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 320 new columns ({'75', '117', '251', '253', '241', '161', '181', '8', '266', '37', '173', '74', '153', '197', '145', '285', '41', '90', '238', '166', '60', '170', '217', '66', '187', '52', '204', '198', '36', '284', '141', '263', '219', '130', '196', '139', '124', '221', '142', '168', '240', '84', '224', '305', '146', '178', '176', '157', '164', '138', '175', '185', '192', '243', '269', '125', '206', '230', '126', '5', '87', '148', '76', '119', '147', '265', '299', '225', '46', '177', '57', '48', '258', '296', '249', '237', '72', '22', '98', '104', '118', '61', '245', '317', '137', '42', '302', '88', '287', '191', '64', '58', '209', '81', '275', '31', '210', '277', '85', '303', '294', '97', '276', '79', '33', '80', '133', '201', '53', '116', '315', '101', '235', '21', '2', '307', '281', '110', '193', '151', '286', '256', '179', '318', '10', '131', '231', '194', '152', '95', '25', '7', '62', '297', '304', '300', '314', '54', '301', '278', '226', '29', '272', '20', '38', '229', '105', '223', '268', '290', '257', '202', '18', '184', '227', '50', '13', '6', '71', '122', '319', '89', '236', '228', '91', '93', '169', '78', '248', '298', '121', '160', '311', '109', '128', '316', '30', '291', '112', '140', '45', '288', '94', '199', '180', '1', '34', '280', '43', '115', '188', '51', '252', '27', '68', '134', '234', '44', '39', '183', '59', '113', '293', '0', '107', '155', '246', '4', '86', '261', '167', '111', '220', '189', '247', '289', '92', '149', '65', '102', '171', '132', '233', '207', '12', '262', '154', '23', '283', '244', '32', '232', '271', '195', '215', '165', '24', '15', '239', '150', '211', '82', '218', '16', '214', '186', '162', '279', '63', '174', '259', '264', '123', '292', '163', '55', '69', '203', '273', '56', '270', '11', '9', '255', '17', '96', '295', '156', '83', '222', '158', '172', '310', '282', '242', '312', '40', '106', '313', '100', '129', '212', '103', '73', '28', '205', '309', '306', '144', '99', '77', '70', '108', '3', '190', '114', '120', '26', '213', '182', '250', '208', '67', '200', '260', '136', '216', '14', '159', '254', '19', '35', '127', '267', '274', '143', '308', '135', '49', '47'}) and 6 missing columns ({'LUFL', 'LULL', 'HUFL', 'MULL', 'MUFL', 'HULL'}).

This happened while the csv dataset builder was generating data using

hf://datasets/pkr7098/time-series-forecasting-datasets/electricity.csv (at revision d00662175559122f084dffd9348550879f803840)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              date: string
              0: double
              1: double
              2: double
              3: double
              4: double
              5: double
              6: double
              7: double
              8: double
              9: double
              10: double
              11: double
              12: double
              13: double
              14: double
              15: double
              16: double
              17: double
              18: double
              19: double
              20: double
              21: double
              22: double
              23: double
              24: double
              25: double
              26: double
              27: double
              28: double
              29: double
              30: double
              31: double
              32: double
              33: double
              34: double
              35: double
              36: double
              37: double
              38: double
              39: double
              40: double
              41: double
              42: double
              43: double
              44: double
              45: double
              46: double
              47: double
              48: double
              49: double
              50: double
              51: double
              52: double
              53: double
              54: double
              55: double
              56: double
              57: double
              58: double
              59: double
              60: double
              61: double
              62: double
              63: double
              64: double
              65: double
              66: double
              67: double
              68: double
              69: double
              70: double
              71: double
              72: double
              73: double
              74: double
              75: double
              76: double
              77: double
              78: double
              79: double
              80: double
              81: double
              82: double
              83: double
              84: double
              85: double
              86: double
              87: double
              88: double
              89: double
              90: double
              91: double
              92: double
              93: double
              94: double
              95: double
              96: double
              97: double
              98: double
              99: double
              100: double
              101: double
              102: double
              103: double
              104: double
              105: double
              106: double
              107: double
              108: double
              109: double
              110: double
              111: double
              112: double
              113: double
              114: double
              115: double
              116: double
              117: double
              118: double
              119: double
              120: double
              121: double
              122: double
              123: double
              124: double
              125: double
              126: double
              127: double
              128: double
              129: double
              130: double
              131: double
              132: double
              1
              ...
              uble
              205: double
              206: double
              207: double
              208: double
              209: double
              210: double
              211: double
              212: double
              213: double
              214: double
              215: double
              216: double
              217: double
              218: double
              219: double
              220: double
              221: double
              222: double
              223: double
              224: double
              225: double
              226: double
              227: double
              228: double
              229: double
              230: double
              231: double
              232: double
              233: double
              234: double
              235: double
              236: double
              237: double
              238: double
              239: double
              240: double
              241: double
              242: double
              243: double
              244: double
              245: double
              246: double
              247: double
              248: double
              249: double
              250: double
              251: double
              252: double
              253: double
              254: double
              255: double
              256: double
              257: double
              258: double
              259: double
              260: double
              261: double
              262: double
              263: double
              264: double
              265: double
              266: double
              267: double
              268: double
              269: double
              270: double
              271: double
              272: double
              273: double
              274: double
              275: double
              276: double
              277: double
              278: double
              279: double
              280: double
              281: double
              282: double
              283: double
              284: double
              285: double
              286: double
              287: double
              288: double
              289: double
              290: double
              291: double
              292: double
              293: double
              294: double
              295: double
              296: double
              297: double
              298: double
              299: double
              300: double
              301: double
              302: double
              303: double
              304: double
              305: double
              306: double
              307: double
              308: double
              309: double
              310: double
              311: double
              312: double
              313: double
              314: double
              315: double
              316: double
              317: double
              318: double
              319: double
              OT: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 34496
              to
              {'date': Value(dtype='string', id=None), 'HUFL': Value(dtype='float64', id=None), 'HULL': Value(dtype='float64', id=None), 'MUFL': Value(dtype='float64', id=None), 'MULL': Value(dtype='float64', id=None), 'LUFL': Value(dtype='float64', id=None), 'LULL': Value(dtype='float64', id=None), 'OT': Value(dtype='float64', id=None)}
              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 1396, 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 1045, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, 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 320 new columns ({'75', '117', '251', '253', '241', '161', '181', '8', '266', '37', '173', '74', '153', '197', '145', '285', '41', '90', '238', '166', '60', '170', '217', '66', '187', '52', '204', '198', '36', '284', '141', '263', '219', '130', '196', '139', '124', '221', '142', '168', '240', '84', '224', '305', '146', '178', '176', '157', '164', '138', '175', '185', '192', '243', '269', '125', '206', '230', '126', '5', '87', '148', '76', '119', '147', '265', '299', '225', '46', '177', '57', '48', '258', '296', '249', '237', '72', '22', '98', '104', '118', '61', '245', '317', '137', '42', '302', '88', '287', '191', '64', '58', '209', '81', '275', '31', '210', '277', '85', '303', '294', '97', '276', '79', '33', '80', '133', '201', '53', '116', '315', '101', '235', '21', '2', '307', '281', '110', '193', '151', '286', '256', '179', '318', '10', '131', '231', '194', '152', '95', '25', '7', '62', '297', '304', '300', '314', '54', '301', '278', '226', '29', '272', '20', '38', '229', '105', '223', '268', '290', '257', '202', '18', '184', '227', '50', '13', '6', '71', '122', '319', '89', '236', '228', '91', '93', '169', '78', '248', '298', '121', '160', '311', '109', '128', '316', '30', '291', '112', '140', '45', '288', '94', '199', '180', '1', '34', '280', '43', '115', '188', '51', '252', '27', '68', '134', '234', '44', '39', '183', '59', '113', '293', '0', '107', '155', '246', '4', '86', '261', '167', '111', '220', '189', '247', '289', '92', '149', '65', '102', '171', '132', '233', '207', '12', '262', '154', '23', '283', '244', '32', '232', '271', '195', '215', '165', '24', '15', '239', '150', '211', '82', '218', '16', '214', '186', '162', '279', '63', '174', '259', '264', '123', '292', '163', '55', '69', '203', '273', '56', '270', '11', '9', '255', '17', '96', '295', '156', '83', '222', '158', '172', '310', '282', '242', '312', '40', '106', '313', '100', '129', '212', '103', '73', '28', '205', '309', '306', '144', '99', '77', '70', '108', '3', '190', '114', '120', '26', '213', '182', '250', '208', '67', '200', '260', '136', '216', '14', '159', '254', '19', '35', '127', '267', '274', '143', '308', '135', '49', '47'}) and 6 missing columns ({'LUFL', 'LULL', 'HUFL', 'MULL', 'MUFL', 'HULL'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/pkr7098/time-series-forecasting-datasets/electricity.csv (at revision d00662175559122f084dffd9348550879f803840)
              
              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.

date
string
HUFL
float64
HULL
float64
MUFL
float64
MULL
float64
LUFL
float64
LULL
float64
OT
float64
2016-07-01 00:00:00
5.827
2.009
1.599
0.462
4.203
1.34
30.531
2016-07-01 01:00:00
5.693
2.076
1.492
0.426
4.142
1.371
27.787001
2016-07-01 02:00:00
5.157
1.741
1.279
0.355
3.777
1.218
27.787001
2016-07-01 03:00:00
5.09
1.942
1.279
0.391
3.807
1.279
25.044001
2016-07-01 04:00:00
5.358
1.942
1.492
0.462
3.868
1.279
21.948
2016-07-01 05:00:00
5.626
2.143
1.528
0.533
4.051
1.371
21.174
2016-07-01 06:00:00
7.167
2.947
2.132
0.782
5.026
1.858
22.792
2016-07-01 07:00:00
7.435
3.282
2.31
1.031
5.087
2.224
23.143999
2016-07-01 08:00:00
5.559
3.014
2.452
1.173
2.955
1.432
21.667
2016-07-01 09:00:00
4.555
2.545
1.919
0.817
2.68
1.371
17.445999
2016-07-01 10:00:00
4.957
2.545
1.99
0.853
2.955
1.492
19.979
2016-07-01 11:00:00
5.76
2.545
2.203
0.853
3.442
1.492
20.118999
2016-07-01 12:00:00
4.689
2.545
1.812
0.853
2.833
1.523
19.205
2016-07-01 13:00:00
4.689
2.679
1.777
1.244
3.107
1.614
18.572001
2016-07-01 14:00:00
5.09
2.947
2.452
1.35
2.559
1.432
19.556
2016-07-01 15:00:00
5.09
3.148
2.487
1.35
2.589
1.523
17.305
2016-07-01 16:00:00
4.22
2.411
1.706
0.782
2.619
1.492
19.486
2016-07-01 17:00:00
4.756
2.344
1.635
0.711
3.076
1.492
19.134001
2016-07-01 18:00:00
5.626
2.88
2.523
1.208
3.076
1.492
20.681999
2016-07-01 19:00:00
5.492
3.014
2.452
1.208
3.015
1.553
18.712
2016-07-01 20:00:00
5.358
3.014
2.452
1.208
2.863
1.523
17.868
2016-07-01 21:00:00
5.09
2.947
2.381
1.208
2.68
1.523
18.009001
2016-07-01 22:00:00
4.823
2.947
2.203
1.173
2.619
1.523
18.009001
2016-07-01 23:00:00
4.622
2.88
2.132
1.137
2.467
1.492
19.768
2016-07-02 00:00:00
5.224
3.081
2.701
1.315
2.437
1.523
21.104
2016-07-02 01:00:00
5.157
3.014
2.878
1.35
2.345
1.432
19.697001
2016-07-02 02:00:00
5.157
3.148
2.878
1.492
2.284
1.432
20.049
2016-07-02 03:00:00
5.157
3.081
2.914
1.492
2.193
1.401
20.752001
2016-07-02 04:00:00
4.555
3.081
2.452
1.492
2.193
1.401
21.385
2016-07-02 05:00:00
5.425
3.282
3.092
1.706
2.437
1.462
22.23
2016-07-02 06:00:00
5.492
3.282
2.523
1.492
2.985
1.462
20.26
2016-07-02 07:00:00
5.626
3.215
2.487
1.492
3.076
1.523
21.104
2016-07-02 08:00:00
5.559
3.282
2.594
1.67
2.924
1.523
20.612
2016-07-02 09:00:00
5.224
3.215
2.559
1.564
2.68
1.462
18.361
2016-07-02 10:00:00
9.913
4.957
6.645
3.305
3.046
1.553
20.962999
2016-07-02 11:00:00
11.788
5.425
8.173
2.523
3.686
1.675
19.416
2016-07-02 12:00:00
9.645
4.957
6.752
2.132
3.107
1.828
20.823
2016-07-02 13:00:00
10.382
5.76
7.462
2.559
2.985
1.767
20.190001
2016-07-02 14:00:00
8.774
4.689
6.112
2.025
2.894
1.919
21.315001
2016-07-02 15:00:00
10.449
5.157
6.965
2.452
2.772
1.736
22.018999
2016-07-02 16:00:00
9.846
4.823
7.036
2.665
2.894
1.767
20.681999
2016-07-02 17:00:00
9.913
4.823
6.894
2.416
3.229
1.736
25.466
2016-07-02 18:00:00
10.65
4.689
6.929
2.452
3.381
1.797
25.888
2016-07-02 19:00:00
10.114
4.354
6.645
1.812
3.107
1.736
27.857
2016-07-02 20:00:00
9.98
4.153
6.574
1.954
3.411
1.767
27.295
2016-07-02 21:00:00
9.31
4.22
6.005
2.132
3.229
1.858
22.23
2016-07-02 22:00:00
9.444
4.622
6.965
2.168
2.955
1.858
21.948
2016-07-02 23:00:00
9.444
4.287
6.823
2.559
2.589
1.736
27.295
2016-07-03 00:00:00
10.382
5.425
7.604
2.31
2.955
1.675
29.334999
2016-07-03 01:00:00
9.779
5.224
6.716
2.843
2.65
1.675
26.028
2016-07-03 02:00:00
10.382
4.689
7.32
2.203
2.985
1.858
24.34
2016-07-03 03:00:00
9.779
4.153
6.823
1.99
2.528
1.675
26.450001
2016-07-03 04:00:00
10.717
4.756
7.356
2.807
2.65
1.797
25.958
2016-07-03 05:00:00
10.315
4.689
7.391
2.452
2.924
1.858
24.059
2016-07-03 06:00:00
12.592
5.224
8.671
2.203
3.716
1.949
25.325001
2016-07-03 07:00:00
11.119
4.622
7.889
2.843
3.625
1.919
23.636999
2016-07-03 08:00:00
10.65
4.421
7.036
2.025
3.594
1.919
26.379999
2016-07-03 09:00:00
10.047
4.22
6.432
1.67
3.686
1.949
27.365
2016-07-03 10:00:00
11.721
5.09
7.889
2.559
3.564
1.858
28.068001
2016-07-03 11:00:00
12.123
5.358
8.066
2.487
4.082
1.919
29.475
2016-07-03 12:00:00
9.98
5.023
6.858
2.559
3.29
1.858
26.802
2016-07-03 13:00:00
9.243
4.957
6.29
2.63
3.137
1.888
29.968
2016-07-03 14:00:00
10.181
5.425
7.178
3.02
3.076
1.888
30.389999
2016-07-03 15:00:00
9.645
5.425
7.107
2.665
3.015
1.828
31.164
2016-07-03 16:00:00
9.779
4.89
6.503
2.985
3.076
2.01
29.757
2016-07-03 17:00:00
11.119
5.157
7.32
2.914
3.807
1.98
32.289001
2016-07-03 18:00:00
11.052
4.957
7.391
2.523
3.686
1.98
31.938
2016-07-03 19:00:00
10.784
4.89
7.214
2.487
3.594
1.888
28.561001
2016-07-03 20:00:00
11.186
4.89
7.178
2.345
3.96
1.919
21.525999
2016-07-03 21:00:00
10.449
4.89
6.61
2.31
3.807
2.041
22.23
2016-07-03 22:00:00
9.578
5.76
6.787
3.127
3.259
1.888
19.416
2016-07-03 23:00:00
9.31
5.76
6.61
3.056
3.168
1.888
18.572001
2016-07-04 00:00:00
9.913
5.894
6.254
2.63
3.015
1.858
21.667
2016-07-04 01:00:00
8.975
4.957
6.29
2.665
2.863
1.828
25.535999
2016-07-04 02:00:00
8.64
4.823
6.148
2.594
2.924
1.828
27.857
2016-07-04 03:00:00
9.176
5.492
5.579
2.381
2.863
1.858
27.927999
2016-07-04 04:00:00
9.109
4.823
5.65
2.523
2.772
1.797
24.621
2016-07-04 05:00:00
9.846
5.559
5.97
2.949
3.107
1.888
23.848
2016-07-04 06:00:00
11.588
5.425
7.391
2.807
3.807
1.98
23.073999
2016-07-04 07:00:00
11.788
6.095
7.214
2.985
3.899
2.041
22.511
2016-07-04 08:00:00
10.583
5.961
7.143
2.914
3.655
2.071
21.667
2016-07-04 09:00:00
11.588
6.296
7.569
3.056
3.472
2.01
25.395
2016-07-04 10:00:00
11.922
6.229
7.711
3.056
3.746
1.949
25.184
2016-07-04 11:00:00
12.324
5.559
8.422
3.234
4.203
1.98
29.546
2016-07-04 12:00:00
10.382
5.894
6.858
2.63
3.564
1.949
29.475
2016-07-04 13:00:00
10.047
5.425
6.752
3.02
3.32
1.949
29.264
2016-07-04 14:00:00
10.516
6.028
7.107
3.376
3.137
1.919
30.952999
2016-07-04 15:00:00
10.717
6.095
6.787
3.02
3.168
2.01
31.726
2016-07-04 16:00:00
9.98
5.023
6.503
2.559
3.442
2.041
33.132999
2016-07-04 17:00:00
11.32
5.09
7.356
2.452
3.868
2.041
28.983
2016-07-04 18:00:00
11.387
4.957
7.356
2.452
4.295
2.193
28.983
2016-07-04 19:00:00
9.377
3.885
6.894
2.239
2.467
1.188
31.726
2016-07-04 20:00:00
10.114
4.086
7.143
2.239
2.955
1.462
25.184
2016-07-04 21:00:00
10.382
4.823
6.894
2.31
3.503
2.01
30.531
2016-07-04 22:00:00
9.645
4.89
6.61
1.919
3.259
1.919
27.646
2016-07-04 23:00:00
12.726
6.497
9.346
3.482
3.168
1.98
25.466
2016-07-05 00:00:00
11.989
5.626
8.777
2.949
3.198
1.98
25.958
2016-07-05 01:00:00
12.525
6.296
8.955
3.163
3.137
2.01
25.958
2016-07-05 02:00:00
12.324
6.296
8.813
3.376
2.985
1.919
26.028
2016-07-05 03:00:00
10.717
5.425
8.066
2.878
2.833
1.858
28.913
End of preview.