call_report.fca.reader.read_schedule_file#
- call_report.fca.reader.read_schedule_file(*, data_path: Path, layout: FCALayout, dataframe_type: None = None) NativeDataFrame[source]#
- call_report.fca.reader.read_schedule_file(*, data_path: Path, layout: FCALayout, dataframe_type: Literal['pandas']) pandas.DataFrame
- call_report.fca.reader.read_schedule_file(*, data_path: Path, layout: FCALayout, dataframe_type: Literal['pyarrow_table']) pyarrow.Table
- call_report.fca.reader.read_schedule_file(*, data_path: Path, layout: FCALayout, dataframe_type: Literal['polars_dataframe']) polars.DataFrame
- call_report.fca.reader.read_schedule_file(*, data_path: Path, layout: FCALayout, dataframe_type: Literal['polars_lazyframe']) polars.LazyFrame
Parse a schedule’s data file into a tidy native dataframe.
The row shape depends on layout’s scenario. A
"single"layout produces one row per institution. The two multi-occurrence scenarios produce one row per (institution, reported code), with the code itself taken from the data rather than assumed from a fixed count.- Parameters:
- data_pathpathlib.Path
Path to the raw, comma-delimited data file.
- layoutFCALayout
The layout describing data_path’s columns, as returned by call_report.fca.layout.parse_layout.
- dataframe_type{“pandas”, “pyarrow_table”, “polars_lazyframe”, “polars_dataframe”}, optional
The dataframe type to convert the result to as a final step. Leave this
None(the default) to get back whatever backend call_report.config.get_config currently has configured. Set it when the code that consumes this result needs a specific type, for example a pandas DataFrame while the package is configured to use polars.
- Returns:
- NativeDataFrame
A native dataframe of the configured backend, or of dataframe_type if it was supplied.
- Raises:
- LayoutParseError
If a row’s field count cannot be reconciled with layout.