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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 ({'has_fluoropolymer_production', 'soil_pfos_ug_kg', 'surface_water_latest_avg_ng_l', 'soil_pfoa_ug_kg', 'data_sources_count', 'blood_median_ng_ml', 'has_regulatory_standards', 'breast_milk_pfoa_median_ng_l'}) and 9 missing columns ({'pfoa_median', 'n', 'total_pfas_median', 'pfos_mean', 'year', 'pfoa_mean', 'total_pfas_mean', 'region', 'pfos_median'}).
This happened while the csv dataset builder was generating data using
zip://pfas_dataset/country_master_dataset.csv::hf://datasets/EduDevCommons/Global-Comprehensive-Dataset-on-PFAS-Contamination@c4ba0fd0c5110081b5c623810923e08048d34203/Global Comprehensive Dataset on PFAS Contamination.zip, ['hf://datasets/EduDevCommons/Global-Comprehensive-Dataset-on-PFAS-Contamination@c4ba0fd0c5110081b5c623810923e08048d34203/Global Comprehensive Dataset on PFAS Contamination.zip']
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 "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
country: string
surface_water_latest_avg_ng_l: double
blood_median_ng_ml: double
breast_milk_pfoa_median_ng_l: double
has_regulatory_standards: bool
soil_pfoa_ug_kg: double
soil_pfos_ug_kg: double
has_fluoropolymer_production: bool
data_sources_count: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1534
to
{'region': Value('string'), 'country': Value('string'), 'year': Value('string'), 'n': Value('string'), 'pfoa_mean': Value('float64'), 'pfoa_median': Value('string'), 'pfos_mean': Value('float64'), 'pfos_median': Value('string'), 'total_pfas_mean': Value('float64'), 'total_pfas_median': Value('float64')}
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
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 ({'has_fluoropolymer_production', 'soil_pfos_ug_kg', 'surface_water_latest_avg_ng_l', 'soil_pfoa_ug_kg', 'data_sources_count', 'blood_median_ng_ml', 'has_regulatory_standards', 'breast_milk_pfoa_median_ng_l'}) and 9 missing columns ({'pfoa_median', 'n', 'total_pfas_median', 'pfos_mean', 'year', 'pfoa_mean', 'total_pfas_mean', 'region', 'pfos_median'}).
This happened while the csv dataset builder was generating data using
zip://pfas_dataset/country_master_dataset.csv::hf://datasets/EduDevCommons/Global-Comprehensive-Dataset-on-PFAS-Contamination@c4ba0fd0c5110081b5c623810923e08048d34203/Global Comprehensive Dataset on PFAS Contamination.zip, ['hf://datasets/EduDevCommons/Global-Comprehensive-Dataset-on-PFAS-Contamination@c4ba0fd0c5110081b5c623810923e08048d34203/Global Comprehensive Dataset on PFAS Contamination.zip']
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.
region string | country string | year string | n string | pfoa_mean float64 | pfoa_median string | pfos_mean float64 | pfos_median string | total_pfas_mean float64 | total_pfas_median float64 |
|---|---|---|---|---|---|---|---|---|---|
Asia | Lebanon | 2018-2021 | 49 | 34.29 | 42 | 79.3 | 91 | 160.45 | 202.8 |
Asia | China | 2020-2021 | 1151 | 325.7 | 335.9 | 52.83 | 49.72 | null | null |
Asia | China | 2017-2020 | 100 | 153 | 61.2 | 63.2 | 35 | 279 | 144 |
Asia | China | 2018-2019 | 174 | 87 | 31 | 25 | 1 | 203 | 112 |
Asia | Korea | 2018 | 207 | 114 | 100 | 58 | 50 | 298 | 263 |
Asia | Vietnam | 2008 | 40 pooled | null | null | 75.8 | 58.5 | null | null |
Asia | India | 2008 | 39 pooled | null | null | 46.1 | 39.4 | null | null |
Asia | Indonesia | 2008 | 20 pooled | null | null | 83.6 | 67.2 | null | null |
Asia | Jordan | 2015 | 79 pooled | 143.64 | 82.5 | 34.78 | 50 | null | null |
Europe | Czech Republic | 2018-2022 | 70 | 34 | 22 | 14 | 8 | null | null |
Europe | Czech Republic | 2018-2022 | 161 | 17 | 10 | 25 | 10 | null | null |
Europe | Sweden | 2015-2020 | 109 | null | 30 | null | 130 | null | null |
Europe | Denmark | 1997-2002 | 16 | null | null | null | null | null | 690 |
Europe | Finland | 1997-2002 | 11 | null | null | null | null | null | 33 |
Europe | Netherlands | 2013-2018 | 123 | null | 43 | null | 35 | null | null |
Europe | Germany | 2016 | 180 | 22 | <25 | 17 | <25 | null | null |
Europe | Spain | 2015-2018 | 82 | null | 7.17 | null | <0.86 | null | 87.67 |
Europe | Ireland | null | 92 | 130 | 100 | 38 | 20 | null | null |
America | USA | 2014 | 116 | 22 | null | 30 | null | null | null |
America | USA | 2014-2019 | 426 | null | 17 | null | 24 | null | null |
America | USA | 2019 | 50 | null | 13.9 | null | 30.4 | null | 121 |
America | Canada | 2008-2011 | 664 | 41.4 | 34.2 | 35.7 | 30.2 | 110 | 95.6 |
Africa | South Africa | 2020 | 50 | 260 | 210 | 90 | 50 | null | null |
null | Afghanistan | null | null | null | null | null | null | null | null |
null | Antarctica | null | null | null | null | null | null | null | null |
null | Australia | null | null | null | null | null | null | null | null |
null | Austria | null | null | null | null | null | null | null | null |
null | Belgium/Slovakia/Spain | null | null | null | null | null | null | null | null |
null | Canada | null | null | null | null | null | null | null | null |
null | China | null | null | null | null | null | null | null | null |
null | Czech Republic | null | null | null | null | null | null | null | null |
null | Denmark | null | null | null | null | null | null | null | null |
null | European Union | null | null | null | null | null | null | null | null |
null | Finland | null | null | null | null | null | null | null | null |
null | France | null | null | null | null | null | null | null | null |
null | Germany | null | null | null | null | null | null | null | null |
null | Ghana | null | null | null | null | null | null | null | null |
null | Greenland | null | null | null | null | null | null | null | null |
null | India | null | null | null | null | null | null | null | null |
null | Indonesia | null | null | null | null | null | null | null | null |
null | Ireland | null | null | null | null | null | null | null | null |
null | Italy | null | null | null | null | null | null | null | null |
null | Japan | null | null | null | null | null | null | null | null |
null | Jordan | null | null | null | null | null | null | null | null |
null | Korea | null | null | null | null | null | null | null | null |
null | Lebanon | null | null | null | null | null | null | null | null |
null | Netherlands | null | null | null | null | null | null | null | null |
null | New Zealand | null | null | null | null | null | null | null | null |
null | Norway | null | null | null | null | null | null | null | null |
null | South Africa | null | null | null | null | null | null | null | null |
null | South Korea | null | null | null | null | null | null | null | null |
null | Spain | null | null | null | null | null | null | null | null |
null | Sweden | null | null | null | null | null | null | null | null |
null | Tanzania | null | null | null | null | null | null | null | null |
null | USA | null | null | null | null | null | null | null | null |
null | Vietnam | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | China | null | null | null | null | null | null | null | null |
null | Japan | null | null | null | null | null | null | null | null |
null | India | null | null | null | null | null | null | null | null |
null | USA | null | null | null | null | null | null | null | null |
null | Global Total | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
null | Australia | null | null | null | null | null | null | null | null |
null | Australia | null | null | null | null | null | null | null | null |
null | Australia | null | null | null | null | null | null | null | null |
null | France | null | null | null | null | null | null | null | null |
null | Italy | null | null | null | null | null | null | null | null |
Asia | Japan | 2001-2005 | 401 | null | null | null | null | null | null |
Asia | Japan | 2003-2011 | 772 | null | null | null | null | null | null |
Asia | China | 2018 | 775 | null | null | null | null | null | null |
Asia | Afghanistan | 2010 | 12 children, 43 adults | null | null | null | null | null | null |
Asia | Lebanon | 2018-2021 | 419 | null | null | null | null | null | null |
Asia | China | 2019-2021 | 203 patients, 203 controls | null | null | null | null | null | null |
Asia | Korea | 2006-2007 | 319 | null | null | null | null | null | null |
Europe | Belgium/Slovakia/Spain | 2017-2020 | 733 | null | null | null | null | null | null |
Europe | Spain | 2017-2019 | 129 | null | null | null | null | null | null |
Europe | Czech Republic | 2019-2020 | 164 | null | null | null | null | null | null |
Europe | Norway | 2016 | 1094 | null | null | null | null | null | null |
Europe | Austria | 2017-2019 | 136 | null | null | null | null | null | null |
Europe | Greenland | 2018-2019 | 305 | null | null | null | null | null | null |
Africa | Ghana | null | 64 | null | null | null | null | null | null |
Africa | Tanzania | 2019 | 48 | null | null | null | null | null | null |
null | China | 2025 | null | null | null | null | null | null | null |
null | Australia | 2025 | null | null | null | null | null | null | null |
null | New Zealand | 2025 | null | null | null | null | null | null | null |
null | European Union | 2020 | null | null | null | null | null | null | null |
null | Canada | 2025 | null | null | null | null | null | null | null |
null | USA | 2024 | null | null | null | null | null | null | null |
null | South Korea | 2017 | null | null | null | null | null | null | null |
null | China | null | null | null | null | null | null | null | null |
null | China | null | null | null | null | null | null | null | null |
null | Antarctica | null | null | null | null | null | null | null | null |
null | Antarctica | null | null | null | null | null | null | null | null |
End of preview.
Global Comprehensive Dataset on PFAS Contamination
This dataset was published on Kaggle by Samyakraj Bayar and mirrored here.
Download
The dataset is available as a ZIP archive: Global Comprehensive Dataset on PFAS Contamination.zip
License
MIT
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