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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 missing columns ({'Unnamed: 0'})
This happened while the csv dataset builder was generating data using
zip://2020blockgroupvoting.csv::/tmp/hf-datasets-cache/medium/datasets/34069626094448-config-parquet-and-info-openenvironments-blockgro-22ec08c0/downloads/74a411eddf9314f50d4a60108d97ceefde3538575c5a0308c24a4bd74d8bae61
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
REP: double
DEM: double
LIB: double
OTH: double
area: double
gap: double
precincts: int64
BLOCKGROUP_GEOID: int64
STATE: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1261
to
{'Unnamed: 0': Value(dtype='int64', id=None), 'REP': Value(dtype='float64', id=None), 'DEM': Value(dtype='float64', id=None), 'LIB': Value(dtype='float64', id=None), 'OTH': Value(dtype='float64', id=None), 'area': Value(dtype='float64', id=None), 'gap': Value(dtype='float64', id=None), 'precincts': Value(dtype='int64', id=None), 'BLOCKGROUP_GEOID': Value(dtype='int64', id=None), 'STATE': Value(dtype='string', 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 1 missing columns ({'Unnamed: 0'})
This happened while the csv dataset builder was generating data using
zip://2020blockgroupvoting.csv::/tmp/hf-datasets-cache/medium/datasets/34069626094448-config-parquet-and-info-openenvironments-blockgro-22ec08c0/downloads/74a411eddf9314f50d4a60108d97ceefde3538575c5a0308c24a4bd74d8bae61
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 | REP
float64 | DEM
float64 | LIB
float64 | OTH
float64 | area
float64 | gap
float64 | precincts
int64 | BLOCKGROUP_GEOID
int64 | STATE
string |
|---|---|---|---|---|---|---|---|---|---|
0
| 112.127739
| 534.97283
| 4.331114
| 2.085351
| 2,919,245.140912
| -0
| 3
| 10,730,059,033
|
AL
|
1
| 238.139112
| 1,128.574145
| 9.350864
| 4.420572
| 6,173,555.610505
| 0
| 5
| 10,730,059,031
|
AL
|
2
| 87.400801
| 750.192068
| 4.658955
| 0.838575
| 3,483,037.530101
| 0
| 3
| 10,730,059,053
|
AL
|
3
| 64.588253
| 469.813181
| 2.064465
| 1.179694
| 2,076,797.852439
| -0
| 3
| 10,730,059,051
|
AL
|
4
| 8.078341
| 376.039237
| 1.616617
| 0.646077
| 1,054,731.55096
| -0
| 4
| 10,730,057,023
|
AL
|
5
| 51.200234
| 383.604457
| 2.65651
| 1.693264
| 2,403,020.441501
| 0
| 4
| 10,730,124,021
|
AL
|
6
| 79.793164
| 195.605818
| 2.216485
| 0.831187
| 3,637,223.466304
| -0
| 2
| 10,730,124,022
|
AL
|
7
| 10.522264
| 201.831467
| 2.630322
| 2.045301
| 756,895.123049
| -0.000001
| 4
| 10,730,106,023
|
AL
|
8
| 238.654759
| 580.659124
| 6.694034
| 2.638914
| 12,971,331.017016
| 0
| 5
| 10,730,124,023
|
AL
|
9
| 143.760124
| 326.829538
| 3.991349
| 1.831325
| 11,430,118.87901
| -0
| 6
| 10,730,124,031
|
AL
|
10
| 222.973959
| 687.00585
| 5.172531
| 1.959651
| 19,074,758.680019
| -33.984228
| 7
| 10,730,125,003
|
AL
|
11
| 22.314268
| 221.587827
| 2.134387
| 0.582107
| 1,476,230.555898
| -0
| 3
| 10,730,001,005
|
AL
|
12
| 31.897073
| 337.739607
| 3.224635
| 0.856471
| 2,398,507.948856
| -0
| 3
| 10,730,001,003
|
AL
|
13
| 39.414593
| 329.271154
| 6.186368
| 2.544282
| 1,270,075.982838
| -0
| 3
| 10,730,003,001
|
AL
|
14
| 8.562229
| 131.423417
| 0.81545
| 0.271817
| 876,007.086049
| -0
| 3
| 10,730,003,002
|
AL
|
15
| 8.331875
| 128.171993
| 0.796508
| 0.264843
| 856,890.172022
| 0
| 4
| 10,730,003,003
|
AL
|
16
| 4.712907
| 148.639373
| 1.284048
| 0.231841
| 1,762,284.842348
| -0
| 2
| 10,730,004,001
|
AL
|
17
| 1.657523
| 58.519776
| 0.506465
| 0.092085
| 687,385.044923
| 0
| 1
| 10,730,004,002
|
AL
|
18
| 2.175824
| 75.694579
| 0.654957
| 0.11898
| 890,158.202254
| 0
| 2
| 10,730,004,003
|
AL
|
19
| 1.266542
| 44.71596
| 0.386999
| 0.070363
| 525,242.652044
| -0
| 2
| 10,730,004,004
|
AL
|
20
| 71.428123
| 525.217772
| 2.363311
| 1.275591
| 2,333,474.831494
| 0
| 3
| 10,730,059,052
|
AL
|
21
| 123.728673
| 566.291265
| 4.743223
| 2.103171
| 3,124,692.64144
| -0
| 2
| 10,730,059,071
|
AL
|
22
| 76.302532
| 373.365414
| 2.920402
| 1.334316
| 1,976,893.656698
| -0
| 4
| 10,730,059,082
|
AL
|
23
| 46.779283
| 285.927011
| 1.623188
| 0.296123
| 1,150,937.730231
| -0
| 5
| 10,730,059,083
|
AL
|
24
| 34.67965
| 146.007519
| 1.028368
| 0.325253
| 3,899,167.7263
| 0
| 2
| 10,730,125,001
|
AL
|
25
| 262.052674
| 160.134131
| 3.630936
| 0.736917
| 9,696,495.220525
| 0
| 5
| 10,730,125,004
|
AL
|
26
| 96.248866
| 112.990821
| 4.498529
| 1.837399
| 2,380,591.605085
| -0
| 3
| 10,730,126,021
|
AL
|
27
| 230.098855
| 944.731922
| 6.148195
| 2.676441
| 509,916,033.278729
| -0.00001
| 11
| 11,056,871,001
|
AL
|
28
| 82.780822
| 253.648929
| 1.48581
| 0.424517
| 75,251,141.848854
| 0
| 3
| 11,056,870,002
|
AL
|
29
| 18.589596
| 649.338516
| 5.618697
| 1.021009
| 7,633,297.071855
| -0
| 5
| 10,730,004,005
|
AL
|
30
| 3.113403
| 86.699972
| 0.716425
| 0.343786
| 592,124.824336
| 0
| 2
| 10,730,005,001
|
AL
|
31
| 6.655781
| 148.569236
| 1.134693
| 0.503433
| 1,007,256.644511
| 0
| 3
| 10,730,005,003
|
AL
|
32
| 2.961518
| 163.306567
| 1.128197
| 0.282049
| 856,317.518598
| -0
| 1
| 10,730,007,003
|
AL
|
33
| 4.280411
| 236.598217
| 1.623828
| 0.406029
| 1,257,436.864299
| -0
| 4
| 10,730,007,002
|
AL
|
34
| 4.913426
| 173.707828
| 1.409904
| 0.128141
| 1,454,993.233037
| -0
| 4
| 10,730,008,001
|
AL
|
35
| 44.863437
| 248.009699
| 1.633872
| 0.524201
| 1,132,214.731407
| -0
| 3
| 10,730,059,081
|
AL
|
36
| 116.728042
| 144.230231
| 2.968986
| 0.62505
| 2,096,678.506808
| 0
| 1
| 10,730,100,012
|
AL
|
37
| 182.150702
| 232.263157
| 4.60479
| 1.04699
| 3,300,009.898488
| 0
| 3
| 10,730,100,014
|
AL
|
38
| 268.238166
| 213.165433
| 5.142299
| 1.972938
| 2,792,380.996555
| -0
| 3
| 10,730,100,022
|
AL
|
39
| 643.460315
| 498.84932
| 12.289231
| 4.721534
| 6,366,240.290638
| -0
| 3
| 10,730,100,023
|
AL
|
40
| 78.486003
| 92.477937
| 3.716607
| 1.530368
| 1,797,949.443111
| -0
| 1
| 10,730,126,022
|
AL
|
41
| 245.753954
| 353.954107
| 10.527954
| 3.673898
| 8,102,028.933388
| 0
| 3
| 10,730,127,014
|
AL
|
42
| 85.038708
| 84.135944
| 2.096353
| 0.409198
| 7,015,331.836037
| 0
| 3
| 10,730,127,013
|
AL
|
43
| 231.003065
| 143.172364
| 8.361857
| 2.823229
| 1,152,674.91118
| -0
| 2
| 10,730,128,022
|
AL
|
44
| 247.729729
| 21.678269
| 1.259203
| 0.82999
| 66,733,249.780757
| 0
| 3
| 10,730,141,024
|
AL
|
45
| 14.708646
| 673.154725
| 4.328648
| 1.082162
| 7,665,600.928238
| -0
| 6
| 10,730,008,002
|
AL
|
46
| 1.992918
| 118.0154
| 0.693189
| 0.173297
| 815,818.800836
| 0
| 1
| 10,730,008,005
|
AL
|
47
| 7.456836
| 434.547973
| 2.53261
| 0.637728
| 2,982,590.384361
| 0
| 8
| 10,730,008,004
|
AL
|
48
| 4.624681
| 181.671249
| 1.087813
| 0.297324
| 977,311.326172
| -0
| 2
| 10,730,011,001
|
AL
|
49
| 14.869891
| 419.652202
| 2.101221
| 0.37388
| 2,514,746.185033
| -0
| 4
| 10,730,011,002
|
AL
|
50
| 8.782565
| 299.888479
| 1.46376
| 0.182975
| 1,812,672.737106
| 0
| 5
| 10,730,011,004
|
AL
|
51
| 18.869396
| 607.973357
| 2.985736
| 0.38107
| 3,712,726.140944
| -0
| 4
| 10,730,011,005
|
AL
|
52
| 101.092225
| 78.305655
| 1.929808
| 0.742234
| 997,999.62978
| -0
| 2
| 10,730,100,024
|
AL
|
53
| 3.040316
| 117.077236
| 0.196098
| 0.000055
| 1,038,114.191843
| 0
| 3
| 10,730,101,001
|
AL
|
54
| 63.676923
| 286.902517
| 1.132386
| 0.687844
| 2,429,193.955394
| -0
| 5
| 10,730,102,002
|
AL
|
55
| 13.227154
| 173.161878
| 1.097683
| 0.049066
| 710,924.421344
| 0
| 3
| 10,730,102,003
|
AL
|
56
| 22.577728
| 265.333865
| 1.04045
| 1.723924
| 854,396.64133
| -0
| 4
| 10,730,103,011
|
AL
|
57
| 44.997293
| 385.741079
| 2.330832
| 0.239091
| 1,566,008.843193
| -0
| 5
| 10,730,103,012
|
AL
|
58
| 91.31183
| 403.547151
| 2.167651
| 0.423988
| 1,974,531.148061
| 0
| 3
| 10,730,104,015
|
AL
|
59
| 540.785429
| 355.725777
| 12.631056
| 8.224873
| 5,475,468.002428
| -0.00038
| 3
| 10,730,129,101
|
AL
|
60
| 859.023641
| 367.483849
| 16.339953
| 17.340745
| 4,670,074.967507
| 0
| 3
| 10,730,129,052
|
AL
|
61
| 28.716502
| 1,173.228924
| 7.418931
| 2.281424
| 6,150,682.309525
| -0
| 6
| 10,730,011,003
|
AL
|
62
| 11.093241
| 389.218115
| 1.271345
| 1.243118
| 4,827,121.301964
| -0
| 6
| 10,730,012,001
|
AL
|
63
| 13.402467
| 546.917596
| 1.555896
| 2.14817
| 5,616,016.094489
| -0
| 7
| 10,730,012,002
|
AL
|
64
| 2.928544
| 114.088844
| 0.46027
| 0.214026
| 776,231.623972
| -0
| 3
| 10,730,012,004
|
AL
|
65
| 6.32437
| 255.865563
| 1.07079
| 0.535395
| 889,356.938538
| 0
| 2
| 10,730,014,001
|
AL
|
66
| 6.818855
| 302.385175
| 1.380412
| 0.666205
| 1,043,594.216462
| 0
| 4
| 10,730,014,003
|
AL
|
67
| 8.387228
| 372.94729
| 1.68017
| 0.831099
| 1,285,614.98063
| -0
| 6
| 10,730,014,002
|
AL
|
68
| 5.030381
| 81.378628
| 0.700972
| 0.247312
| 431,144.191279
| 0
| 3
| 10,730,015,004
|
AL
|
69
| 10.529383
| 197.126546
| 1.108315
| 0.45917
| 795,231.565313
| 0
| 3
| 10,730,015,001
|
AL
|
70
| 29.019069
| 131.210621
| 0.694236
| 0.138847
| 631,467.43929
| 0
| 2
| 10,730,104,011
|
AL
|
71
| 33.425146
| 250.830765
| 1.507214
| 0.118307
| 1,055,452.645417
| -0
| 2
| 10,730,104,012
|
AL
|
72
| 40.218332
| 174.126135
| 0.948209
| 0.181737
| 864,851.848432
| -0
| 3
| 10,730,104,014
|
AL
|
73
| 19.672117
| 311.187381
| 2.048051
| 0.002087
| 1,165,920.555124
| 0
| 4
| 10,730,104,013
|
AL
|
74
| 52.411267
| 222.88894
| 2.049056
| 0.300741
| 3,458,740.241727
| -0
| 5
| 10,730,104,021
|
AL
|
75
| 11.270556
| 219.697792
| 1.07423
| 0.433377
| 1,147,416.863256
| -0
| 5
| 10,730,105,001
|
AL
|
76
| 10.578799
| 149.539819
| 0.130602
| 0.52241
| 1,013,222.566377
| -0
| 2
| 10,730,105,002
|
AL
|
77
| 29.642758
| 447.738815
| 0.848965
| 1.394774
| 2,857,568.687248
| 0
| 4
| 10,730,105,003
|
AL
|
78
| 36.618117
| 873.337584
| 7.703414
| 4.90151
| 3,262,935.502212
| 0
| 7
| 10,730,106,021
|
AL
|
79
| 3.8735
| 184.033089
| 0.843186
| 0.419343
| 519,830.332411
| 0
| 2
| 10,730,106,024
|
AL
|
80
| 4.852319
| 270.294817
| 1.010922
| 0.40442
| 738,509.885025
| 0
| 2
| 10,730,106,025
|
AL
|
81
| 10.470446
| 148.625947
| 0.543919
| 1.087839
| 654,071.227396
| -0
| 3
| 10,730,020,003
|
AL
|
82
| 415.776648
| 466.815954
| 12.538091
| 9.069023
| 3,981,000.653593
| -0
| 5
| 10,730,129,071
|
AL
|
83
| 7.55413
| 353.155587
| 1.510826
| 0.755413
| 1,248,122.231103
| -0
| 5
| 10,730,130,021
|
AL
|
84
| 4.653134
| 147.373517
| 0.504563
| 0.209323
| 629,539.104569
| -0
| 2
| 10,730,015,003
|
AL
|
85
| 11.949846
| 188.383089
| 1.813718
| 0.617302
| 848,134.328713
| -0
| 3
| 10,730,016,003
|
AL
|
86
| 12.012266
| 255.499
| 1.144025
| 0.762684
| 712,782.398261
| -0
| 3
| 10,730,019,022
|
AL
|
87
| 11.195874
| 211.798455
| 1.067235
| 0.623975
| 680,177.599746
| 0
| 2
| 10,730,019,023
|
AL
|
88
| 66.469817
| 733.159631
| 3.395519
| 5.523254
| 3,519,231.081865
| 0.000001
| 5
| 10,730,020,001
|
AL
|
89
| 12.383073
| 152.121304
| 0.844885
| 0.141717
| 682,128.601839
| -0
| 2
| 10,730,132,002
|
AL
|
90
| 24.343507
| 73.743014
| 0.831242
| 0.118749
| 680,030.929225
| -0
| 1
| 11,130,302,001
|
AL
|
91
| 4.258624
| 237.240867
| 0.887213
| 0.354885
| 648,189.312736
| -0
| 3
| 10,730,106,026
|
AL
|
92
| 24.199043
| 925.846062
| 4.343644
| 3.649875
| 2,603,009.495522
| -0
| 4
| 10,730,106,022
|
AL
|
93
| 22.626466
| 746.324178
| 2.846042
| 0.00884
| 2,508,394.986619
| 0
| 5
| 10,730,106,031
|
AL
|
94
| 298.485018
| 452.05561
| 14.502848
| 11.843076
| 2,027,744.49341
| 0
| 5
| 10,730,107,021
|
AL
|
95
| 367.008258
| 611.405461
| 19.291645
| 19.427796
| 1,754,335.912828
| -0
| 6
| 10,730,107,022
|
AL
|
96
| 122.284203
| 132.296179
| 4.76432
| 3.65955
| 744,835.719572
| 0
| 2
| 10,730,107,023
|
AL
|
97
| 587.015186
| 541.013634
| 18.384504
| 21.773969
| 2,103,294.753388
| 0
| 4
| 10,730,107,031
|
AL
|
98
| 12.449045
| 581.996939
| 2.489817
| 1.24486
| 2,056,799.486498
| 0
| 5
| 10,730,130,022
|
AL
|
99
| 13.624456
| 630.797778
| 4.168172
| 0.313709
| 2,186,217.690051
| 0
| 5
| 10,730,131,002
|
AL
|
End of preview.
Problem and Opportunity
In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected.
For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups:
- are exclusive, and do not overlap
- are adjacent, fully covering their corresponding state and potentially county
- have roughly the same size in area, population and voter presence
Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure.
The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates.
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