Datasets:
kenqgu
/
RADAR

Dataset card Data Studio Files
xet
 Community
1
task_id
stringclasses
53 values
query
stringclasses
53 values
answer
stringlengths
1
139
artifact_type
stringclasses
6 values
artifact_scope
stringclasses
4 values
query_cols
listlengths
1
5
artifact_reasoning_cols
listlengths
0
8
table
dict
num_rows
int64
10
1.13k
num_cols
int64
5
20
recovered_tables_transform_spec
dict
base_data_num_tokens
int64
1.94k
16.1k
base_data_token_bucket
int64
2k
16k
perturbation_note
stringclasses
257 values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
24497
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "762616", "INR", "The Complete SQL Bootcamp...
40
10
{ "drop_rows": [ [ 6, 34, 36 ] ], "overwrite_cells": [ [] ] }
3,988
4,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
22291
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "The Complete SQL Bootcamp 2020: Go from Zero to Hero",...
40
10
{ "drop_rows": [ [ 35, 36, 37, 38, 39 ] ], "overwrite_cells": [ [ { "col": "id", "new_value": "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "row": 0 }, { "col": "discount_price__currency", "new_value": "2...
3,988
4,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
11544
outliers
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "149684848" ], [ "937678", "...
106
5
{ "drop_rows": [ [ 0, 1, 30, 52, 53, 62, 93, 99 ] ], "overwrite_cells": [ [] ] }
7,995
8,000
Introduced large outliers in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
12535
inconsistent-formatting
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "published_time", "created" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "7.800600e+04___reviews" ], [ "937...
106
5
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "num_reviews", "new_value": "7.800600e+04___reviews", "row": 0 }, { "col": "num_reviews", "new_value": "5.458100e+04_____reviews", "row": 1 }, { "col": "published_ti...
7,995
8,000
Introduced formatting inconsistencies in num_reviews, published_time, and created columns
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
19168
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
50
20
{ "drop_rows": [ [ 1, 3, 13 ] ], "overwrite_cells": [ [] ] }
8,026
8,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
20689
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
50
20
{ "drop_rows": [ [ 4, 5, 19, 20, 45, 48 ] ], "overwrite_cells": [ [ { "col": "num_subscribers", "new_value": " ", "row": 3 }, { "col": "discount_price__amount", "new_value": " ", "row": 37 } ...
8,026
8,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
12993
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
102
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
16,057
16,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
27556
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
24
20
{ "drop_rows": [ [ 0 ] ], "overwrite_cells": [ [] ] }
3,947
4,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
6076
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006" ], [ "937678", "Tabl...
212
5
{ "drop_rows": [ [ 2, 57, 64, 85, 86, 128, 167 ] ], "overwrite_cells": [ [] ] }
16,001
16,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
20881
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
50
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
8,026
8,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
21031
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
50
20
{ "drop_rows": [ [ 4, 45, 48 ] ], "overwrite_cells": [ [] ] }
8,026
8,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
6473
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006" ], [ "937678", "Tabl...
212
5
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
16,001
16,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
11495
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "", "", "" ], [ "2016-08-23 16:59:49+00:00", "2016-08-22 12:10:18+00:00", "54581", ...
106
5
{ "drop_rows": [ [ 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105 ] ], "overwrite_cells": [ [ { "col": "id", "new_value": "2016-04-06 05:16:11+00:00", "row": 0 ...
7,995
8,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
29850
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
24
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
3,947
4,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
6823
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006" ], [ "937678", "Tabl...
212
5
{ "drop_rows": [ [ 25, 87, 100, 106, 120, 129, 144, 147, 173, 176, 177, 184 ] ], "overwrite_cells": [ [] ] }
16,001
16,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
29054
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
24
20
{ "drop_rows": [ [ 0, 14, 20, 23 ] ], "overwrite_cells": [ [ { "col": "discount_price__amount", "new_value": " ", "row": 11 }, { "col": "id", "new_value": "980086", "row": 17 }, { "col": "titl...
3,947
4,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
29547
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
24
20
{ "drop_rows": [ [ 1, 16, 18 ] ], "overwrite_cells": [ [] ] }
3,947
4,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
6234
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006", "", "" ], [ "2016-08-23 16:59:49+00:00", "2016-08-22 12:10:18+00:00", "54581", ...
212
5
{ "drop_rows": [ [ 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211 ] ], "overwrite_cells": [ [ { "col": "id", "new_value": "2016-04-06 05:16:1...
16,001
16,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
7867
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "762616", "INR", "The Complete SQL Bootcamp...
165
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
16,021
16,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
14159
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "The Complete SQL Bootcamp 2020: Go from Zero to Hero",...
82
10
{ "drop_rows": [ [ 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 ] ], "overwrite_cells": [ [ { "col": "id", "new_value": "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "row": 0 }, { ...
8,015
8,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
15149
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "762616", "INR", "The Complete SQL Bootcamp...
82
10
{ "drop_rows": [ [ 1, 16, 31, 36, 46, 62, 79 ] ], "overwrite_cells": [ [] ] }
8,015
8,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
15308
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "762616", "INR", "The Complete SQL Bootcamp...
82
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
8,015
8,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
24368
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "762616", "INR", "The Complete SQL Bootcamp...
40
10
{ "drop_rows": [ [ 17, 35 ] ], "overwrite_cells": [ [] ] }
3,988
4,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
29339
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "762616", "INR", "The Complete SQL Bootcamp...
20
10
{ "drop_rows": [ [ 0 ] ], "overwrite_cells": [ [] ] }
2,024
2,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
9279
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "discount_price__currency", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "avg_rating_recent", "price_detail__currency", "avg_rating" ], "rows": [ [ "The Complete SQL Bootcamp 2020: Go from Zero to Hero",...
165
10
{ "drop_rows": [ [ 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164 ] ], "overwrite_cells": [ [ { "col": "id", "new_value": "The Complete SQL Bootcamp 2020: Go from Zero to Hero", ...
16,021
16,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
18966
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006" ], [ "937678", "TEST...
52
5
{ "drop_rows": [ [ 1, 3, 22 ] ], "overwrite_cells": [ [] ] }
3,969
4,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
11790
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
102
20
{ "drop_rows": [ [ 0, 4, 11, 47, 62, 65 ] ], "overwrite_cells": [ [] ] }
16,057
16,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
36000
missingness
single-column
[ "num_reviews", "published_time" ]
[ "num_reviews" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
12
20
{ "drop_rows": [ [ 0, 5 ] ], "overwrite_cells": [ [ { "col": "num_subscribers", "new_value": " ", "row": 1 }, { "col": "discount_price__amount", "new_value": " ", "row": 6 } ] ] }
2,037
2,000
Introduced missingness in num_reviews column so the LLM needs to remove the rows with missing values
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
40440
bad-values
naive-multi-column
[ "num_reviews", "published_time" ]
[ "num_reviews", "title", "published_time", "created" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
12
20
{ "drop_rows": [ [ 1, 5, 9 ] ], "overwrite_cells": [ [] ] }
2,037
2,000
Introduced bad values in num_reviews column
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
12535
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006" ], [ "937678", "Tabl...
106
5
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
7,995
8,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
40009
clean
clean
[ "num_reviews", "published_time" ]
[]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews", "num_published_practice_tests", "discount_price__currency", "avg_rating_recent", "price_detail__currency", "avg_rating", "price_detail__amount", "url", "rating", "discount_price__price_str...
12
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
2,037
2,000
udemy-classes
Among the courses published in the last 3 years (where today is the latest date in the dataset), what is the average number of reviews per course? Return the result rounded to the nearest integer using bankers rounding (round half to even). Examples: round(2.5) → 2, round(3.5) → 4, round(4.3) → 4, round(4.7) → 5.
20651
inconsistent-commonsense-logic
connected-multi-column
[ "num_reviews", "published_time" ]
[ "created", "published_time" ]
{ "headers": [ "id", "title", "published_time", "created", "num_reviews" ], "rows": [ [ "762616", "The Complete SQL Bootcamp 2020: Go from Zero to Hero", "2016-04-06 05:16:11+00:00", "2016-02-14 22:57:48+00:00", "78006" ], [ "937678", "Tabl...
52
5
{ "drop_rows": [ [ 4, 45, 48 ] ], "overwrite_cells": [ [] ] }
3,969
4,000
Introduced inconsistent logic in published_time column and how it should be strictly after to created column
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
203
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "#REF!", "row": 120 }, { "col": "medal", "new_value": "#NAME?", "row": 140 }, { "col": "medal", "new_value": "#REF!", ...
15,964
16,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
203
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
15,964
16,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
130
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
367
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "5.0", "row": 119 }, { "col": "medal_type_numeric", "new_value": "5.0", "row": 220 }, { "col": "medal_type_numeric", "new_...
15,977
16,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
203
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
15,964
16,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
130
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
367
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
15,977
16,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
130
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
367
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal", "new_value": "#REF!", "row": 74 }, { "col": "medal_type_numeric", "new_value": "0.1", "row": 113 }, { "col": "medal", "new_value": "#NAME?", ...
15,977
16,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
130
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1", "Gymnastics", "Gymnasti...
367
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
15,977
16,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
203
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "5.0", "row": 113 }, { "col": "medal_type_numeric", "new_value": "5.0", "row": 120 }, { "col": "medal_type_numeric", "new_...
15,964
16,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
47
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": null, "row": 20 }, { "col": "medal", "new_value": null, "row": 23 } ] ] }
1,988
2,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
51
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
3,994
4,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
185
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "#REF!", "row": 120 }, { "col": "medal", "new_value": "#NAME?", "row": 140 }, { "col": "medal", "new_value": "#REF!", ...
7,992
8,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1", "Gymnastics", "Gymnasti...
185
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
7,992
8,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
47
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "4.0", "row": 23 } ] ] }
1,988
2,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
51
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "0.3", "row": 16 }, { "col": "medal", "new_value": "#REF!", "row": 19 }, { "col": "medal", "new_value": "#NAME?", ...
3,994
4,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
47
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
1,988
2,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
57
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
203
20
{ "drop_rows": [ [ 113, 120, 164 ] ], "overwrite_cells": [ [] ] }
15,964
16,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
22
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
47
10
{ "drop_rows": [ [ 23 ] ], "overwrite_cells": [ [] ] }
1,988
2,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
32
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
103
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": null, "row": 58 }, { "col": "medal", "new_value": null, "row": 59 } ] ] }
8,028
8,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
29
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
95
10
{ "drop_rows": [ [ 87 ] ], "overwrite_cells": [ [] ] }
3,998
4,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
31
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
95
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
3,998
4,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
25
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "0.3", "row": 16 }, { "col": "medal", "new_value": "#REF!", "row": 19 }, { "col": "medal", "new_value": "#NAME?", ...
1,967
2,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
31
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
95
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "4.0", "row": 87 } ] ] }
3,998
4,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
25
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
1,967
2,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
32
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
103
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "4.0", "row": 59 } ] ] }
8,028
8,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
203
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal", "new_value": null, "row": 113 }, { "col": "medal", "new_value": null, "row": 120 }, { "col": "medal_type_numeric", "new_value": null, "row":...
15,964
16,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
32
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
103
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
8,028
8,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
31
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
95
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": null, "row": 24 }, { "col": "medal", "new_value": null, "row": 87 } ] ] }
3,998
4,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
62
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
103
20
{ "drop_rows": [ [ 59 ] ], "overwrite_cells": [ [] ] }
8,028
8,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
22
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
51
20
{ "drop_rows": [ [ 23 ] ], "overwrite_cells": [ [] ] }
3,994
4,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
185
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal", "new_value": null, "row": 113 }, { "col": "medal", "new_value": null, "row": 120 }, { "col": "medal_type_numeric", "new_value": null, "row":...
7,992
8,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
47
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "0.3", "row": 16 }, { "col": "medal", "new_value": "#REF!", "row": 19 }, { "col": "medal", "new_value": "#NAME?", ...
1,988
2,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
51
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "4.0", "row": 23 } ] ] }
3,994
4,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
51
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
3,994
4,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
185
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
7,992
8,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
130
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
367
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": null, "row": 114 }, { "col": "medal", "new_value": null, "row": 119 }, { "col": "medal_type_numeric", "new_value": null, ...
15,977
16,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1", "Gymnastics", "Gymnasti...
47
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
1,988
2,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
60
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
185
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "5.0", "row": 113 }, { "col": "medal_type_numeric", "new_value": "5.0", "row": 120 }, { "col": "medal_type_numeric", "new_...
7,992
8,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
57
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
185
10
{ "drop_rows": [ [ 113, 120, 164 ] ], "overwrite_cells": [ [] ] }
7,992
8,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
51
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": null, "row": 20 }, { "col": "medal", "new_value": null, "row": 23 } ] ] }
3,994
4,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
missingness
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
25
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": null, "row": 20 }, { "col": "medal", "new_value": null, "row": 23 } ] ] }
1,967
2,000
Introduced missingness in medal and medal_type_numeric columns. You can recover the missing values by looking at the medal or medal_type_numeric column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
31
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1", "Gymnastics", "Gymnasti...
95
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
3,998
4,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
32
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
103
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "0.3", "row": 48 }, { "col": "medal", "new_value": "#NAME?", "row": 58 }, { "col": "medal_type_numeric", "new_value": "#RE...
8,028
8,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
116
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
367
10
{ "drop_rows": [ [ 119, 220, 246, 261, 266, 325 ] ], "overwrite_cells": [ [] ] }
15,977
16,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
clean
clean
[ "team", "medal_type_numeric", "medal" ]
[]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
25
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [] ] }
1,967
2,000
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
32
inconsistent-formatting
naive-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
103
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "1", "row": 0 }, { "col": "medal_type_numeric", "new_value": "3", "row": 1 }, { "col": "medal_type_numeric", "new_value": ...
8,028
8,000
Introduced formatting inconsistencies in medal and medal_type_numeric columns
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
25
outliers
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
25
20
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "4.0", "row": 23 } ] ] }
1,967
2,000
Introduced a obvious outliers in medal_type_numeric column. Can be recovered by looking at the medal column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
31
bad-values
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal", "medal_type_numeric" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc" ], "rows": [ [ "17", "Paavo Johannes Aaltonen", "M", "1948 Summer", "Bronze", "1.0", "Gymnastics", "Gymnas...
95
10
{ "drop_rows": [ [] ], "overwrite_cells": [ [ { "col": "medal_type_numeric", "new_value": "0.2", "row": 21 }, { "col": "medal", "new_value": "#NAME?", "row": 24 }, { "col": "medal", "new_value": "#REF!", ...
3,998
4,000
Introduced bad values in medal and medal_type_numeric columns. You can recover the missing values by looking at the other column.
olympics-medal-winners
In this dataset of medal winners, we can count the value of medals based on the medal type. A gold is 3 points, silver is 2 points, and bronze is 1 point. For the team with the highest number of points, how many points do they have? Assume the team column is correct (i.e., no logical inconsistencies). Return your answe...
22
inconsistent-commonsense-logic
connected-multi-column
[ "team", "medal_type_numeric", "medal" ]
[ "medal_type_numeric", "medal" ]
{ "headers": [ "athlete_id", "name", "sex", "games", "medal", "medal_type_numeric", "sport", "event", "team", "noc", "season", "is_medalist", "year", "height", "bmi", "city", "age_category", "season_numeric", "weight", "age" ], "rows"...
25
20
{ "drop_rows": [ [ 23 ] ], "overwrite_cells": [ [] ] }
1,967
2,000
Introduced an inconsistency in the medal_type_numeric column. Needs to be dropped because can't verify the consistency.