The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 10 new columns ({'NumberOfTrips', 'PitchSatisfactionScore', 'ProdTaken', 'Unnamed: 0', 'MaritalStatus', 'CityTier', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'CustomerID', 'ProductPitched'})
This happened while the csv dataset builder was generating data using
hf://datasets/anithajk/Customer_Wellness_Tourism_Package_Prediction/tourism.csv (at revision 6da4e51ee2b0d049252c325e6bac945c3a5f981c)
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 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, 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
Unnamed: 0: int64
CustomerID: int64
ProdTaken: int64
Age: double
TypeofContact: string
CityTier: int64
DurationOfPitch: double
Occupation: string
Gender: string
NumberOfPersonVisiting: int64
NumberOfFollowups: double
ProductPitched: string
PreferredPropertyStar: double
MaritalStatus: string
NumberOfTrips: double
Passport: int64
PitchSatisfactionScore: int64
OwnCar: int64
NumberOfChildrenVisiting: double
Designation: string
MonthlyIncome: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2881
to
{'Age': Value('float64'), 'DurationOfPitch': Value('float64'), 'NumberOfFollowups': Value('float64'), 'MonthlyIncome': Value('float64'), 'NumberOfChildrenVisiting': Value('float64'), 'Passport': Value('int64'), 'OwnCar': Value('int64'), 'TypeofContact': Value('string'), 'Occupation': Value('string'), 'Gender': Value('string'), 'Designation': Value('string')}
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 1339, 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 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, 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 1833, 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 10 new columns ({'NumberOfTrips', 'PitchSatisfactionScore', 'ProdTaken', 'Unnamed: 0', 'MaritalStatus', 'CityTier', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'CustomerID', 'ProductPitched'})
This happened while the csv dataset builder was generating data using
hf://datasets/anithajk/Customer_Wellness_Tourism_Package_Prediction/tourism.csv (at revision 6da4e51ee2b0d049252c325e6bac945c3a5f981c)
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 | DurationOfPitch
float64 | NumberOfFollowups
float64 | MonthlyIncome
float64 | NumberOfChildrenVisiting
float64 | Passport
int64 | OwnCar
int64 | TypeofContact
string | Occupation
string | Gender
string | Designation
string |
|---|---|---|---|---|---|---|---|---|---|---|
44
| 8
| 1
| 22,879
| 0
| 1
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Senior Manager
|
35
| 20
| 4
| 27,306
| 2
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Senior Manager
|
47
| 7
| 4
| 29,131
| 2
| 0
| 1
|
Self Enquiry
|
Small Business
|
Female
|
Senior Manager
|
32
| 6
| 3
| 21,220
| 0
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
59
| 9
| 4
| 21,157
| 2
| 0
| 1
|
Self Enquiry
|
Large Business
|
Male
|
Executive
|
44
| 11
| 3
| 33,213
| 1
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
VP
|
32
| 35
| 4
| 17,837
| 0
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Executive
|
27
| 7
| 4
| 23,974
| 2
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
38
| 8
| 4
| 20,249
| 1
| 0
| 1
|
Company Invited
|
Salaried
|
Male
|
Manager
|
32
| 12
| 4
| 23,499
| 1
| 1
| 1
|
Self Enquiry
|
Large Business
|
Male
|
Executive
|
40
| 30
| 3
| 18,319
| 1
| 0
| 1
|
Self Enquiry
|
Large Business
|
Male
|
Manager
|
38
| 20
| 4
| 22,963
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
35
| 6
| 3
| 23,789
| 0
| 0
| 1
|
Company Invited
|
Small Business
|
Fe Male
|
Senior Manager
|
35
| 8
| 3
| 17,074
| 1
| 1
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Executive
|
34
| 17
| 6
| 22,086
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Executive
|
33
| 36
| 5
| 21,515
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Executive
|
51
| 15
| 3
| 17,075
| 0
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
29
| 30
| 1
| 16,091
| 1
| 0
| 1
|
Company Invited
|
Large Business
|
Male
|
Executive
|
34
| 25
| 2
| 20,304
| 2
| 1
| 1
|
Company Invited
|
Small Business
|
Male
|
Manager
|
38
| 14
| 4
| 32,342
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Senior Manager
|
46
| 6
| 3
| 24,396
| 0
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Senior Manager
|
54
| 25
| 3
| 25,725
| 0
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Senior Manager
|
56
| 15
| 3
| 26,103
| 0
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
AVP
|
30
| 10
| 3
| 17,285
| 1
| 1
| 1
|
Company Invited
|
Large Business
|
Male
|
Executive
|
26
| 6
| 3
| 17,867
| 2
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Executive
|
33
| 13
| 3
| 26,691
| 0
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Senior Manager
|
24
| 23
| 4
| 17,127
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
30
| 36
| 6
| 25,062
| 3
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
33
| 8
| 3
| 20,147
| 0
| 0
| 0
|
Company Invited
|
Small Business
|
Female
|
Manager
|
53
| 8
| 4
| 22,525
| 0
| 0
| 1
|
Company Invited
|
Small Business
|
Female
|
Senior Manager
|
29
| 14
| 4
| 23,576
| 2
| 0
| 1
|
Company Invited
|
Salaried
|
Male
|
Manager
|
39
| 15
| 3
| 20,151
| 0
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
46
| 9
| 4
| 23,483
| 3
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
35
| 14
| 4
| 30,672
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Senior Manager
|
35
| 9
| 4
| 20,909
| 1
| 0
| 0
|
Company Invited
|
Small Business
|
Female
|
Executive
|
33
| 7
| 5
| 21,010
| 3
| 0
| 0
|
Company Invited
|
Salaried
|
Female
|
Executive
|
29
| 16
| 4
| 21,623
| 0
| 0
| 1
|
Company Invited
|
Salaried
|
Female
|
Executive
|
41
| 16
| 3
| 21,230
| 1
| 0
| 0
|
Company Invited
|
Salaried
|
Male
|
Manager
|
43
| 36
| 6
| 22,950
| 1
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
35
| 13
| 6
| 21,029
| 2
| 0
| 0
|
Company Invited
|
Small Business
|
Female
|
Executive
|
41
| 12
| 3
| 28,591
| 0
| 1
| 0
|
Self Enquiry
|
Salaried
|
Female
|
Senior Manager
|
33
| 6
| 4
| 21,949
| 0
| 0
| 0
|
Self Enquiry
|
Salaried
|
Female
|
Manager
|
40
| 15
| 3
| 28,499
| 0
| 0
| 0
|
Company Invited
|
Small Business
|
Fe Male
|
Senior Manager
|
26
| 9
| 3
| 18,102
| 1
| 0
| 0
|
Company Invited
|
Large Business
|
Male
|
Executive
|
41
| 25
| 3
| 18,072
| 0
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
37
| 17
| 3
| 27,185
| 1
| 1
| 0
|
Company Invited
|
Salaried
|
Male
|
Senior Manager
|
31
| 13
| 4
| 17,329
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
45
| 8
| 6
| 21,040
| 2
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
33
| 9
| 3
| 18,348
| 2
| 1
| 1
|
Company Invited
|
Salaried
|
Male
|
Executive
|
33
| 9
| 4
| 21,048
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Female
|
Executive
|
33
| 14
| 3
| 21,388
| 2
| 1
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
30
| 18
| 3
| 21,577
| 0
| 0
| 1
|
Self Enquiry
|
Large Business
|
Female
|
Manager
|
42
| 25
| 2
| 17,759
| 1
| 1
| 1
|
Company Invited
|
Small Business
|
Male
|
Executive
|
46
| 8
| 3
| 32,861
| 0
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
AVP
|
51
| 16
| 4
| 21,058
| 3
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
30
| 8
| 5
| 21,091
| 0
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Manager
|
37
| 25
| 3
| 22,366
| 1
| 0
| 0
|
Company Invited
|
Salaried
|
Male
|
Executive
|
28
| 6
| 3
| 17,706
| 1
| 0
| 0
|
Company Invited
|
Salaried
|
Male
|
Executive
|
42
| 12
| 3
| 28,348
| 0
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Senior Manager
|
44
| 10
| 3
| 20,933
| 0
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
39
| 9
| 5
| 21,118
| 1
| 0
| 1
|
Company Invited
|
Small Business
|
Female
|
Executive
|
42
| 23
| 2
| 21,545
| 0
| 1
| 0
|
Self Enquiry
|
Salaried
|
Female
|
Manager
|
39
| 28
| 3
| 25,880
| 1
| 1
| 1
|
Company Invited
|
Small Business
|
Fe Male
|
Senior Manager
|
28
| 6
| 5
| 21,674
| 0
| 0
| 1
|
Company Invited
|
Salaried
|
Female
|
Manager
|
43
| 20
| 3
| 32,159
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Male
|
AVP
|
45
| 22
| 4
| 26,656
| 2
| 0
| 0
|
Self Enquiry
|
Small Business
|
Female
|
Senior Manager
|
53
| 13
| 4
| 24,255
| 2
| 1
| 1
|
Self Enquiry
|
Large Business
|
Male
|
Manager
|
42
| 16
| 4
| 20,916
| 1
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
36
| 33
| 3
| 20,237
| 0
| 0
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
22
| 7
| 5
| 20,748
| 3
| 1
| 0
|
Self Enquiry
|
Large Business
|
Female
|
Executive
|
37
| 12
| 4
| 24,592
| 3
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Manager
|
30
| 20
| 4
| 24,443
| 2
| 0
| 0
|
Company Invited
|
Large Business
|
Fe Male
|
Manager
|
36
| 18
| 5
| 28,562
| 3
| 1
| 1
|
Company Invited
|
Small Business
|
Male
|
Senior Manager
|
40
| 10
| 3
| 34,033
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Female
|
VP
|
51
| 14
| 5
| 25,650
| 1
| 0
| 0
|
Company Invited
|
Salaried
|
Male
|
Senior Manager
|
39
| 7
| 5
| 21,536
| 2
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
43
| 18
| 4
| 29,336
| 1
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
AVP
|
35
| 10
| 3
| 16,951
| 0
| 0
| 0
|
Self Enquiry
|
Salaried
|
Male
|
Executive
|
40
| 9
| 4
| 29,616
| 2
| 0
| 1
|
Company Invited
|
Large Business
|
Female
|
Senior Manager
|
27
| 17
| 4
| 23,362
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
26
| 8
| 3
| 17,042
| 0
| 1
| 1
|
Company Invited
|
Salaried
|
Male
|
Executive
|
43
| 32
| 3
| 31,959
| 0
| 1
| 0
|
Company Invited
|
Salaried
|
Male
|
AVP
|
32
| 18
| 4
| 25,511
| 3
| 1
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
35
| 12
| 5
| 30,309
| 1
| 0
| 0
|
Self Enquiry
|
Small Business
|
Female
|
Senior Manager
|
34
| 11
| 5
| 21,300
| 2
| 0
| 0
|
Self Enquiry
|
Small Business
|
Female
|
Executive
|
31
| 14
| 4
| 16,261
| 1
| 0
| 0
|
Self Enquiry
|
Salaried
|
Female
|
Executive
|
35
| 16
| 4
| 24,392
| 1
| 0
| 0
|
Self Enquiry
|
Salaried
|
Female
|
Manager
|
42
| 16
| 6
| 24,829
| 2
| 0
| 1
|
Company Invited
|
Salaried
|
Male
|
AVP
|
34
| 14
| 3
| 20,121
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Manager
|
34
| 9
| 4
| 21,385
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Executive
|
34
| 13
| 3
| 26,994
| 0
| 0
| 1
|
Self Enquiry
|
Salaried
|
Fe Male
|
Senior Manager
|
39
| 36
| 4
| 24,939
| 2
| 0
| 0
|
Self Enquiry
|
Large Business
|
Male
|
Manager
|
29
| 12
| 4
| 22,119
| 1
| 1
| 0
|
Self Enquiry
|
Large Business
|
Male
|
Executive
|
35
| 8
| 3
| 20,762
| 1
| 0
| 0
|
Company Invited
|
Small Business
|
Male
|
Manager
|
26
| 10
| 4
| 20,828
| 1
| 1
| 1
|
Self Enquiry
|
Small Business
|
Male
|
Manager
|
37
| 10
| 4
| 21,513
| 1
| 0
| 1
|
Self Enquiry
|
Salaried
|
Female
|
Executive
|
35
| 16
| 4
| 24,024
| 2
| 0
| 0
|
Company Invited
|
Salaried
|
Male
|
Manager
|
40
| 9
| 4
| 30,847
| 1
| 0
| 1
|
Company Invited
|
Salaried
|
Male
|
AVP
|
33
| 11
| 3
| 17,851
| 0
| 1
| 1
|
Self Enquiry
|
Small Business
|
Female
|
Executive
|
38
| 15
| 4
| 17,899
| 0
| 0
| 0
|
Self Enquiry
|
Small Business
|
Male
|
Executive
|
End of preview.