Datasets:

THULab
/

nigerian-banking-retail-transactions

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Nigerian Retail Banking Transactions (TsFile)

This dataset is an Apache TsFile conversion of electricsheepafrica/nigerian-banking-retail-transactions, a synthetic Nigerian retail banking transaction dataset for Current and Savings Accounts (CASA) with fraud detection labels.

Source Dataset

Original dataset: electricsheepafrica/nigerian-banking-retail-transactions
Author: Electric Sheep Africa
Dataset type: Banking and finance
Version: 1.0
Original formats: Parquet and CSV sample
Rows: 5,000,000
Columns: 16
Time range: 2023-01-01 to 2024-12-31 in the source card; converted rows span 2023-01-01 00:07:00 to 2024-12-30 23:59:00
Geography: Nigeria, 37 states/FCT
Fraud prevalence: 0.8% (40,000 fraud rows and 4,960,000 non-fraud rows in the converted file)
Modalities: Time-series / tabular transaction log
License: Apache 2.0

The source dataset card describes realistic Nigerian retail-banking patterns, including mobile banking, POS usage, state-specific transaction distributions, Nigerian merchant categories, and fraud labels for detection and anomaly-modeling use cases.

Converted TsFile Data

TsFile path: nigerian_retail_transactions_full_1.tsfile
Table name: nigerian_retail_transactions
Conversion mode: Java TsFile Parquet import tool, schema mode
Time precision: milliseconds (ms)
Rows converted: 5,000,000
Dropped rows: none
Source file used: nigerian_retail_transactions_full.parquet

Time, TAG, and FIELD Mapping

Source column	TsFile role	Type	Notes
`timestamp`	Time	INT64 timestamp	Transaction timestamp converted to TsFile `Time` in milliseconds.
`transaction_id`	TAG	STRING	Unique transaction identifier; used as the device/tag key to preserve each event.
`account_id`	FIELD	STRING	Account identifier.
`customer_id`	FIELD	STRING	Customer identifier.
`amount_ngn`	FIELD	DOUBLE	Transaction amount in Nigerian Naira.
`balance_before_ngn`	FIELD	DOUBLE	Account balance before transaction.
`balance_after_ngn`	FIELD	DOUBLE	Account balance after transaction.
`transaction_type`	FIELD	STRING	Debit or credit.
`channel`	FIELD	STRING	Transaction channel.
`merchant_category_code`	FIELD	STRING	ISO 18245 merchant category code.
`merchant_name`	FIELD	STRING	Merchant name.
`location_lga`	FIELD	STRING	Local Government Area.
`location_state`	FIELD	STRING	Nigerian state/FCT.
`device_id`	FIELD	STRING	Device fingerprint for digital channels when present.
`status`	FIELD	STRING	Transaction status.
`fraud_flag`	FIELD	BOOLEAN	Fraud detection label.

transaction_id is unique across all 5,000,000 source rows and is used as the TAG to avoid event collisions when multiple transactions share the same timestamp. The source timestamp column is represented as TsFile Time and is not duplicated as a FIELD.

Minimal Read Example

from tsfile import TsFileReader

path = "nigerian_retail_transactions_full_1.tsfile"
with TsFileReader(path) as reader:
    schemas = reader.get_all_table_schemas()
    print(schemas.keys())  # includes: nigerian_retail_transactions

Citation

@dataset{nigerian_retail_transactions_2025,
  author = {Electric Sheep Africa},
  title = {Nigerian Retail Banking Transactions Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian-banking-retail-transactions}
}

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