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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 2 new columns ({'Upper Confidence Interval', 'Lower Confidence Interval'}) and 1 missing columns ({'Standard Error'}). This happened while the csv dataset builder was generating data using hf://datasets/nateraw/world-happiness/2016.csv (at revision 6bba8e2773773739878a9e5ab1d8e10b8733260f) 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: string Region: string Happiness Rank: int64 Happiness Score: double Lower Confidence Interval: double Upper Confidence Interval: double Economy (GDP per Capita): double Family: double Health (Life Expectancy): double Freedom: double Trust (Government Corruption): double Generosity: double Dystopia Residual: double -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1986 to {'Country': Value(dtype='string', id=None), 'Region': Value(dtype='string', id=None), 'Happiness Rank': Value(dtype='int64', id=None), 'Happiness Score': Value(dtype='float64', id=None), 'Standard Error': Value(dtype='float64', id=None), 'Economy (GDP per Capita)': Value(dtype='float64', id=None), 'Family': Value(dtype='float64', id=None), 'Health (Life Expectancy)': Value(dtype='float64', id=None), 'Freedom': Value(dtype='float64', id=None), 'Trust (Government Corruption)': Value(dtype='float64', id=None), 'Generosity': Value(dtype='float64', id=None), 'Dystopia Residual': 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 2 new columns ({'Upper Confidence Interval', 'Lower Confidence Interval'}) and 1 missing columns ({'Standard Error'}). This happened while the csv dataset builder was generating data using hf://datasets/nateraw/world-happiness/2016.csv (at revision 6bba8e2773773739878a9e5ab1d8e10b8733260f) 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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Country
string | Region
string | Happiness Rank
int64 | Happiness Score
float64 | Standard Error
float64 | Economy (GDP per Capita)
float64 | Family
float64 | Health (Life Expectancy)
float64 | Freedom
float64 | Trust (Government Corruption)
float64 | Generosity
float64 | Dystopia Residual
float64 |
---|---|---|---|---|---|---|---|---|---|---|---|
Switzerland | Western Europe | 1 | 7.587 | 0.03411 | 1.39651 | 1.34951 | 0.94143 | 0.66557 | 0.41978 | 0.29678 | 2.51738 |
Iceland | Western Europe | 2 | 7.561 | 0.04884 | 1.30232 | 1.40223 | 0.94784 | 0.62877 | 0.14145 | 0.4363 | 2.70201 |
Denmark | Western Europe | 3 | 7.527 | 0.03328 | 1.32548 | 1.36058 | 0.87464 | 0.64938 | 0.48357 | 0.34139 | 2.49204 |
Norway | Western Europe | 4 | 7.522 | 0.0388 | 1.459 | 1.33095 | 0.88521 | 0.66973 | 0.36503 | 0.34699 | 2.46531 |
Canada | North America | 5 | 7.427 | 0.03553 | 1.32629 | 1.32261 | 0.90563 | 0.63297 | 0.32957 | 0.45811 | 2.45176 |
Finland | Western Europe | 6 | 7.406 | 0.0314 | 1.29025 | 1.31826 | 0.88911 | 0.64169 | 0.41372 | 0.23351 | 2.61955 |
Netherlands | Western Europe | 7 | 7.378 | 0.02799 | 1.32944 | 1.28017 | 0.89284 | 0.61576 | 0.31814 | 0.4761 | 2.4657 |
Sweden | Western Europe | 8 | 7.364 | 0.03157 | 1.33171 | 1.28907 | 0.91087 | 0.6598 | 0.43844 | 0.36262 | 2.37119 |
New Zealand | Australia and New Zealand | 9 | 7.286 | 0.03371 | 1.25018 | 1.31967 | 0.90837 | 0.63938 | 0.42922 | 0.47501 | 2.26425 |
Australia | Australia and New Zealand | 10 | 7.284 | 0.04083 | 1.33358 | 1.30923 | 0.93156 | 0.65124 | 0.35637 | 0.43562 | 2.26646 |
Israel | Middle East and Northern Africa | 11 | 7.278 | 0.0347 | 1.22857 | 1.22393 | 0.91387 | 0.41319 | 0.07785 | 0.33172 | 3.08854 |
Costa Rica | Latin America and Caribbean | 12 | 7.226 | 0.04454 | 0.95578 | 1.23788 | 0.86027 | 0.63376 | 0.10583 | 0.25497 | 3.17728 |
Austria | Western Europe | 13 | 7.2 | 0.03751 | 1.33723 | 1.29704 | 0.89042 | 0.62433 | 0.18676 | 0.33088 | 2.5332 |
Mexico | Latin America and Caribbean | 14 | 7.187 | 0.04176 | 1.02054 | 0.91451 | 0.81444 | 0.48181 | 0.21312 | 0.14074 | 3.60214 |
United States | North America | 15 | 7.119 | 0.03839 | 1.39451 | 1.24711 | 0.86179 | 0.54604 | 0.1589 | 0.40105 | 2.51011 |
Brazil | Latin America and Caribbean | 16 | 6.983 | 0.04076 | 0.98124 | 1.23287 | 0.69702 | 0.49049 | 0.17521 | 0.14574 | 3.26001 |
Luxembourg | Western Europe | 17 | 6.946 | 0.03499 | 1.56391 | 1.21963 | 0.91894 | 0.61583 | 0.37798 | 0.28034 | 1.96961 |
Ireland | Western Europe | 18 | 6.94 | 0.03676 | 1.33596 | 1.36948 | 0.89533 | 0.61777 | 0.28703 | 0.45901 | 1.9757 |
Belgium | Western Europe | 19 | 6.937 | 0.03595 | 1.30782 | 1.28566 | 0.89667 | 0.5845 | 0.2254 | 0.2225 | 2.41484 |
United Arab Emirates | Middle East and Northern Africa | 20 | 6.901 | 0.03729 | 1.42727 | 1.12575 | 0.80925 | 0.64157 | 0.38583 | 0.26428 | 2.24743 |
United Kingdom | Western Europe | 21 | 6.867 | 0.01866 | 1.26637 | 1.28548 | 0.90943 | 0.59625 | 0.32067 | 0.51912 | 1.96994 |
Oman | Middle East and Northern Africa | 22 | 6.853 | 0.05335 | 1.36011 | 1.08182 | 0.76276 | 0.63274 | 0.32524 | 0.21542 | 2.47489 |
Venezuela | Latin America and Caribbean | 23 | 6.81 | 0.06476 | 1.04424 | 1.25596 | 0.72052 | 0.42908 | 0.11069 | 0.05841 | 3.19131 |
Singapore | Southeastern Asia | 24 | 6.798 | 0.0378 | 1.52186 | 1.02 | 1.02525 | 0.54252 | 0.4921 | 0.31105 | 1.88501 |
Panama | Latin America and Caribbean | 25 | 6.786 | 0.0491 | 1.06353 | 1.1985 | 0.79661 | 0.5421 | 0.0927 | 0.24434 | 2.84848 |
Germany | Western Europe | 26 | 6.75 | 0.01848 | 1.32792 | 1.29937 | 0.89186 | 0.61477 | 0.21843 | 0.28214 | 2.11569 |
Chile | Latin America and Caribbean | 27 | 6.67 | 0.058 | 1.10715 | 1.12447 | 0.85857 | 0.44132 | 0.12869 | 0.33363 | 2.67585 |
Qatar | Middle East and Northern Africa | 28 | 6.611 | 0.06257 | 1.69042 | 1.0786 | 0.79733 | 0.6404 | 0.52208 | 0.32573 | 1.55674 |
France | Western Europe | 29 | 6.575 | 0.03512 | 1.27778 | 1.26038 | 0.94579 | 0.55011 | 0.20646 | 0.12332 | 2.21126 |
Argentina | Latin America and Caribbean | 30 | 6.574 | 0.04612 | 1.05351 | 1.24823 | 0.78723 | 0.44974 | 0.08484 | 0.11451 | 2.836 |
Czech Republic | Central and Eastern Europe | 31 | 6.505 | 0.04168 | 1.17898 | 1.20643 | 0.84483 | 0.46364 | 0.02652 | 0.10686 | 2.67782 |
Uruguay | Latin America and Caribbean | 32 | 6.485 | 0.04539 | 1.06166 | 1.2089 | 0.8116 | 0.60362 | 0.24558 | 0.2324 | 2.32142 |
Colombia | Latin America and Caribbean | 33 | 6.477 | 0.05051 | 0.91861 | 1.24018 | 0.69077 | 0.53466 | 0.0512 | 0.18401 | 2.85737 |
Thailand | Southeastern Asia | 34 | 6.455 | 0.03557 | 0.9669 | 1.26504 | 0.7385 | 0.55664 | 0.03187 | 0.5763 | 2.31945 |
Saudi Arabia | Middle East and Northern Africa | 35 | 6.411 | 0.04633 | 1.39541 | 1.08393 | 0.72025 | 0.31048 | 0.32524 | 0.13706 | 2.43872 |
Spain | Western Europe | 36 | 6.329 | 0.03468 | 1.23011 | 1.31379 | 0.95562 | 0.45951 | 0.06398 | 0.18227 | 2.12367 |
Malta | Western Europe | 37 | 6.302 | 0.04206 | 1.2074 | 1.30203 | 0.88721 | 0.60365 | 0.13586 | 0.51752 | 1.6488 |
Taiwan | Eastern Asia | 38 | 6.298 | 0.03868 | 1.29098 | 1.07617 | 0.8753 | 0.3974 | 0.08129 | 0.25376 | 2.32323 |
Kuwait | Middle East and Northern Africa | 39 | 6.295 | 0.04456 | 1.55422 | 1.16594 | 0.72492 | 0.55499 | 0.25609 | 0.16228 | 1.87634 |
Suriname | Latin America and Caribbean | 40 | 6.269 | 0.09811 | 0.99534 | 0.972 | 0.6082 | 0.59657 | 0.13633 | 0.16991 | 2.79094 |
Trinidad and Tobago | Latin America and Caribbean | 41 | 6.168 | 0.10895 | 1.21183 | 1.18354 | 0.61483 | 0.55884 | 0.0114 | 0.31844 | 2.26882 |
El Salvador | Latin America and Caribbean | 42 | 6.13 | 0.05618 | 0.76454 | 1.02507 | 0.67737 | 0.4035 | 0.11776 | 0.10692 | 3.035 |
Guatemala | Latin America and Caribbean | 43 | 6.123 | 0.05224 | 0.74553 | 1.04356 | 0.64425 | 0.57733 | 0.09472 | 0.27489 | 2.74255 |
Uzbekistan | Central and Eastern Europe | 44 | 6.003 | 0.04361 | 0.63244 | 1.34043 | 0.59772 | 0.65821 | 0.30826 | 0.22837 | 2.23741 |
Slovakia | Central and Eastern Europe | 45 | 5.995 | 0.04267 | 1.16891 | 1.26999 | 0.78902 | 0.31751 | 0.03431 | 0.16893 | 2.24639 |
Japan | Eastern Asia | 46 | 5.987 | 0.03581 | 1.27074 | 1.25712 | 0.99111 | 0.49615 | 0.1806 | 0.10705 | 1.68435 |
South Korea | Eastern Asia | 47 | 5.984 | 0.04098 | 1.24461 | 0.95774 | 0.96538 | 0.33208 | 0.07857 | 0.18557 | 2.21978 |
Ecuador | Latin America and Caribbean | 48 | 5.975 | 0.04528 | 0.86402 | 0.99903 | 0.79075 | 0.48574 | 0.1809 | 0.11541 | 2.53942 |
Bahrain | Middle East and Northern Africa | 49 | 5.96 | 0.05412 | 1.32376 | 1.21624 | 0.74716 | 0.45492 | 0.306 | 0.17362 | 1.73797 |
Italy | Western Europe | 50 | 5.948 | 0.03914 | 1.25114 | 1.19777 | 0.95446 | 0.26236 | 0.02901 | 0.22823 | 2.02518 |
Bolivia | Latin America and Caribbean | 51 | 5.89 | 0.05642 | 0.68133 | 0.97841 | 0.5392 | 0.57414 | 0.088 | 0.20536 | 2.82334 |
Moldova | Central and Eastern Europe | 52 | 5.889 | 0.03799 | 0.59448 | 1.01528 | 0.61826 | 0.32818 | 0.01615 | 0.20951 | 3.10712 |
Paraguay | Latin America and Caribbean | 53 | 5.878 | 0.04563 | 0.75985 | 1.30477 | 0.66098 | 0.53899 | 0.08242 | 0.3424 | 2.18896 |
Kazakhstan | Central and Eastern Europe | 54 | 5.855 | 0.04114 | 1.12254 | 1.12241 | 0.64368 | 0.51649 | 0.08454 | 0.11827 | 2.24729 |
Slovenia | Central and Eastern Europe | 55 | 5.848 | 0.04251 | 1.18498 | 1.27385 | 0.87337 | 0.60855 | 0.03787 | 0.25328 | 1.61583 |
Lithuania | Central and Eastern Europe | 56 | 5.833 | 0.03843 | 1.14723 | 1.25745 | 0.73128 | 0.21342 | 0.01031 | 0.02641 | 2.44649 |
Nicaragua | Latin America and Caribbean | 57 | 5.828 | 0.05371 | 0.59325 | 1.14184 | 0.74314 | 0.55475 | 0.19317 | 0.27815 | 2.32407 |
Peru | Latin America and Caribbean | 58 | 5.824 | 0.04615 | 0.90019 | 0.97459 | 0.73017 | 0.41496 | 0.05989 | 0.14982 | 2.5945 |
Belarus | Central and Eastern Europe | 59 | 5.813 | 0.03938 | 1.03192 | 1.23289 | 0.73608 | 0.37938 | 0.1909 | 0.11046 | 2.1309 |
Poland | Central and Eastern Europe | 60 | 5.791 | 0.04263 | 1.12555 | 1.27948 | 0.77903 | 0.53122 | 0.04212 | 0.16759 | 1.86565 |
Malaysia | Southeastern Asia | 61 | 5.77 | 0.0433 | 1.12486 | 1.07023 | 0.72394 | 0.53024 | 0.10501 | 0.33075 | 1.88541 |
Croatia | Central and Eastern Europe | 62 | 5.759 | 0.04394 | 1.08254 | 0.79624 | 0.78805 | 0.25883 | 0.0243 | 0.05444 | 2.75414 |
Libya | Middle East and Northern Africa | 63 | 5.754 | 0.07832 | 1.13145 | 1.11862 | 0.7038 | 0.41668 | 0.11023 | 0.18295 | 2.09066 |
Russia | Central and Eastern Europe | 64 | 5.716 | 0.03135 | 1.13764 | 1.23617 | 0.66926 | 0.36679 | 0.03005 | 0.00199 | 2.27394 |
Jamaica | Latin America and Caribbean | 65 | 5.709 | 0.13693 | 0.81038 | 1.15102 | 0.68741 | 0.50442 | 0.02299 | 0.2123 | 2.32038 |
North Cyprus | Western Europe | 66 | 5.695 | 0.05635 | 1.20806 | 1.07008 | 0.92356 | 0.49027 | 0.1428 | 0.26169 | 1.59888 |
Cyprus | Western Europe | 67 | 5.689 | 0.0558 | 1.20813 | 0.89318 | 0.92356 | 0.40672 | 0.06146 | 0.30638 | 1.88931 |
Algeria | Middle East and Northern Africa | 68 | 5.605 | 0.05099 | 0.93929 | 1.07772 | 0.61766 | 0.28579 | 0.17383 | 0.07822 | 2.43209 |
Kosovo | Central and Eastern Europe | 69 | 5.589 | 0.05018 | 0.80148 | 0.81198 | 0.63132 | 0.24749 | 0.04741 | 0.2831 | 2.76579 |
Turkmenistan | Central and Eastern Europe | 70 | 5.548 | 0.04175 | 0.95847 | 1.22668 | 0.53886 | 0.4761 | 0.30844 | 0.16979 | 1.86984 |
Mauritius | Sub-Saharan Africa | 71 | 5.477 | 0.07197 | 1.00761 | 0.98521 | 0.7095 | 0.56066 | 0.07521 | 0.37744 | 1.76145 |
Hong Kong | Eastern Asia | 72 | 5.474 | 0.05051 | 1.38604 | 1.05818 | 1.01328 | 0.59608 | 0.37124 | 0.39478 | 0.65429 |
Estonia | Central and Eastern Europe | 73 | 5.429 | 0.04013 | 1.15174 | 1.22791 | 0.77361 | 0.44888 | 0.15184 | 0.0868 | 1.58782 |
Indonesia | Southeastern Asia | 74 | 5.399 | 0.02596 | 0.82827 | 1.08708 | 0.63793 | 0.46611 | 0 | 0.51535 | 1.86399 |
Vietnam | Southeastern Asia | 75 | 5.36 | 0.03107 | 0.63216 | 0.91226 | 0.74676 | 0.59444 | 0.10441 | 0.1686 | 2.20173 |
Turkey | Middle East and Northern Africa | 76 | 5.332 | 0.03864 | 1.06098 | 0.94632 | 0.73172 | 0.22815 | 0.15746 | 0.12253 | 2.08528 |
Kyrgyzstan | Central and Eastern Europe | 77 | 5.286 | 0.03823 | 0.47428 | 1.15115 | 0.65088 | 0.43477 | 0.04232 | 0.3003 | 2.2327 |
Nigeria | Sub-Saharan Africa | 78 | 5.268 | 0.04192 | 0.65435 | 0.90432 | 0.16007 | 0.34334 | 0.0403 | 0.27233 | 2.89319 |
Bhutan | Southern Asia | 79 | 5.253 | 0.03225 | 0.77042 | 1.10395 | 0.57407 | 0.53206 | 0.15445 | 0.47998 | 1.63794 |
Azerbaijan | Central and Eastern Europe | 80 | 5.212 | 0.03363 | 1.02389 | 0.93793 | 0.64045 | 0.3703 | 0.16065 | 0.07799 | 2.00073 |
Pakistan | Southern Asia | 81 | 5.194 | 0.03726 | 0.59543 | 0.41411 | 0.51466 | 0.12102 | 0.10464 | 0.33671 | 3.10709 |
Jordan | Middle East and Northern Africa | 82 | 5.192 | 0.04524 | 0.90198 | 1.05392 | 0.69639 | 0.40661 | 0.14293 | 0.11053 | 1.87996 |
Montenegro | Central and Eastern Europe | 82 | 5.192 | 0.05235 | 0.97438 | 0.90557 | 0.72521 | 0.1826 | 0.14296 | 0.1614 | 2.10017 |
China | Eastern Asia | 84 | 5.14 | 0.02424 | 0.89012 | 0.94675 | 0.81658 | 0.51697 | 0.02781 | 0.08185 | 1.8604 |
Zambia | Sub-Saharan Africa | 85 | 5.129 | 0.06988 | 0.47038 | 0.91612 | 0.29924 | 0.48827 | 0.12468 | 0.19591 | 2.6343 |
Romania | Central and Eastern Europe | 86 | 5.124 | 0.06607 | 1.04345 | 0.88588 | 0.7689 | 0.35068 | 0.00649 | 0.13748 | 1.93129 |
Serbia | Central and Eastern Europe | 87 | 5.123 | 0.04864 | 0.92053 | 1.00964 | 0.74836 | 0.20107 | 0.02617 | 0.19231 | 2.025 |
Portugal | Western Europe | 88 | 5.102 | 0.04802 | 1.15991 | 1.13935 | 0.87519 | 0.51469 | 0.01078 | 0.13719 | 1.26462 |
Latvia | Central and Eastern Europe | 89 | 5.098 | 0.0464 | 1.11312 | 1.09562 | 0.72437 | 0.29671 | 0.06332 | 0.18226 | 1.62215 |
Philippines | Southeastern Asia | 90 | 5.073 | 0.04934 | 0.70532 | 1.03516 | 0.58114 | 0.62545 | 0.12279 | 0.24991 | 1.7536 |
Somaliland region | Sub-Saharan Africa | 91 | 5.057 | 0.06161 | 0.18847 | 0.95152 | 0.43873 | 0.46582 | 0.39928 | 0.50318 | 2.11032 |
Morocco | Middle East and Northern Africa | 92 | 5.013 | 0.0342 | 0.73479 | 0.64095 | 0.60954 | 0.41691 | 0.08546 | 0.07172 | 2.45373 |
Macedonia | Central and Eastern Europe | 93 | 5.007 | 0.05376 | 0.91851 | 1.00232 | 0.73545 | 0.33457 | 0.05327 | 0.22359 | 1.73933 |
Mozambique | Sub-Saharan Africa | 94 | 4.971 | 0.07896 | 0.08308 | 1.02626 | 0.09131 | 0.34037 | 0.15603 | 0.22269 | 3.05137 |
Albania | Central and Eastern Europe | 95 | 4.959 | 0.05013 | 0.87867 | 0.80434 | 0.81325 | 0.35733 | 0.06413 | 0.14272 | 1.89894 |
Bosnia and Herzegovina | Central and Eastern Europe | 96 | 4.949 | 0.06913 | 0.83223 | 0.91916 | 0.79081 | 0.09245 | 0.00227 | 0.24808 | 2.06367 |
Lesotho | Sub-Saharan Africa | 97 | 4.898 | 0.09438 | 0.37545 | 1.04103 | 0.07612 | 0.31767 | 0.12504 | 0.16388 | 2.79832 |
Dominican Republic | Latin America and Caribbean | 98 | 4.885 | 0.07446 | 0.89537 | 1.17202 | 0.66825 | 0.57672 | 0.14234 | 0.21684 | 1.21305 |
Laos | Southeastern Asia | 99 | 4.876 | 0.06698 | 0.59066 | 0.73803 | 0.54909 | 0.59591 | 0.24249 | 0.42192 | 1.73799 |
Mongolia | Eastern Asia | 100 | 4.874 | 0.03313 | 0.82819 | 1.3006 | 0.60268 | 0.43626 | 0.02666 | 0.3323 | 1.34759 |
End of preview.