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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)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.
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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