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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.52 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"8.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 56 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "8.0",
"row": 0
},
{
"col": "rank",
"new_value": "8.0",
"row": 41
},
{
"col": "rank",
"new_value": "6.0",
"row": 52
}
... | 3,977 | 4,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 6
},
{
"col": "rank",
"new_value": "#RANK",
"row": 16
}
]
]
} | 1,977 | 2,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "31 years old",
"row": 5
},
{
"col": "age",
"new_value": "43 years old",
"row": 13
},
{
"col": "age",
"new_value": "40 years old",
... | 4,040 | 4,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 9
},
{
"col": "rank",
"new_value": null,
"row": 16
},
{
"col": "rank",
"new_value": null,
"row": 20
},
... | 15,952 | 16,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "43 years old",
"row": 1
},
{
"col": "age",
"new_value": "31 years old",
"row": 5
},
{
"col": "rank",
"new_value": "RANK: ---5th Place",
... | 7,995 | 8,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.43 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 225 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "9.0",
"row": 16
},
{
"col": "rank",
"new_value": "18.0",
"row": 29
},
{
"col": "rank",
"new_value": "9.0",
"row": 35
},
... | 15,991 | 16,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 1,977 | 2,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.59 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "8.0",
"row": 0
},
{
"col": "rank",
"new_value": "9.0",
"row": 35
},
{
"col": "rank",
"new_value": "12.0",
"row": 65
}
... | 7,994 | 8,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.88 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[
16,
35
]
],
"overwrite_cells": [
[]
]
} | 15,952 | 16,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 4,040 | 4,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 5
},
{
"col": "rank",
"new_value": "#RANK",
"row": 17
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 20
... | 7,995 | 8,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 9
},
{
"col": "rank",
"new_value": "#RANK",
"row": 16
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 20
... | 15,952 | 16,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 15,952 | 16,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "30 years old",
"row": 0
},
{
"col": "age",
"new_value": "55 years old",
"row": 6
},
{
"col": "rank",
"new_value": "12-th Place",
... | 1,977 | 2,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 5
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 23
},
{
"col": "rank",
"new_value": "#RANK",
"row": 36
... | 4,040 | 4,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 7,995 | 8,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 29 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "4.0",
"row": 5
},
{
"col": "rank",
"new_value": "11.0",
"row": 16
}
]
]
} | 2,027 | 2,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.52 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 56 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 3,977 | 4,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.43 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 225 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 15,991 | 16,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.43 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 225 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#RANK",
"row": 16
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 25
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 26
... | 15,991 | 16,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.59 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#RANK",
"row": 0
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 22
},
{
"col": "rank",
"new_value": "#RANK",
"row": 35
... | 7,994 | 8,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.52 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"#RANK",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"20... | 56 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#RANK",
"row": 0
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 23
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 39
... | 3,977 | 4,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30 years old",
"26H 35M 25S",
"95725.0",
... | 29 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "30 years old",
"row": 0
},
{
"col": "age",
"new_value": "31 years old",
"row": 5
},
{
"col": "rank",
"new_value": "RANK: -4th Place",
... | 2,027 | 2,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "5.0",
"row": 6
},
{
"col": "rank",
"new_value": "11.0",
"row": 16
}
]
]
} | 1,977 | 2,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.59 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 7,994 | 8,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "4.0",
"row": 5
},
{
"col": "rank",
"new_value": "12.0",
"row": 36
}
]
]
} | 4,040 | 4,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.52 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"RANK: -1st Place",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0"... | 56 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "RANK: -1st Place",
"row": 0
},
{
"col": "rank",
"new_value": "6-th Place",
"row": 5
},
{
"col": "rank",
"new_value": "13-th Place",
... | 3,977 | 4,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.43 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 225 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 16
},
{
"col": "rank",
"new_value": null,
"row": 25
},
{
"col": "rank",
"new_value": null,
"row": 26
},
... | 15,991 | 16,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 29 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 5
},
{
"col": "rank",
"new_value": "#RANK",
"row": 16
}
]
]
} | 2,027 | 2,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "11.0",
"row": 16
},
{
"col": "rank",
"new_value": "9.0",
"row": 35
},
{
"col": "rank",
"new_value": "25.0",
"row": 78
},
... | 15,952 | 16,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.59 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "RANK: -1st Place",
"row": 0
},
{
"col": "rank",
"new_value": "8-th Place",
"row": 7
},
{
"col": "age",
"new_value": "43 years old",
... | 7,994 | 8,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.61 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 225 | 10 | {
"drop_rows": [
[
16,
35,
55,
56
]
],
"overwrite_cells": [
[]
]
} | 15,991 | 16,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "10.0",
"row": 17
},
{
"col": "rank",
"new_value": "8.0",
"row": 50
},
{
"col": "rank",
"new_value": "9.0",
"row": 62
}
... | 7,995 | 8,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 29 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 2,027 | 2,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.29 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[
36
]
],
"overwrite_cells": [
[]
]
} | 4,040 | 4,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.59 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 0
},
{
"col": "rank",
"new_value": null,
"row": 22
},
{
"col": "rank",
"new_value": null,
"row": 35
},
... | 7,994 | 8,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.52 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021-09... | 56 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 0
},
{
"col": "rank",
"new_value": null,
"row": 23
},
{
"col": "rank",
"new_value": null,
"row": 39
},
... | 3,977 | 4,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[
17,
50
]
],
"overwrite_cells": [
[]
]
} | 7,995 | 8,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 29 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 5
},
{
"col": "rank",
"new_value": null,
"row": 16
}
]
]
} | 2,027 | 2,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 39.56 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[
16
]
],
"overwrite_cells": [
[]
]
} | 1,977 | 2,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.43 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30 years old",
"26H 35M 25S",
"95725.0",
... | 225 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "30 years old",
"row": 0
},
{
"col": "rank",
"new_value": "RANK: --4th Place",
"row": 16
},
{
"col": "rank",
"new_value": "7-th Place",
... | 15,991 | 16,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 6
},
{
"col": "rank",
"new_value": null,
"row": 16
}
]
]
} | 1,977 | 2,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "29 years old",
"row": 9
},
{
"col": "rank",
"new_value": "RANK: -4th Place",
"row": 16
},
{
"col": "age",
"new_value": "57 years old",
... | 15,952 | 16,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 39.56 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 29 | 10 | {
"drop_rows": [
[
16
]
],
"overwrite_cells": [
[]
]
} | 2,027 | 2,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.63 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"84",
"26H 35M 25S",
"95725.0",
"2021... | 56 | 10 | {
"drop_rows": [
[
0,
52
]
],
"overwrite_cells": [
[]
]
} | 3,977 | 4,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 5
},
{
"col": "rank",
"new_value": null,
"row": 17
},
{
"col": "rank",
"new_value": null,
"row": 20
},
... | 7,995 | 8,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 5
},
{
"col": "rank",
"new_value": null,
"row": 23
},
{
"col": "rank",
"new_value": null,
"row": 36
}
]
... | 4,040 | 4,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.05 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[
0,
35
]
],
"overwrite_cells": [
[]
]
} | 7,994 | 8,000 | Introduced a obvious outliers in age column. Should be removed. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 120795 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 88 | 20 | {
"drop_rows": [
[
0,
10,
12,
22,
26,
67,
76
]
],
"overwrite_cells": [
[]
]
} | 8,025 | 8,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 131553 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 88 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "876 householdss in the area",
"row": 0
},
{
"col": ... | 8,025 | 8,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 194446 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 141 | 10 | {
"drop_rows": [
[
12,
19,
27,
32,
45,
51,
69,
82,
95,
105,
108,
114,
134,
137
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household com... | 7,990 | 8,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 22423 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 18 | 20 | {
"drop_rows": [
[
0,
15
]
],
"overwrite_cells": [
[]
]
} | 2,031 | 2,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 432141 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 293 | 10 | {
"drop_rows": [
[
9,
30,
33,
42,
45,
47,
70,
84,
86,
90,
118,
124,
125,
143,
147,
176,
178,
196,
227,
244,
245,
257,
260,
268,
271,
275,
281
]
],... | 15,991 | 16,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 52310 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 41 | 20 | {
"drop_rows": [
[
8,
13,
24,
25
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "#NUM!",
"row": 0
},
{
"co... | 3,994 | 4,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 101702 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 68 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "543 householdsss in the area",
"row": 4
},
{
"col":... | 3,995 | 4,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 45772 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 32 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 0
},
{
"col": "Bedrooms; number of [E][W... | 1,972 | 2,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 45772 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 32 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 1,972 | 2,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 94426 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 68 | 10 | {
"drop_rows": [
[
4,
9,
16,
28,
46
]
],
"overwrite_cells": [
[]
]
} | 3,995 | 4,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 42667 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 32 | 10 | {
"drop_rows": [
[
15,
29
]
],
"overwrite_cells": [
[]
]
} | 1,972 | 2,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 101702 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 68 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 3,995 | 4,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 101702 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 68 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 3
},
{
"col": "Bedrooms; number of [E][W... | 3,995 | 4,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 45772 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 32 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": " 250 households...in the area",
"row": 0
},
{
"col": "Bedrooms; numb... | 1,972 | 2,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 475113 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 293 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 9
},
{
"col": "Bedrooms; number of [E][W] : Total\\ Numbe... | 15,991 | 16,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 284612 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 183 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": " 291 households...in the area",
"row": 14
},
{
"col": "Bedrooms; num... | 15,964 | 16,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 23851 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 18 | 20 | {
"drop_rows": [
[
0
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "#NUM!",
"row": 14
},
{
"col": "Bedrooms; number of [E][... | 2,031 | 2,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 195790 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 141 | 10 | {
"drop_rows": [
[
31,
50,
51,
54,
68,
69,
82,
95,
103,
104,
108,
113,
137
]
],
"overwrite_cells": [
[]
]
} | 7,990 | 8,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 54213 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 41 | 20 | {
"drop_rows": [
[
13,
23,
24
]
],
"overwrite_cells": [
[]
]
} | 3,994 | 4,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 131553 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 88 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 8,025 | 8,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 131553 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 88 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 0
},
{
"col": "Bedrooms; number of [E][W] : 1 bedroom - H... | 8,025 | 8,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 475113 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 293 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "612 householdss in the area",
"row": 5
},
{
"col": ... | 15,991 | 16,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 284612 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 183 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 12
},
{
"col": "Bedrooms; number of [E][... | 15,964 | 16,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 25416 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "876 householdss in the area",
"row": 0
},
{
"col": ... | 2,031 | 2,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 59250 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 41 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 3,994 | 4,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 59250 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 41 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 13
},
{
"col": "Bedrooms; number of [E][W] : Total\\ Numb... | 3,994 | 4,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 23851 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 18 | 20 | {
"drop_rows": [
[
0
]
],
"overwrite_cells": [
[]
]
} | 2,031 | 2,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 119688 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 88 | 20 | {
"drop_rows": [
[
0,
22,
26,
55,
64,
76,
77,
85
]
],
"overwrite_cells": [
[]
]
} | 8,025 | 8,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 40978 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 32 | 10 | {
"drop_rows": [
[
15,
24,
29
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "#NUM!",
"row": 0
},
{
"col": "Bedr... | 1,972 | 2,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 93824 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 68 | 10 | {
"drop_rows": [
[
3,
4,
16,
33,
46,
51
]
],
"overwrite_cells": [
[]
]
} | 3,995 | 4,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 216574 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 141 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 7,990 | 8,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 216574 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 141 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 31
},
{
"col": "Bedrooms; number of [E][W] : Total\\ Numb... | 7,990 | 8,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 216574 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 141 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": " 80 households..in the area",
"row": 6
},
{
"col": "Bedrooms; number... | 7,990 | 8,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 198841 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 141 | 10 | {
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[
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27,
31,
51,
68,
82,
95,
108,
114,
134,
137
]
],
"overwrite_cells": [
[]
]
} | 7,990 | 8,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 42530 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 32 | 10 | {
"drop_rows": [
[
0,
29
]
],
"overwrite_cells": [
[]
]
} | 1,972 | 2,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 119216 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 88 | 20 | {
"drop_rows": [
[
0,
10,
12,
19,
22,
26,
67,
76
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value"... | 8,025 | 8,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 92855 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 68 | 10 | {
"drop_rows": [
[
4,
9,
16,
28,
41,
46
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": "#NUM!",
"row": 12
},... | 3,995 | 4,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 258654 | inconsistent-commonsense-logic | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 183 | 20 | {
"drop_rows": [
[
12,
15,
19,
24,
42,
46,
47,
68,
107,
111,
117,
120,
123,
127,
133,
147,
155
]
],
"overwrite_cells": [
[]
]
} | 15,964 | 16,000 | Introduced an inconsistency in the Total Number of Bedrooms column. Needs to be dropped because you cannot confidently tell which column is the wrong one. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 53678 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 41 | 20 | {
"drop_rows": [
[
8,
13,
24
]
],
"overwrite_cells": [
[]
]
} | 3,994 | 4,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 25416 | missingness | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"new_value": null,
"row": 0
},
{
"col": "Bedrooms; number of [E][W] : Total\\ Numbe... | 2,031 | 2,000 | Introduced missingness in Total Number of Bedrooms column. Can be recovered by using the formula Total Number of Bedrooms = 1 Bedroom + 2 Bedrooms + 3 Bedrooms + 4 or more Bedrooms. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 25416 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 2,031 | 2,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 59250 | inconsistent-formatting | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 41 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"new_value": " 250 households...in the area",
"row": 0
},
{
"col": "Bedrooms; numb... | 3,994 | 4,000 | Introduced formatting inconsistencies in ILI AGE 25-64 and ILI AGE 25-49 columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 475113 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 293 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 15,991 | 16,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 256800 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 183 | 20 | {
"drop_rows": [
[
15,
16,
19,
24,
35,
42,
45,
51,
68,
69,
97,
98,
111,
117,
147,
155,
161,
173
]
],
"overwrite_cells": [
[
{
"col": "Bedrooms; number of [E][W] : 2 bedrooms - Hous... | 15,964 | 16,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 262698 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 183 | 20 | {
"drop_rows": [
[
15,
19,
24,
42,
68,
69,
97,
98,
111,
117,
147,
155,
161,
173
]
],
"overwrite_cells": [
[]
]
} | 15,964 | 16,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 284612 | clean | clean | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [] | {
"headers": [
"Bedrooms; number of [E][W] : 4 or more bedrooms - Household composition : Other household types - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : One family only\\ Lone parent - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household ... | 183 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 15,964 | 16,000 | |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 439887 | outliers | naive-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household composition : Total\\ Household composition - Unit : Households"
] | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 293 | 10 | {
"drop_rows": [
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5,
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33,
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125,
143,
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227,
231,
238,
244,
260,
275,
279,
281
]
],
"overwrite_cells": [
[]
]
} | 15,991 | 16,000 | Introduced a large obvious outliers in Total Number of Bedrooms column and 2 bedrooms column should drop. |
england-wales-housing-bedroom-count | The dataset contains household composition in England and Wales in 2011. Household composition classifies households according to the relationships between the household members.
Using the dataset, calculate the estimated total number of bedrooms by multiplying the number of households in each bedroom category (1, 2, ... | 429740 | bad-values | connected-multi-column | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compos... | [
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 3 bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 2 bedrooms - Household compo... | {
"headers": [
"GEO_CODE",
"GEO_LABEL",
"GEO_TYPE",
"GEO_TYP2",
"Bedrooms; number of [E][W] : Total\\ Number of bedrooms - Household composition : Total\\ Household composition - Unit : Households",
"Bedrooms; number of [E][W] : 1 bedroom - Household composition : Total\\ Household composition... | 293 | 10 | {
"drop_rows": [
[
5,
9,
33,
45,
46,
60,
75,
77,
84,
90,
101,
109,
118,
124,
125,
143,
147,
177,
221,
227,
231,
238,
244,
260,
271,
274,
275,
279,... | 15,991 | 16,000 | Introduced bad values in Total Number of Bedrooms and 3 Bedrooms columns. Need to ignore these rows. Also introduced bad values in 2 bedrooms column. This can be recovered by looking at the other columns. |
nyc-green-taxis | How many trips in the dataset occurred during the top 5 pickup hours (hours of the day) with the highest average trip duration?
Trip duration is defined as the difference between drop-off and pickup timestamps. | 76 | missingness | single-column | [
"lpep_pickup_datetime",
"lpep_dropoff_datetime"
] | [
"lpep_dropoff_datetime"
] | {
"headers": [
"RideID",
"payment_type",
"lpep_pickup_datetime",
"lpep_dropoff_datetime",
"trip_distance",
"fare_amount",
"extra",
"mta_tax",
"tolls_amount",
"store_and_fwd_flag"
],
"rows": [
[
"239022",
"2.0",
"04/10/2021 11:11:55 AM",
"04/10/20... | 208 | 10 | {
"drop_rows": [
[
3,
35,
44,
59,
68,
151,
171,
180
]
],
"overwrite_cells": [
[]
]
} | 16,014 | 16,000 | Introduced missingness in lpep_dropoff_datetime column so the LLM needs to remove the rows with missing values |
nyc-green-taxis | How many trips in the dataset occurred during the top 5 pickup hours (hours of the day) with the highest average trip duration?
Trip duration is defined as the difference between drop-off and pickup timestamps. | 24 | missingness | single-column | [
"lpep_pickup_datetime",
"lpep_dropoff_datetime"
] | [
"lpep_dropoff_datetime"
] | {
"headers": [
"RideID",
"payment_type",
"lpep_pickup_datetime",
"lpep_dropoff_datetime",
"trip_distance",
"fare_amount",
"extra",
"mta_tax",
"tolls_amount",
"store_and_fwd_flag"
],
"rows": [
[
"239022",
"2.0",
"04/10/2021 11:11:55 AM",
"04/10/20... | 103 | 10 | {
"drop_rows": [
[
22,
58
]
],
"overwrite_cells": [
[]
]
} | 8,003 | 8,000 | Introduced missingness in lpep_dropoff_datetime column so the LLM needs to remove the rows with missing values |
nyc-green-taxis | How many trips in the dataset occurred during the top 5 pickup hours (hours of the day) with the highest average trip duration?
Trip duration is defined as the difference between drop-off and pickup timestamps. | 15 | inconsistent-formatting | single-column | [
"lpep_pickup_datetime",
"lpep_dropoff_datetime"
] | [
"lpep_dropoff_datetime"
] | {
"headers": [
"RideID",
"lpep_pickup_datetime",
"lpep_dropoff_datetime",
"trip_distance",
"fare_amount"
],
"rows": [
[
"239022",
"04/10/2021 11:11:55 AM",
"04/10/2021 11:18:14 AM",
"1.1",
"6.5"
],
[
"926728",
"11/27/2021 01:13:13 PM",
... | 67 | 5 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "lpep_dropoff_datetime",
"new_value": "21-10-07 09:18:AM -- ",
"row": 7
}
]
]
} | 3,999 | 4,000 | Introduced formatting inconsistencies in lpep_dropoff_datetime column |
nyc-green-taxis | How many trips in the dataset occurred during the top 5 pickup hours (hours of the day) with the highest average trip duration?
Trip duration is defined as the difference between drop-off and pickup timestamps. | 11 | outliers | connected-multi-column | [
"lpep_pickup_datetime",
"lpep_dropoff_datetime"
] | [
"lpep_dropoff_datetime",
"lpep_pickup_datetime"
] | {
"headers": [
"RideID",
"payment_type",
"lpep_pickup_datetime",
"lpep_dropoff_datetime",
"trip_distance",
"fare_amount",
"extra",
"mta_tax",
"tolls_amount",
"store_and_fwd_flag"
],
"rows": [
[
"239022",
"2.0",
"04/10/2021 11:11:55 AM",
"04/10/20... | 51 | 10 | {
"drop_rows": [
[
31
]
],
"overwrite_cells": [
[]
]
} | 4,032 | 4,000 | Introduced large outliers in lpep_dropoff_datetime column for absurdly long trip durations |
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