How to use prepare_dataframe_check_output method in pandera

Best Python code snippet using pandera_python

check_utils.py

Source:check_utils.py Github

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...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...

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utils.py

Source:utils.py Github

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...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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