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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 8 new columns ({'year', 'Log GDP per capita', 'Healthy life expectancy at birth', 'Negative affect', 'Happiness score', 'Iso alpha', 'Country name', 'Positive affect'}) and 5 missing columns ({'Country or region', 'Healthy life expectancy', 'GDP per capita', 'Overall rank', 'Score'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Slapping/Happiness/WHR-1.csv (at revision b9481601cc4678da83c0032b0a38c826f9312104)

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 2011, 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
              Country name: string
              Iso alpha: string
              year: int64
              Happiness score: double
              Log GDP per capita: double
              Social support: double
              Healthy life expectancy at birth: double
              Freedom to make life choices: double
              Generosity: double
              Perceptions of corruption: double
              Positive affect: double
              Negative affect: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1862
              to
              {'Overall rank': Value(dtype='int64', id=None), 'Country or region': Value(dtype='string', id=None), 'Score': Value(dtype='float64', id=None), 'GDP per capita': Value(dtype='float64', id=None), 'Social support': Value(dtype='float64', id=None), 'Healthy life expectancy': Value(dtype='float64', id=None), 'Freedom to make life choices': Value(dtype='float64', id=None), 'Generosity': Value(dtype='float64', id=None), 'Perceptions of corruption': 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 1321, 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 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 8 new columns ({'year', 'Log GDP per capita', 'Healthy life expectancy at birth', 'Negative affect', 'Happiness score', 'Iso alpha', 'Country name', 'Positive affect'}) and 5 missing columns ({'Country or region', 'Healthy life expectancy', 'GDP per capita', 'Overall rank', 'Score'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Slapping/Happiness/WHR-1.csv (at revision b9481601cc4678da83c0032b0a38c826f9312104)
              
              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)

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Overall rank
int64
Country or region
string
Score
float64
GDP per capita
float64
Social support
float64
Healthy life expectancy
float64
Freedom to make life choices
float64
Generosity
float64
Perceptions of corruption
float64
1
Finland
7.769
1.34
1.587
0.986
0.596
0.153
0.393
2
Denmark
7.6
1.383
1.573
0.996
0.592
0.252
0.41
3
Norway
7.554
1.488
1.582
1.028
0.603
0.271
0.341
4
Iceland
7.494
1.38
1.624
1.026
0.591
0.354
0.118
5
Netherlands
7.488
1.396
1.522
0.999
0.557
0.322
0.298
6
Switzerland
7.48
1.452
1.526
1.052
0.572
0.263
0.343
7
Sweden
7.343
1.387
1.487
1.009
0.574
0.267
0.373
8
New Zealand
7.307
1.303
1.557
1.026
0.585
0.33
0.38
9
Canada
7.278
1.365
1.505
1.039
0.584
0.285
0.308
10
Austria
7.246
1.376
1.475
1.016
0.532
0.244
0.226
11
Australia
7.228
1.372
1.548
1.036
0.557
0.332
0.29
12
Costa Rica
7.167
1.034
1.441
0.963
0.558
0.144
0.093
13
Israel
7.139
1.276
1.455
1.029
0.371
0.261
0.082
14
Luxembourg
7.09
1.609
1.479
1.012
0.526
0.194
0.316
15
United Kingdom
7.054
1.333
1.538
0.996
0.45
0.348
0.278
16
Ireland
7.021
1.499
1.553
0.999
0.516
0.298
0.31
17
Germany
6.985
1.373
1.454
0.987
0.495
0.261
0.265
18
Belgium
6.923
1.356
1.504
0.986
0.473
0.16
0.21
19
United States
6.892
1.433
1.457
0.874
0.454
0.28
0.128
20
Czech Republic
6.852
1.269
1.487
0.92
0.457
0.046
0.036
21
United Arab Emirates
6.825
1.503
1.31
0.825
0.598
0.262
0.182
22
Malta
6.726
1.3
1.52
0.999
0.564
0.375
0.151
23
Mexico
6.595
1.07
1.323
0.861
0.433
0.074
0.073
24
France
6.592
1.324
1.472
1.045
0.436
0.111
0.183
25
Taiwan
6.446
1.368
1.43
0.914
0.351
0.242
0.097
26
Chile
6.444
1.159
1.369
0.92
0.357
0.187
0.056
27
Guatemala
6.436
0.8
1.269
0.746
0.535
0.175
0.078
28
Saudi Arabia
6.375
1.403
1.357
0.795
0.439
0.08
0.132
29
Qatar
6.374
1.684
1.313
0.871
0.555
0.22
0.167
30
Spain
6.354
1.286
1.484
1.062
0.362
0.153
0.079
31
Panama
6.321
1.149
1.442
0.91
0.516
0.109
0.054
32
Brazil
6.3
1.004
1.439
0.802
0.39
0.099
0.086
33
Uruguay
6.293
1.124
1.465
0.891
0.523
0.127
0.15
34
Singapore
6.262
1.572
1.463
1.141
0.556
0.271
0.453
35
El Salvador
6.253
0.794
1.242
0.789
0.43
0.093
0.074
36
Italy
6.223
1.294
1.488
1.039
0.231
0.158
0.03
37
Bahrain
6.199
1.362
1.368
0.871
0.536
0.255
0.11
38
Slovakia
6.198
1.246
1.504
0.881
0.334
0.121
0.014
39
Trinidad & Tobago
6.192
1.231
1.477
0.713
0.489
0.185
0.016
40
Poland
6.182
1.206
1.438
0.884
0.483
0.117
0.05
41
Uzbekistan
6.174
0.745
1.529
0.756
0.631
0.322
0.24
42
Lithuania
6.149
1.238
1.515
0.818
0.291
0.043
0.042
43
Colombia
6.125
0.985
1.41
0.841
0.47
0.099
0.034
44
Slovenia
6.118
1.258
1.523
0.953
0.564
0.144
0.057
45
Nicaragua
6.105
0.694
1.325
0.835
0.435
0.2
0.127
46
Kosovo
6.1
0.882
1.232
0.758
0.489
0.262
0.006
47
Argentina
6.086
1.092
1.432
0.881
0.471
0.066
0.05
48
Romania
6.07
1.162
1.232
0.825
0.462
0.083
0.005
49
Cyprus
6.046
1.263
1.223
1.042
0.406
0.19
0.041
50
Ecuador
6.028
0.912
1.312
0.868
0.498
0.126
0.087
51
Kuwait
6.021
1.5
1.319
0.808
0.493
0.142
0.097
52
Thailand
6.008
1.05
1.409
0.828
0.557
0.359
0.028
53
Latvia
5.94
1.187
1.465
0.812
0.264
0.075
0.064
54
South Korea
5.895
1.301
1.219
1.036
0.159
0.175
0.056
55
Estonia
5.893
1.237
1.528
0.874
0.495
0.103
0.161
56
Jamaica
5.89
0.831
1.478
0.831
0.49
0.107
0.028
57
Mauritius
5.888
1.12
1.402
0.798
0.498
0.215
0.06
58
Japan
5.886
1.327
1.419
1.088
0.445
0.069
0.14
59
Honduras
5.86
0.642
1.236
0.828
0.507
0.246
0.078
60
Kazakhstan
5.809
1.173
1.508
0.729
0.41
0.146
0.096
61
Bolivia
5.779
0.776
1.209
0.706
0.511
0.137
0.064
62
Hungary
5.758
1.201
1.41
0.828
0.199
0.081
0.02
63
Paraguay
5.743
0.855
1.475
0.777
0.514
0.184
0.08
64
Northern Cyprus
5.718
1.263
1.252
1.042
0.417
0.191
0.162
65
Peru
5.697
0.96
1.274
0.854
0.455
0.083
0.027
66
Portugal
5.693
1.221
1.431
0.999
0.508
0.047
0.025
67
Pakistan
5.653
0.677
0.886
0.535
0.313
0.22
0.098
68
Russia
5.648
1.183
1.452
0.726
0.334
0.082
0.031
69
Philippines
5.631
0.807
1.293
0.657
0.558
0.117
0.107
70
Serbia
5.603
1.004
1.383
0.854
0.282
0.137
0.039
71
Moldova
5.529
0.685
1.328
0.739
0.245
0.181
0
72
Libya
5.525
1.044
1.303
0.673
0.416
0.133
0.152
73
Montenegro
5.523
1.051
1.361
0.871
0.197
0.142
0.08
74
Tajikistan
5.467
0.493
1.098
0.718
0.389
0.23
0.144
75
Croatia
5.432
1.155
1.266
0.914
0.296
0.119
0.022
76
Hong Kong
5.43
1.438
1.277
1.122
0.44
0.258
0.287
77
Dominican Republic
5.425
1.015
1.401
0.779
0.497
0.113
0.101
78
Bosnia and Herzegovina
5.386
0.945
1.212
0.845
0.212
0.263
0.006
79
Turkey
5.373
1.183
1.36
0.808
0.195
0.083
0.106
80
Malaysia
5.339
1.221
1.171
0.828
0.508
0.26
0.024
81
Belarus
5.323
1.067
1.465
0.789
0.235
0.094
0.142
82
Greece
5.287
1.181
1.156
0.999
0.067
0
0.034
83
Mongolia
5.285
0.948
1.531
0.667
0.317
0.235
0.038
84
North Macedonia
5.274
0.983
1.294
0.838
0.345
0.185
0.034
85
Nigeria
5.265
0.696
1.111
0.245
0.426
0.215
0.041
86
Kyrgyzstan
5.261
0.551
1.438
0.723
0.508
0.3
0.023
87
Turkmenistan
5.247
1.052
1.538
0.657
0.394
0.244
0.028
88
Algeria
5.211
1.002
1.16
0.785
0.086
0.073
0.114
89
Morocco
5.208
0.801
0.782
0.782
0.418
0.036
0.076
90
Azerbaijan
5.208
1.043
1.147
0.769
0.351
0.035
0.182
91
Lebanon
5.197
0.987
1.224
0.815
0.216
0.166
0.027
92
Indonesia
5.192
0.931
1.203
0.66
0.491
0.498
0.028
93
China
5.191
1.029
1.125
0.893
0.521
0.058
0.1
94
Vietnam
5.175
0.741
1.346
0.851
0.543
0.147
0.073
95
Bhutan
5.082
0.813
1.321
0.604
0.457
0.37
0.167
96
Cameroon
5.044
0.549
0.91
0.331
0.381
0.187
0.037
97
Bulgaria
5.011
1.092
1.513
0.815
0.311
0.081
0.004
98
Ghana
4.996
0.611
0.868
0.486
0.381
0.245
0.04
99
Ivory Coast
4.944
0.569
0.808
0.232
0.352
0.154
0.09
100
Nepal
4.913
0.446
1.226
0.677
0.439
0.285
0.089
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