Task: In this task, you are given a natural language interpretation of commands (consist of logical operations) to select relevant rows from the given table. Your job is to generate command (in terms of logical operations) from given natural language interpretation. Define body (contains a collection of statements that define what the this logical operator does) of each logical operator between '{}' parenthesis. Here are the definitions of logical operators that you can use while generating command: 
 1. count: returns the number of rows in the view. 
 2. only: returns whether there is exactly one row in the view. 
 3. hop: returns the value under the header column of the row. 
 4. and: returns the boolean operation result of two arguments. 
 5. max/min/avg/sum: returns the max/min/average/sum of the values under the header column. 
 6. nth_max/nth_min: returns the n-th max/n-th min of the values under the header column. 
 7. argmax/argmin: returns the row with the max/min value in header column. 
 8. nth_argmax/nth_argmin: returns the row with the n-th max/min value in header column. 
 9. eq/not_eq: returns if the two arguments are equal. 
 10. round_eq: returns if the two arguments are roughly equal under certain tolerance. 
 11. greater/less: returns if the first argument is greater/less than the second argument. 
 12. diff: returns the difference between two arguments. 
 13. filter_eq/ filter_not_eq: returns the subview whose values under the header column is equal/not equal to the third argument. 
 14. filter_greater/filter_less: returns the subview whose values under the header column is greater/less than the third argument. 
 15. filter_greater_eq /filter_less_eq: returns the subview whose values under the header column is greater/less or equal than the third argument. 
 16. filter_all: returns the view itself for the case of describing the whole table 
 17. all_eq/not_eq: returns whether all the values under the header column are equal/not equal to the third argument. 
 18. all_greater/less: returns whether all the values under the header column are greater/less than the third argument. 
 19. all_greater_eq/less_eq: returns whether all the values under the header column are greater/less or equal to the third argument. 
 20. most_eq/not_eq: returns whether most of the values under the header column are equal/not equal to the third argument. 
 21. most_greater/less: returns whether most of the values under the header column are greater/less than the third argument. 
 22. most_greater_eq/less_eq: returns whether most of the values under the header column are greater/less or equal to the third argument.
Categories: Text to Code
Domains: Wikipedia; Logic -> Propositional Logic
Reasoning: Logical Reasoning

Positive examples:
input: select the row whose attendance record of all rows is 3rd maximum. the competition record of this row is danish superliga 2005-06.
output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; competition } ; danish superliga 2005 - 06 }
explanation: Here, the generated command using logical operators correctly selects third maximum from given table rows which is mentioned in natural language interpretation.; input: select the row whose duration record of all rows is maximum. the actor record of this row is lesley saweard.
output: eq { hop { argmax { all_rows ; duration } ; actor } ; lesley saweard }
explanation: Here, the generated command using logical operators correctly selects the maximum duration from the given table rows.; input: for the points records of all rows, most of them are equal to 0.
output: most_eq { all_rows ; points ; 0 }
explanation: Here, the generated command using logical operators correctly indicates the most of the point records that are zero.; input: the sum of the intake record of all rows is 505 .
output: round_eq { sum { all_rows ; intake } ; 505 }
explanation: In order to select all the rows, we should use all_rows ; intake, and for calculating the sum of them, we should use sum { all_rows ; intake } ;. Then we check if the sum is 505 using round_eq { sum { all_rows ; intake } ; 505 }.

Negative examples:
input: select the row whose total record of all rows is 3rd maximum . the club record of this row is maidstone united .
output: eq { hop { nth_argmax { all_rows ; total ; 3 } } ; maidstone united }
explanation: Given natural language interpretation and generated command are miss matched. We need to choose the club record after selecting the row whose total record of all rows is 3rd maximum.; input: select the row whose goal gain record of all rows is 3rd maximum. the team record of this row is india.
output: eq { hop { nth_argmax { all_rows ; goal gain ; 3 } ; team } ; south china }
explanation: In the natural language interpretation, it says that 3rd maximum should be selected from given table rows for team of India, but generated command selects for team of south china. It should be India.