Dataset Preview
Duplicate
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 1 new columns ({'ProdTaken'}) and 17 missing columns ({'PreferredPropertyStar', 'Occupation', 'NumberOfPersonVisiting', 'CityTier', 'MonthlyIncome', 'Designation', 'Unnamed: 0', 'NumberOfFollowups', 'PitchSatisfactionScore', 'DurationOfPitch', 'Age', 'TypeofContact', 'MaritalStatus', 'Gender', 'ProductPitched', 'Passport', 'NumberOfTrips'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Sudu1976/PredictTourPkg/ytest.csv (at revision 98bffbb46bad1064bba818b0da7c2252a285eef4)

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
              ProdTaken: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 376
              to
              {'Unnamed: 0': Value('int64'), 'Age': Value('float64'), 'TypeofContact': Value('int64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'Occupation': Value('int64'), 'Gender': Value('int64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'ProductPitched': Value('int64'), 'PreferredPropertyStar': Value('float64'), 'MaritalStatus': Value('int64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': 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 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 1 new columns ({'ProdTaken'}) and 17 missing columns ({'PreferredPropertyStar', 'Occupation', 'NumberOfPersonVisiting', 'CityTier', 'MonthlyIncome', 'Designation', 'Unnamed: 0', 'NumberOfFollowups', 'PitchSatisfactionScore', 'DurationOfPitch', 'Age', 'TypeofContact', 'MaritalStatus', 'Gender', 'ProductPitched', 'Passport', 'NumberOfTrips'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Sudu1976/PredictTourPkg/ytest.csv (at revision 98bffbb46bad1064bba818b0da7c2252a285eef4)
              
              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.

Unnamed: 0
int64
Age
float64
TypeofContact
int64
CityTier
int64
DurationOfPitch
float64
Occupation
int64
Gender
int64
NumberOfPersonVisiting
int64
NumberOfFollowups
float64
ProductPitched
int64
PreferredPropertyStar
float64
MaritalStatus
int64
NumberOfTrips
float64
Passport
int64
PitchSatisfactionScore
int64
Designation
string
MonthlyIncome
float64
1,214
44
1
1
8
2
1
3
1
3
3
1
2
1
4
Senior Manager
22,879
3,829
35
1
3
20
3
2
3
4
3
3
1
3
0
1
Senior Manager
27,306
2,622
47
1
3
7
3
1
4
4
3
5
1
3
0
2
Senior Manager
29,131
1,543
32
1
1
6
2
2
3
3
1
4
1
2
0
3
Manager
21,220
3,144
59
1
1
9
1
2
3
4
0
3
2
6
0
2
Executive
21,157
907
44
1
3
11
3
2
2
3
2
4
0
1
0
5
VP
33,213
1,426
32
1
1
35
2
1
2
4
0
4
2
2
0
3
Executive
17,837
4,269
27
1
3
7
2
2
3
4
1
3
1
3
0
5
Manager
23,974
261
38
0
3
8
2
2
2
4
1
3
0
4
0
5
Manager
20,249
4,223
32
1
1
12
1
2
3
4
0
3
1
2
1
4
Executive
23,499
243
40
1
1
30
1
2
3
3
1
3
1
2
0
3
Manager
18,319
3,533
38
1
1
20
3
2
3
4
1
3
1
3
0
1
Manager
22,963
228
35
0
3
6
3
0
3
3
3
3
3
2
0
5
Senior Manager
23,789
1,110
35
1
1
8
2
1
3
3
0
5
1
2
1
1
Executive
17,074
4,350
34
1
1
17
3
2
3
6
0
3
1
2
0
5
Executive
22,086
3,870
33
1
1
36
2
1
3
5
0
4
3
3
0
3
Executive
21,515
87
51
1
1
15
2
2
3
3
0
3
0
4
0
3
Executive
17,075
1,365
29
0
3
30
1
2
2
1
0
5
2
2
0
3
Executive
16,091
378
34
0
3
25
3
2
3
2
1
3
2
1
1
2
Manager
20,304
2,522
38
1
1
14
3
2
2
4
3
3
2
6
0
2
Senior Manager
32,342
209
46
1
1
6
3
2
3
3
3
5
1
1
0
2
Senior Manager
24,396
510
54
1
2
25
3
2
2
3
3
4
0
3
0
3
Senior Manager
25,725
2,022
56
1
1
15
3
2
2
3
4
3
1
1
0
4
AVP
26,103
385
30
0
1
10
1
2
2
3
0
3
2
19
1
4
Executive
17,285
1,386
26
1
1
6
3
2
3
3
0
5
2
1
0
5
Executive
17,867
2,060
33
1
1
13
3
2
2
3
3
3
1
1
0
4
Senior Manager
26,691
1,946
24
1
1
23
2
2
3
4
0
4
1
2
0
3
Executive
17,127
3,768
30
1
1
36
2
2
4
6
1
3
1
2
0
5
Manager
25,062
1,253
33
0
3
8
3
1
3
3
1
4
2
1
0
1
Manager
20,147
2,230
53
0
3
8
3
1
2
4
3
4
1
3
0
1
Senior Manager
22,525
3,514
29
0
3
14
2
2
3
4
1
5
3
2
0
3
Manager
23,576
1,372
39
1
1
15
3
2
2
3
1
5
1
2
0
4
Manager
20,151
4,366
46
1
3
9
2
2
4
4
1
4
1
2
0
5
Manager
23,483
2,466
35
1
1
14
2
1
3
4
3
4
2
2
0
3
Senior Manager
30,672
4,073
35
0
3
9
3
1
4
4
0
3
1
8
0
5
Executive
20,909
4,596
33
0
1
7
2
1
4
5
0
4
1
8
0
3
Executive
21,010
2,373
29
0
1
16
2
1
2
4
0
3
3
2
0
4
Executive
21,623
1,916
41
0
3
16
2
2
2
3
1
3
2
1
0
1
Manager
21,230
3,268
43
1
1
36
3
2
3
6
1
3
3
6
0
3
Manager
22,950
4,329
35
0
3
13
3
1
3
6
0
3
1
2
0
4
Executive
21,029
1,685
41
1
3
12
2
1
3
3
3
3
2
4
1
1
Senior Manager
28,591
694
33
1
1
6
2
1
2
4
1
3
3
1
0
4
Manager
21,949
837
40
0
1
15
3
0
2
3
3
3
3
1
0
4
Senior Manager
28,499
1,852
26
0
1
9
1
2
3
3
0
5
2
1
0
3
Executive
18,102
1,712
41
1
1
25
2
2
2
3
1
5
1
3
0
1
Manager
18,072
222
37
0
1
17
2
2
2
3
3
3
1
2
1
3
Senior Manager
27,185
2,145
31
1
3
13
2
2
2
4
0
3
1
4
0
4
Executive
17,329
4,867
45
1
3
8
2
2
3
6
1
4
2
8
0
3
Manager
21,040
514
33
0
1
9
2
2
3
3
0
5
2
2
1
5
Executive
18,348
2,795
33
1
1
9
3
1
4
4
0
4
0
3
0
4
Executive
21,048
1,074
33
1
1
14
2
2
3
3
1
3
3
3
1
3
Manager
21,388
402
30
1
3
18
1
1
2
3
1
3
3
1
0
2
Manager
21,577
547
42
0
1
25
3
2
2
2
0
3
1
7
1
3
Executive
17,759
1,899
46
1
1
8
2
2
2
3
4
3
1
7
0
5
AVP
32,861
4,656
51
1
1
16
2
2
4
4
0
3
1
6
0
5
Executive
21,058
1,880
30
1
1
8
2
1
2
5
1
3
2
3
0
1
Manager
21,091
2,742
37
0
1
25
2
2
3
3
0
3
0
6
0
5
Executive
22,366
1,323
28
0
2
6
2
2
2
3
0
3
1
2
0
4
Executive
17,706
1,357
42
1
1
12
3
2
2
3
3
5
1
1
0
3
Senior Manager
28,348
617
44
1
1
10
3
2
2
3
1
4
2
1
0
2
Manager
20,933
3,637
39
0
1
9
3
1
3
5
0
4
2
3
0
1
Executive
21,118
253
42
1
1
23
2
1
2
2
1
5
3
4
1
2
Manager
21,545
2,223
39
0
1
28
3
0
2
3
3
5
3
2
1
5
Senior Manager
25,880
944
28
0
1
6
2
1
2
5
1
3
0
1
0
3
Manager
21,674
2,079
43
1
1
20
2
2
3
3
4
5
1
7
0
5
AVP
32,159
3,372
45
1
1
22
3
1
4
4
3
3
0
3
0
3
Senior Manager
26,656
4,382
53
1
1
13
1
2
4
4
1
5
1
5
1
4
Manager
24,255
4,062
42
1
1
16
2
2
4
4
0
5
1
4
0
1
Executive
20,916
9
36
1
1
33
3
2
3
3
1
3
0
7
0
3
Manager
20,237
3,259
22
1
1
7
1
1
4
5
0
4
2
3
1
5
Executive
20,748
2,664
37
1
1
12
2
2
4
4
1
4
3
2
0
2
Manager
24,592
3,501
30
0
3
20
1
0
3
4
1
4
3
7
0
3
Manager
24,443
3,967
36
0
1
18
3
2
4
5
3
5
1
4
1
5
Senior Manager
28,562
186
40
1
1
10
3
1
2
3
2
3
0
2
0
5
VP
34,033
136
51
0
1
14
2
2
2
5
3
3
3
3
0
2
Senior Manager
25,650
3,835
39
1
3
7
2
2
3
5
0
5
3
6
0
3
Executive
21,536
390
43
1
1
18
2
2
2
4
4
4
1
2
0
3
AVP
29,336
40
35
1
1
10
2
2
3
3
0
3
1
2
0
4
Executive
16,951
2,695
40
0
1
9
1
1
4
4
3
3
2
2
0
2
Senior Manager
29,616
3,753
27
1
3
17
3
2
3
4
1
3
3
3
0
1
Manager
23,362
762
26
0
1
8
2
2
2
3
0
5
0
7
1
5
Executive
17,042
119
43
0
3
32
2
2
3
3
4
3
0
2
1
2
AVP
31,959
3,339
32
1
1
18
3
2
4
4
1
5
0
3
1
2
Manager
25,511
2,560
35
1
1
12
3
1
3
5
3
5
2
4
0
2
Senior Manager
30,309
4,135
34
1
1
11
3
1
3
5
0
4
1
8
0
4
Executive
21,300
1,016
31
1
1
14
2
1
2
4
0
4
2
2
0
4
Executive
16,261
4,748
35
1
3
16
2
1
4
4
1
3
1
3
0
1
Manager
24,392
4,865
42
0
3
16
2
2
3
6
4
3
1
2
0
5
AVP
24,829
2,030
34
1
1
14
2
1
2
3
1
5
1
4
0
5
Manager
20,121
2,680
34
1
1
9
2
1
3
4
0
5
0
2
0
3
Executive
21,385
22
34
1
1
13
2
0
2
3
3
4
3
1
0
3
Senior Manager
26,994
2,643
39
1
1
36
1
2
3
4
1
3
0
5
0
2
Manager
24,939
3,965
29
1
1
12
1
2
3
4
0
3
3
3
1
1
Executive
22,119
1,288
35
0
1
8
3
2
2
3
1
3
1
3
0
3
Manager
20,762
293
26
1
3
10
3
2
2
4
1
3
2
2
1
2
Manager
20,828
2,562
37
1
1
10
2
1
3
4
0
3
1
7
0
2
Executive
21,513
3,734
35
0
1
16
2
2
4
4
1
5
1
6
0
3
Manager
24,024
4,727
40
0
1
9
2
2
3
4
4
3
1
2
0
3
AVP
30,847
363
33
1
3
11
3
1
2
3
0
3
2
2
1
2
Executive
17,851
642
38
1
3
15
3
2
3
4
0
4
0
1
0
4
Executive
17,899
End of preview.

No dataset card yet

Downloads last month
8