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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'ProdTaken'}) and 14 missing columns ({'Gender', 'Passport', 'Designation', 'PreferredPropertyStar', 'MonthlyIncome', 'NumberOfPersonVisiting', 'Age', 'MaritalStatus', 'CityTier', 'TypeofContact', 'NumberOfTrips', 'OwnCar', 'Occupation', 'NumberOfChildrenVisiting'}).
This happened while the csv dataset builder was generating data using
hf://datasets/rakesh1715/Tourism-Package-Prediction/y_train.csv (at revision c42cdadd6d35d832b1cfb016186ce29d6f542664), [/tmp/hf-datasets-cache/medium/datasets/43567840596184-config-parquet-and-info-rakesh1715-Tourism-Packag-dd4b4896/hub/datasets--rakesh1715--Tourism-Package-Prediction/snapshots/c42cdadd6d35d832b1cfb016186ce29d6f542664/X_train.csv (origin=hf://datasets/rakesh1715/Tourism-Package-Prediction@c42cdadd6d35d832b1cfb016186ce29d6f542664/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/43567840596184-config-parquet-and-info-rakesh1715-Tourism-Packag-dd4b4896/hub/datasets--rakesh1715--Tourism-Package-Prediction/snapshots/c42cdadd6d35d832b1cfb016186ce29d6f542664/y_train.csv (origin=hf://datasets/rakesh1715/Tourism-Package-Prediction@c42cdadd6d35d832b1cfb016186ce29d6f542664/y_train.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
ProdTaken: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 377
to
{'Age': Value('float64'), 'TypeofContact': Value('string'), 'CityTier': Value('int64'), 'Occupation': Value('string'), 'Gender': Value('string'), 'NumberOfPersonVisiting': Value('int64'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('string'), 'NumberOfTrips': Value('int64'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('int64'), 'Designation': Value('string'), 'MonthlyIncome': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'ProdTaken'}) and 14 missing columns ({'Gender', 'Passport', 'Designation', 'PreferredPropertyStar', 'MonthlyIncome', 'NumberOfPersonVisiting', 'Age', 'MaritalStatus', 'CityTier', 'TypeofContact', 'NumberOfTrips', 'OwnCar', 'Occupation', 'NumberOfChildrenVisiting'}).
This happened while the csv dataset builder was generating data using
hf://datasets/rakesh1715/Tourism-Package-Prediction/y_train.csv (at revision c42cdadd6d35d832b1cfb016186ce29d6f542664), [/tmp/hf-datasets-cache/medium/datasets/43567840596184-config-parquet-and-info-rakesh1715-Tourism-Packag-dd4b4896/hub/datasets--rakesh1715--Tourism-Package-Prediction/snapshots/c42cdadd6d35d832b1cfb016186ce29d6f542664/X_train.csv (origin=hf://datasets/rakesh1715/Tourism-Package-Prediction@c42cdadd6d35d832b1cfb016186ce29d6f542664/X_train.csv), /tmp/hf-datasets-cache/medium/datasets/43567840596184-config-parquet-and-info-rakesh1715-Tourism-Packag-dd4b4896/hub/datasets--rakesh1715--Tourism-Package-Prediction/snapshots/c42cdadd6d35d832b1cfb016186ce29d6f542664/y_train.csv (origin=hf://datasets/rakesh1715/Tourism-Package-Prediction@c42cdadd6d35d832b1cfb016186ce29d6f542664/y_train.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Age
float64 | TypeofContact
string | CityTier
int64 | Occupation
string | Gender
string | NumberOfPersonVisiting
int64 | PreferredPropertyStar
float64 | MaritalStatus
string | NumberOfTrips
int64 | Passport
int64 | OwnCar
int64 | NumberOfChildrenVisiting
int64 | Designation
string | MonthlyIncome
float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34
|
Self Enquiry
| 1
|
Small Business
|
Male
| 4
| 4
|
Married
| 3
| 0
| 1
| 2
|
Executive
| 20,706
|
34
|
Self Enquiry
| 1
|
Small Business
|
Male
| 3
| 3
|
Married
| 1
| 0
| 0
| 0
|
Executive
| 17,661
|
40
|
Self Enquiry
| 3
|
Salaried
|
Male
| 3
| 3
|
Single
| 2
| 0
| 1
| 2
|
Senior Manager
| 26,558
|
39
|
Self Enquiry
| 3
|
Salaried
|
Male
| 2
| 3
|
Divorced
| 4
| 1
| 0
| 1
|
Manager
| 21,120
|
39
|
Self Enquiry
| 1
|
Small Business
|
Male
| 4
| 5
|
Divorced
| 2
| 1
| 0
| 2
|
Manager
| 25,351
|
36
|
Company Invited
| 3
|
Small Business
|
Male
| 2
| 3
|
Divorced
| 5
| 0
| 0
| 1
|
Senior Manager
| 24,699
|
36
|
Self Enquiry
| 2
|
Salaried
|
Male
| 3
| 5
|
Divorced
| 1
| 0
| 1
| 2
|
Executive
| 17,342
|
32
|
Self Enquiry
| 1
|
Large Business
|
Male
| 2
| 5
|
Single
| 5
| 0
| 0
| 1
|
Manager
| 17,176
|
36
|
Self Enquiry
| 1
|
Small Business
|
Female
| 2
| 5
|
Married
| 5
| 1
| 0
| 1
|
Executive
| 22,692
|
52
|
Company Invited
| 3
|
Small Business
|
Male
| 3
| 4
|
Divorced
| 4
| 0
| 0
| 1
|
Senior Manager
| 29,274
|
32
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 3
|
Married
| 5
| 1
| 0
| 1
|
Executive
| 21,034
|
34
|
Company Invited
| 3
|
Salaried
|
Female
| 2
| 4
|
Married
| 2
| 0
| 0
| 1
|
Manager
| 22,980
|
45
|
Company Invited
| 3
|
Small Business
|
Female
| 4
| 3
|
Divorced
| 2
| 0
| 1
| 2
|
Manager
| 23,446
|
37
|
Company Invited
| 1
|
Salaried
|
Male
| 3
| 3
|
Married
| 6
| 0
| 1
| 2
|
Manager
| 20,163
|
30
|
Self Enquiry
| 1
|
Salaried
|
Female
| 4
| 3
|
Married
| 3
| 0
| 1
| 3
|
Executive
| 22,438
|
42
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 3
|
Divorced
| 3
| 1
| 0
| 0
|
Manager
| 20,231
|
45
|
Self Enquiry
| 2
|
Small Business
|
Male
| 2
| 4
|
Single
| 2
| 0
| 0
| 0
|
Executive
| 17,177
|
49
|
Self Enquiry
| 1
|
Salaried
|
Male
| 4
| 3
|
Single
| 2
| 0
| 1
| 2
|
Senior Manager
| 29,677
|
24
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 3
|
Married
| 3
| 0
| 0
| 2
|
Executive
| 21,582
|
18
|
Self Enquiry
| 1
|
Salaried
|
Male
| 2
| 3
|
Single
| 2
| 0
| 0
| 0
|
Executive
| 16,420
|
45
|
Company Invited
| 1
|
Small Business
|
Male
| 4
| 4
|
Divorced
| 6
| 0
| 1
| 1
|
Manager
| 20,720
|
37
|
Company Invited
| 1
|
Salaried
|
Female
| 3
| 4
|
Divorced
| 2
| 0
| 0
| 1
|
Manager
| 24,352
|
35
|
Self Enquiry
| 1
|
Salaried
|
Female
| 4
| 4
|
Married
| 3
| 0
| 1
| 3
|
Manager
| 24,111
|
56
|
Self Enquiry
| 1
|
Large Business
|
Male
| 3
| 3
|
Married
| 1
| 0
| 1
| 2
|
Executive
| 17,339
|
28
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 4
|
Divorced
| 1
| 0
| 0
| 2
|
Executive
| 18,201
|
39
|
Self Enquiry
| 1
|
Large Business
|
Female
| 3
| 3
|
Single
| 5
| 1
| 1
| 1
|
Manager
| 22,995
|
30
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 5
|
Single
| 5
| 0
| 0
| 1
|
Executive
| 20,740
|
41
|
Self Enquiry
| 3
|
Salaried
|
Female
| 3
| 5
|
Married
| 1
| 0
| 1
| 2
|
AVP
| 31,595
|
29
|
Company Invited
| 3
|
Small Business
|
Female
| 2
| 3
|
Married
| 2
| 0
| 1
| 1
|
Senior Manager
| 22,918
|
28
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 4
|
Married
| 1
| 0
| 0
| 2
|
Executive
| 18,201
|
34
|
Self Enquiry
| 1
|
Salaried
|
Male
| 2
| 3
|
Divorced
| 2
| 0
| 1
| 0
|
Executive
| 17,691
|
61
|
Company Invited
| 3
|
Small Business
|
Female
| 4
| 5
|
Married
| 6
| 0
| 1
| 1
|
Senior Manager
| 28,944
|
26
|
Self Enquiry
| 3
|
Large Business
|
Male
| 2
| 5
|
Single
| 7
| 0
| 0
| 0
|
Manager
| 20,326
|
20
|
Self Enquiry
| 1
|
Large Business
|
Male
| 2
| 5
|
Single
| 2
| 0
| 1
| 0
|
Executive
| 17,973
|
34
|
Company Invited
| 1
|
Salaried
|
Female
| 4
| 4
|
Married
| 8
| 0
| 1
| 2
|
Senior Manager
| 30,556
|
37
|
Self Enquiry
| 1
|
Small Business
|
Male
| 4
| 3
|
Married
| 2
| 1
| 1
| 3
|
Executive
| 22,066
|
25
|
Self Enquiry
| 1
|
Small Business
|
Female
| 3
| 3
|
Married
| 3
| 0
| 0
| 1
|
Manager
| 23,055
|
54
|
Self Enquiry
| 3
|
Salaried
|
Female
| 4
| 3
|
Divorced
| 4
| 0
| 1
| 1
|
AVP
| 34,105
|
28
|
Self Enquiry
| 1
|
Large Business
|
Male
| 2
| 5
|
Single
| 3
| 0
| 0
| 0
|
Executive
| 17,080
|
29
|
Company Invited
| 1
|
Salaried
|
Male
| 3
| 3
|
Single
| 1
| 1
| 0
| 1
|
Manager
| 21,294
|
43
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 5
|
Single
| 5
| 1
| 1
| 2
|
Manager
| 25,223
|
32
|
Self Enquiry
| 1
|
Small Business
|
Female
| 3
| 3
|
Single
| 1
| 0
| 0
| 0
|
Manager
| 20,055
|
35
|
Company Invited
| 1
|
Small Business
|
Male
| 4
| 5
|
Single
| 2
| 0
| 0
| 1
|
Manager
| 24,021
|
36
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 4
|
Single
| 1
| 0
| 0
| 2
|
Manager
| 20,914
|
59
|
Self Enquiry
| 1
|
Small Business
|
Female
| 4
| 4
|
Married
| 6
| 1
| 1
| 2
|
Executive
| 22,024
|
28
|
Self Enquiry
| 1
|
Salaried
|
Female
| 4
| 3
|
Single
| 3
| 0
| 1
| 2
|
Executive
| 20,996
|
40
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 5
|
Married
| 2
| 0
| 1
| 2
|
Manager
| 23,396
|
44
|
Self Enquiry
| 1
|
Small Business
|
Female
| 2
| 3
|
Divorced
| 4
| 0
| 1
| 0
|
Senior Manager
| 25,248
|
36
|
Self Enquiry
| 1
|
Salaried
|
Male
| 2
| 3
|
Divorced
| 1
| 0
| 1
| 1
|
Executive
| 18,210
|
21
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Single
| 3
| 0
| 1
| 2
|
Executive
| 21,356
|
39
|
Self Enquiry
| 3
|
Salaried
|
Female
| 3
| 5
|
Married
| 5
| 0
| 0
| 2
|
Manager
| 25,571
|
33
|
Company Invited
| 1
|
Large Business
|
Male
| 4
| 4
|
Single
| 4
| 0
| 1
| 2
|
Executive
| 21,396
|
37
|
Self Enquiry
| 1
|
Large Business
|
Female
| 2
| 3
|
Single
| 5
| 0
| 1
| 0
|
Senior Manager
| 22,491
|
31
|
Company Invited
| 1
|
Small Business
|
Male
| 4
| 5
|
Married
| 3
| 0
| 0
| 2
|
Manager
| 21,750
|
33
|
Company Invited
| 1
|
Salaried
|
Male
| 2
| 3
|
Married
| 2
| 0
| 1
| 1
|
Manager
| 20,968
|
31
|
Company Invited
| 1
|
Large Business
|
Male
| 3
| 3
|
Single
| 20
| 1
| 1
| 2
|
Executive
| 20,963
|
25
|
Self Enquiry
| 1
|
Salaried
|
Female
| 4
| 4
|
Divorced
| 3
| 0
| 0
| 2
|
Executive
| 20,888
|
29
|
Self Enquiry
| 3
|
Small Business
|
Female
| 2
| 3
|
Divorced
| 2
| 0
| 1
| 1
|
Senior Manager
| 22,639
|
37
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 3
|
Married
| 5
| 0
| 0
| 1
|
Executive
| 21,716
|
29
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 3
|
Divorced
| 1
| 1
| 1
| 0
|
Executive
| 17,168
|
58
|
Self Enquiry
| 1
|
Large Business
|
Female
| 3
| 3
|
Married
| 1
| 0
| 0
| 0
|
Senior Manager
| 17,372
|
45
|
Self Enquiry
| 1
|
Small Business
|
Male
| 3
| 4
|
Single
| 2
| 0
| 1
| 2
|
Manager
| 24,611
|
41
|
Company Invited
| 1
|
Small Business
|
Female
| 3
| 4
|
Married
| 3
| 0
| 0
| 1
|
Manager
| 22,922
|
32
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 5
|
Married
| 4
| 0
| 0
| 2
|
Manager
| 22,911
|
41
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Married
| 4
| 1
| 1
| 1
|
Manager
| 26,135
|
52
|
Self Enquiry
| 3
|
Small Business
|
Male
| 4
| 3
|
Single
| 2
| 1
| 0
| 3
|
Manager
| 24,119
|
42
|
Company Invited
| 1
|
Small Business
|
Female
| 2
| 5
|
Married
| 4
| 1
| 0
| 0
|
Senior Manager
| 28,191
|
61
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 5
|
Married
| 7
| 0
| 1
| 1
|
VP
| 38,244
|
41
|
Self Enquiry
| 1
|
Small Business
|
Female
| 3
| 3
|
Single
| 2
| 1
| 1
| 2
|
Executive
| 21,020
|
34
|
Company Invited
| 3
|
Small Business
|
Male
| 3
| 5
|
Divorced
| 2
| 0
| 0
| 1
|
Manager
| 23,051
|
35
|
Self Enquiry
| 1
|
Small Business
|
Female
| 2
| 5
|
Single
| 2
| 0
| 1
| 1
|
Executive
| 17,559
|
35
|
Self Enquiry
| 3
|
Salaried
|
Male
| 4
| 3
|
Married
| 4
| 1
| 0
| 3
|
Senior Manager
| 28,391
|
28
|
Company Invited
| 3
|
Small Business
|
Female
| 4
| 3
|
Married
| 3
| 1
| 1
| 1
|
Manager
| 23,325
|
34
|
Self Enquiry
| 1
|
Small Business
|
Male
| 3
| 4
|
Single
| 6
| 1
| 0
| 1
|
Executive
| 20,991
|
32
|
Self Enquiry
| 1
|
Salaried
|
Male
| 4
| 5
|
Married
| 3
| 0
| 1
| 2
|
Executive
| 20,896
|
26
|
Self Enquiry
| 1
|
Salaried
|
Female
| 2
| 3
|
Married
| 2
| 0
| 1
| 1
|
Executive
| 17,886
|
31
|
Company Invited
| 3
|
Salaried
|
Male
| 2
| 5
|
Single
| 1
| 0
| 0
| 0
|
Manager
| 20,332
|
36
|
Self Enquiry
| 3
|
Small Business
|
Male
| 4
| 4
|
Divorced
| 3
| 0
| 1
| 3
|
Senior Manager
| 25,973
|
34
|
Company Invited
| 1
|
Large Business
|
Male
| 2
| 5
|
Married
| 2
| 1
| 1
| 0
|
Executive
| 17,307
|
59
|
Self Enquiry
| 3
|
Large Business
|
Male
| 3
| 3
|
Divorced
| 4
| 1
| 0
| 1
|
Senior Manager
| 26,904
|
56
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Married
| 3
| 0
| 1
| 2
|
VP
| 38,264
|
33
|
Company Invited
| 1
|
Salaried
|
Female
| 3
| 3
|
Single
| 5
| 1
| 0
| 2
|
Executive
| 21,110
|
36
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 3
|
Single
| 5
| 0
| 1
| 2
|
Executive
| 21,184
|
53
|
Self Enquiry
| 1
|
Small Business
|
Male
| 3
| 4
|
Married
| 4
| 0
| 1
| 1
|
Manager
| 23,619
|
40
|
Company Invited
| 1
|
Small Business
|
Male
| 2
| 3
|
Single
| 7
| 0
| 1
| 1
|
Manager
| 20,094
|
36
|
Self Enquiry
| 3
|
Large Business
|
Male
| 3
| 5
|
Single
| 2
| 1
| 1
| 2
|
Senior Manager
| 28,260
|
35
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 4
|
Married
| 3
| 0
| 1
| 2
|
Executive
| 20,686
|
33
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Single
| 3
| 0
| 1
| 2
|
Manager
| 24,074
|
36
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Married
| 8
| 1
| 1
| 2
|
Manager
| 24,118
|
44
|
Company Invited
| 1
|
Small Business
|
Male
| 3
| 4
|
Married
| 5
| 0
| 0
| 1
|
Manager
| 17,042
|
25
|
Self Enquiry
| 1
|
Large Business
|
Male
| 3
| 5
|
Married
| 2
| 0
| 0
| 1
|
Executive
| 20,974
|
44
|
Self Enquiry
| 3
|
Salaried
|
Male
| 2
| 4
|
Single
| 7
| 0
| 0
| 1
|
Manager
| 17,362
|
45
|
Self Enquiry
| 1
|
Salaried
|
Male
| 4
| 3
|
Single
| 3
| 1
| 1
| 1
|
Executive
| 21,614
|
29
|
Company Invited
| 1
|
Salaried
|
Male
| 3
| 3
|
Married
| 2
| 0
| 1
| 0
|
Executive
| 17,720
|
54
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Married
| 4
| 1
| 0
| 2
|
Manager
| 20,984
|
56
|
Self Enquiry
| 3
|
Small Business
|
Male
| 3
| 3
|
Single
| 8
| 1
| 1
| 2
|
AVP
| 32,373
|
36
|
Self Enquiry
| 1
|
Salaried
|
Male
| 2
| 3
|
Married
| 2
| 0
| 1
| 0
|
Executive
| 17,741
|
58
|
Self Enquiry
| 3
|
Salaried
|
Female
| 3
| 3
|
Married
| 5
| 1
| 1
| 0
|
AVP
| 32,875
|
24
|
Self Enquiry
| 1
|
Salaried
|
Female
| 3
| 4
|
Divorced
| 2
| 0
| 0
| 1
|
Executive
| 17,210
|
45
|
Self Enquiry
| 1
|
Salaried
|
Male
| 3
| 4
|
Single
| 3
| 0
| 1
| 2
|
Executive
| 22,098
|
End of preview.