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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 5 new columns ({'20220228', '18.1', '17', '71.76', '18'}) and 5 missing columns ({'electrical-meter-id', 'customer-id', 'hour', 'date', 'amount-of-consumption'}). This happened while the csv dataset builder was generating data using hf://datasets/andrewlee1807/Gyeonggi/xab.csv (at revision 264665bf10ad1c4efb94585c47119f20c040304e) 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 18: int64 20220228: int64 17: int64 18.1: int64 71.76: double -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 794 to {'electrical-meter-id': Value(dtype='int64', id=None), 'date': Value(dtype='int64', id=None), 'hour': Value(dtype='int64', id=None), 'customer-id': Value(dtype='int64', id=None), 'amount-of-consumption': 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 5 new columns ({'20220228', '18.1', '17', '71.76', '18'}) and 5 missing columns ({'electrical-meter-id', 'customer-id', 'hour', 'date', 'amount-of-consumption'}). This happened while the csv dataset builder was generating data using hf://datasets/andrewlee1807/Gyeonggi/xab.csv (at revision 264665bf10ad1c4efb94585c47119f20c040304e) 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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electrical-meter-id
int64 | date
int64 | hour
int64 | customer-id
int64 | amount-of-consumption
float64 |
---|---|---|---|---|
1 | 20,200,801 | 4 | 1 | 38.74 |
1 | 20,200,801 | 18 | 1 | 69.74 |
1 | 20,200,801 | 22 | 1 | 69.51 |
1 | 20,200,801 | 23 | 1 | 70.92 |
1 | 20,200,802 | 5 | 1 | 61.08 |
1 | 20,200,802 | 6 | 1 | 62.67 |
1 | 20,200,803 | 13 | 1 | 222.38 |
1 | 20,200,804 | 2 | 1 | 74.83 |
1 | 20,200,804 | 14 | 1 | 220.58 |
1 | 20,200,804 | 20 | 1 | 117.12 |
1 | 20,200,806 | 1 | 1 | 69.03 |
1 | 20,200,806 | 4 | 1 | 62.13 |
1 | 20,200,806 | 6 | 1 | 45.19 |
1 | 20,200,806 | 12 | 1 | 216.91 |
1 | 20,200,806 | 20 | 1 | 90.36 |
1 | 20,200,808 | 5 | 1 | 32.37 |
1 | 20,200,808 | 14 | 1 | 75.33 |
1 | 20,200,809 | 11 | 1 | 74.21 |
1 | 20,200,809 | 17 | 1 | 21.72 |
1 | 20,200,809 | 23 | 1 | 55.03 |
1 | 20,200,810 | 7 | 1 | 43.63 |
1 | 20,200,810 | 17 | 1 | 152.01 |
1 | 20,200,810 | 22 | 1 | 83.43 |
1 | 20,200,810 | 24 | 1 | 64.72 |
1 | 20,200,811 | 1 | 1 | 56.42 |
1 | 20,200,811 | 2 | 1 | 54.48 |
1 | 20,200,811 | 12 | 1 | 103.25 |
1 | 20,200,812 | 7 | 1 | 55.74 |
1 | 20,200,812 | 9 | 1 | 104.66 |
1 | 20,200,812 | 19 | 1 | 63.21 |
1 | 20,200,813 | 1 | 1 | 51.22 |
1 | 20,200,813 | 15 | 1 | 104.66 |
1 | 20,200,813 | 16 | 1 | 206.83 |
1 | 20,200,813 | 19 | 1 | 125.8 |
1 | 20,200,813 | 20 | 1 | 137.08 |
1 | 20,200,814 | 10 | 1 | 166.82 |
1 | 20,200,814 | 17 | 1 | 52.3 |
1 | 20,200,814 | 24 | 1 | 40.06 |
1 | 20,200,815 | 23 | 1 | 54.19 |
1 | 20,200,816 | 4 | 1 | 23.59 |
1 | 20,200,816 | 19 | 1 | 63.21 |
1 | 20,200,817 | 9 | 1 | 72.05 |
1 | 20,200,818 | 6 | 1 | 47.16 |
1 | 20,200,818 | 7 | 1 | 70.51 |
1 | 20,200,818 | 12 | 1 | 240.04 |
1 | 20,200,818 | 20 | 1 | 133.1 |
1 | 20,200,818 | 22 | 1 | 81.98 |
1 | 20,200,818 | 23 | 1 | 58.39 |
1 | 20,200,819 | 12 | 1 | 229.13 |
1 | 20,200,819 | 17 | 1 | 215.4 |
1 | 20,200,820 | 4 | 1 | 48.93 |
1 | 20,200,820 | 20 | 1 | 119 |
1 | 20,200,820 | 22 | 1 | 75.22 |
1 | 20,200,821 | 4 | 1 | 25.94 |
1 | 20,200,821 | 22 | 1 | 71.61 |
1 | 20,200,821 | 24 | 1 | 50.66 |
1 | 20,200,822 | 14 | 1 | 75.1 |
1 | 20,200,823 | 2 | 1 | 46.3 |
1 | 20,200,823 | 14 | 1 | 89.19 |
1 | 20,200,824 | 4 | 1 | 44.34 |
1 | 20,200,824 | 5 | 1 | 34.78 |
1 | 20,200,824 | 6 | 1 | 47.33 |
1 | 20,200,824 | 17 | 1 | 225.66 |
1 | 20,200,824 | 24 | 1 | 51.36 |
1 | 20,200,825 | 2 | 1 | 49.1 |
1 | 20,200,825 | 3 | 1 | 49.32 |
1 | 20,200,825 | 4 | 1 | 49.16 |
1 | 20,200,825 | 7 | 1 | 59.73 |
1 | 20,200,826 | 2 | 1 | 52.3 |
1 | 20,200,826 | 9 | 1 | 188 |
1 | 20,200,826 | 20 | 1 | 99.29 |
1 | 20,200,827 | 1 | 1 | 74.92 |
1 | 20,200,827 | 5 | 1 | 61.6 |
1 | 20,200,828 | 12 | 1 | 238.64 |
1 | 20,200,828 | 14 | 1 | 231.94 |
1 | 20,200,829 | 3 | 1 | 49.91 |
1 | 20,200,829 | 17 | 1 | 81.5 |
1 | 20,200,829 | 23 | 1 | 50.95 |
1 | 20,200,830 | 2 | 1 | 48.1 |
1 | 20,200,830 | 9 | 1 | 72.81 |
1 | 20,200,830 | 19 | 1 | 78.76 |
1 | 20,200,831 | 6 | 1 | 46.83 |
1 | 20,200,831 | 7 | 1 | 62.74 |
1 | 20,200,831 | 13 | 1 | 224.64 |
1 | 20,200,831 | 18 | 1 | 209.4 |
1 | 20,200,831 | 23 | 1 | 60.04 |
1 | 20,200,901 | 1 | 1 | 49.17 |
1 | 20,200,901 | 2 | 1 | 50.07 |
1 | 20,200,901 | 3 | 1 | 48 |
1 | 20,200,901 | 4 | 1 | 47.31 |
1 | 20,200,901 | 5 | 1 | 48.26 |
1 | 20,200,901 | 6 | 1 | 48.08 |
1 | 20,200,901 | 7 | 1 | 61.44 |
1 | 20,200,901 | 8 | 1 | 98.28 |
1 | 20,200,901 | 9 | 1 | 200.21 |
1 | 20,200,901 | 10 | 1 | 214.38 |
1 | 20,200,901 | 11 | 1 | 215.67 |
1 | 20,200,901 | 12 | 1 | 223.29 |
1 | 20,200,901 | 13 | 1 | 217.1 |
1 | 20,200,901 | 14 | 1 | 217.97 |
End of preview.
Dataset Description
Gyeonggi dataset is 10,000 households based on the highest meter reading rate for all branches of the around in Gyeonggi Province, South Korea. For privacy reasons, the name of the household is not provided. We only provide the ID of the household. |
Dataset Summary
This dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.
electrical-meter-id | date | hour | customer-id | amount-of-consumption |
---|---|---|---|---|
7871 | 20201020 | 1 | 7871 | 4.25 |
7871 | 20201020 | 2 | 7871 | 4.12 |
7871 | 20201020 | 3 | 7871 | 4.08 |
7871 | 20201020 | 4 | 7871 | 4.03 |
7871 | 20201020 | 5 | 7871 | 4.09 |
Our experiment focuses on the total electricity consumption of a particular ID 6499
license: apache-2.0
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