | $1.3 billion | 2012 | -
row 9 : 9 | Russia | $1.2 billion | 2012 | -
row 10 : 10 | Australia | $1.2 billion | 2012 | 4.1% (2011)
row 11 : 11 | Italy | $0.84 billion | 2013 | 30% (2013)
row 12 : 12 | Brazil | $0.72 billion | 2013 | 17% (2013)
row 13 : - | World | $34.7 billion | 2012 | -

how much are all of the amounts of money made buy each country? which is the least amount? which country made this amount?

Answer: Brazil
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what was the highest rankings the americans had?

Answer: 1st
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what was the highest rankings the americans had? what season was that?

Answer: 1945/46
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what were the rankings the americans had each season?

Answer: 6th, 2nd, 2nd, American, 4th, American, 5th, American, 2nd(t), 3rd, 5th, 5th, 3rd, 4th, 1st, 4th, 4th, Withdrew after 3 games
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what were the rankings the americans had each season? in what season did the team rank 1st?

Answer: 1945/46
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what were the regular season finishes by the baltimore americans?

Answer: 6th, 2nd, 2nd, American, 4th, American, 5th, American, 2nd(t), 3rd, 5th, 5th, 3rd, 4th, 1st, 4th, 4th, Withdrew after 3 games
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what were the regular season finishes by the baltimore americans? which of these were the best finish?

Answer: 1st
Please answer a question about the following table 

header: Year | Division | League | Reg. Season | Playoffs | National Cup row 1 : 1934/35 | nan | ASL | 6th | No playoff | ?
row 2 : 1935/36 | nan | ASL | 2nd | No playoff | ?
row 3 : 1936/37 | nan | ASL | 2nd, American | 1st Round | ?
row 4 : 1937/38 | nan | ASL | 4th, American | Did not qualify | ?
row 5 : 1938/39 | nan | ASL | 5th, American | Did not qualify | ?
row 6 : 1939/40 | nan | ASL | 2nd(t) | No playoff | Co-champion
row 7 : 1940/41 | nan | ASL | 3rd | No playoff | ?
row 8 : 1941/42 | nan | ASL | 5th | No playoff | ?
row 9 : 1942/43 | nan | ASL | 5th | No playoff | ?
row 10 : 1943/44 | nan | ASL | 3rd | No playoff | ?
row 11 : 1944/45 | nan | ASL | 4th | No playoff | ?
row 12 : 1945/46 | nan | ASL | 1st | Champion (no playoff) | ?
row 13 : 1946/47 | nan | ASL | 4th | No playoff | ?
row 14 : 1947/48 | nan | ASL | 4th | No playoff | ?
row 15 : 1948/49 | nan | ASL | Withdrew after 3 games | nan | nan

what were the regular season finishes by the baltimore americans? which of these were the best finish? which year was this?

Answer: 1945/46
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values?

Answer: 80, 80, 80, 80, 79, 79, 79, 77, 76, 76, 58, 47, 45, 24, 22, 21, 5, 1
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values? of these, which are 80?

Answer: 80, 80, 80, 80
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values? of these, which are 80? of these, what are the no values?

Answer: 11, 17, 4, 9
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values? of these, which are 80? of these, what are the no values? of these values, which are less than 12?

Answer: 11, 4, 9
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values? of these, which are 80? of these, what are the no values? of these values, which are less than 12? of these, what are the grid values?

Answer: 1, 2, 8
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values? of these, which are 80? of these, what are the no values? of these values, which are less than 12? of these, what are the grid values? which of these are less than 3?

Answer: 1, 2
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

what are the laps with values? of these, which are 80? of these, what are the no values? of these values, which are less than 12? of these, what are the grid values? which of these are less than 3? what is the time difference between these two drivers?

Answer: + 53.3
Please answer a question about the following table 

header: Pos | No | Driver | Constructor | Laps | Time/Retired | Grid | Points row 1 : 1 | 11 | Jackie Stewart | Tyrrell-Ford | 80.0 | 1:52:21.3 | 1.0 | 9.0
row 2 : 2 | 17 | Ronnie Peterson | March-Ford | 80.0 | + 25.6 | 6.0 | 6.0
row 3 : 3 | 4 | Jacky Ickx | Ferrari | 80.0 | + 53.3 | 2.0 | 4.0
row 4 : 4 | 9 | Denny Hulme | McLaren-Ford | 80.0 | + 1:06.7 | 8.0 | 3.0
row 5 : 5 | 1 | Emerson Fittipaldi | Lotus-Ford | 79.0 | + 1 Lap | 17.0 | 2.0
row 6 : 6 | 24 | Rolf Stommelen | Surtees-Ford | 79.0 | + 1 Lap | 16.0 | 1.0
row 7 : 7 | 22 | John Surtees | Surtees-Ford | 79.0 | + 1 Lap | 10.0 | nan
row 8 : 8 | 27 | Henri Pescarolo | March-Ford | 77.0 | + 3 Laps | 13.0 | nan
row 9 : 9 | 15 | Pedro Rodriguez | BRM | 76.0 | + 4 Laps | 5.0 | nan
row 10 : 10 | 8 | Tim Schenken | Brabham-Ford | 76.0 | + 4 Laps | 18.0 | nan
row 11 : Ret | 14 | Jo Siffert | BRM | 58.0 | Oil Pipe | 3.0 | nan
row 12 : Ret | 21 | Jean-Pierre Beltoise | Matra | 47.0 | Differential | 7.0 | nan
row 13 : Ret | 20 | Chris Amon | Matra | 45.0 | Differential | 4.0 | nan
row 14 : Ret | 5 | Clay Regazzoni | Ferrari | 24.0 | Accident | 11.0 | nan
row 15 : Ret | 10 | Peter Gethin | McLaren-Ford | 22.0 | Accident | 14.0 | nan
row 16 : Ret | 2 | Reine Wisell | Lotus-Ford | 21.0 | Wheel bearing | 12.0 | nan
row 17 : Ret | 12 | Francois Cevert | Tyrrell-Ford | 5.0 | Accident | 15.0 | nan
row 18 : Ret | 7 | Graham Hill | Brabham-Ford | 1.0 | Accident | 9.0 | nan
row 19 : DNQ | 16 | Howden Ganley | BRM | nan | nan | nan | nan
row 20 : DNQ | 6 | Mario Andretti | Ferrari | nan | nan | nan | nan
row 21 : DNQ | 19 | Nanni Galli* | March-Alfa-Romeo | nan | nan | nan | nan
row 22 : DNQ | 18 | Alex Soler-Roig | March-Ford | nan | nan | nan | nan
row 23 : DNQ | 28 | Skip Barber | March-Ford | nan | nan | nan | nan

who finished the race first?

Answer: Jackie Stewart