Datasets:

---

Rianknow

/

creditscoring

like 2

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 1 missing columns ({'Credit_Score'})

This happened while the csv dataset builder was generating data using

hf://datasets/Rianknow/creditscoring/test.csv (at revision 7fd77bc9b0803a6794931bf3438d3c0d3ac0232e)

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
              ID: string
              Customer_ID: string
              Month: string
              Name: string
              Age: string
              SSN: string
              Occupation: string
              Annual_Income: string
              Monthly_Inhand_Salary: double
              Num_Bank_Accounts: int64
              Num_Credit_Card: int64
              Interest_Rate: int64
              Num_of_Loan: string
              Type_of_Loan: string
              Delay_from_due_date: int64
              Num_of_Delayed_Payment: string
              Changed_Credit_Limit: string
              Num_Credit_Inquiries: double
              Credit_Mix: string
              Outstanding_Debt: string
              Credit_Utilization_Ratio: double
              Credit_History_Age: string
              Payment_of_Min_Amount: string
              Total_EMI_per_month: double
              Amount_invested_monthly: string
              Payment_Behaviour: string
              Monthly_Balance: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 3724
              to
              {'ID': Value(dtype='string', id=None), 'Customer_ID': Value(dtype='string', id=None), 'Month': Value(dtype='string', id=None), 'Name': Value(dtype='string', id=None), 'Age': Value(dtype='string', id=None), 'SSN': Value(dtype='string', id=None), 'Occupation': Value(dtype='string', id=None), 'Annual_Income': Value(dtype='string', id=None), 'Monthly_Inhand_Salary': Value(dtype='float64', id=None), 'Num_Bank_Accounts': Value(dtype='int64', id=None), 'Num_Credit_Card': Value(dtype='int64', id=None), 'Interest_Rate': Value(dtype='int64', id=None), 'Num_of_Loan': Value(dtype='string', id=None), 'Type_of_Loan': Value(dtype='string', id=None), 'Delay_from_due_date': Value(dtype='int64', id=None), 'Num_of_Delayed_Payment': Value(dtype='string', id=None), 'Changed_Credit_Limit': Value(dtype='string', id=None), 'Num_Credit_Inquiries': Value(dtype='float64', id=None), 'Credit_Mix': Value(dtype='string', id=None), 'Outstanding_Debt': Value(dtype='string', id=None), 'Credit_Utilization_Ratio': Value(dtype='float64', id=None), 'Credit_History_Age': Value(dtype='string', id=None), 'Payment_of_Min_Amount': Value(dtype='string', id=None), 'Total_EMI_per_month': Value(dtype='float64', id=None), 'Amount_invested_monthly': Value(dtype='string', id=None), 'Payment_Behaviour': Value(dtype='string', id=None), 'Monthly_Balance': Value(dtype='string', id=None), 'Credit_Score': Value(dtype='string', 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 1 missing columns ({'Credit_Score'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Rianknow/creditscoring/test.csv (at revision 7fd77bc9b0803a6794931bf3438d3c0d3ac0232e)
              
              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)