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How to use prepare_dataframe_check_output method in pandera

Best Python code snippet using pandera_python

check_utils.py

Source:check_utils.py

...103            failure_cases = failure_cases.groupby(check_output).head(104                n_failure_cases105            )106    return check_output, failure_cases107def prepare_dataframe_check_output(108    check_obj: pd.DataFrame,109    check_output: pd.DataFrame,110    df_orig: Optional[pd.DataFrame] = None,111    ignore_na: bool = True,112    n_failure_cases: Optional[int] = None,113) -> Tuple[pd.Series, pd.Series]:114    """Unstack a dataframe of boolean values.115    Check results consisting of a boolean dataframe should be reported at the116    most granular level.117    """118    if df_orig is not None:119        assert df_orig.shape == check_output.shape120    if df_orig is None:121        df_orig = check_obj...

utils.py

Source:utils.py

...46        check_output = data_container.apply(  # type: ignore[union-attr]47            numpy_pandas_coercible,48            args=(data_type,),49        )50        _, failure_cases = check_utils.prepare_dataframe_check_output(51            data_container,52            check_output,53            ignore_na=False,54        )55    elif check_utils.is_field(data_container):56        check_output = numpy_pandas_coercible(data_container, data_type)57        _, failure_cases = check_utils.prepare_series_check_output(58            data_container,59            check_output,60            ignore_na=False,61        )62    else:63        raise TypeError(64            f"type of data_container {type(data_container)} not understood. "...

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