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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)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.
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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