How to use reshape_failure_cases method in pandera

Best Python code snippet using pandera_python

test_checks.py

Source:test_checks.py

...309    # it's ok to access it because the function needs to be tested.310    check = Check(lambda x: x.isna().sum() == 0)311    for data in [1, "foobar", 1.0, {"key": "value"}, list(range(10))]:312        with pytest.raises(TypeError):313            error_formatters.reshape_failure_cases(314                data, bool(check.n_failure_cases)315            )316def test_check_equality_operators() -> None:317    """Test the usage of == between a Check and an entirely different Check,318    and a non-Check."""319    check = Check(lambda g: g["foo"]["col1"].iat[0] == 1, groupby="col3")320    not_equal_check = Check(lambda x: x.isna().sum() == 0)321    assert check == copy.deepcopy(check)322    assert check != not_equal_check323    assert check != "not a check"324def test_equality_operators_functional_equivalence() -> None:325    """Test the usage of == for Checks where the Check callable object has326    the same implementation."""327    main_check = Check(lambda g: g["foo"]["col1"].iat[0] == 1, groupby="col3")...

utils.py

Source:utils.py

...100        raise TypeError(101            f"type of data_container {type(data_container)} not understood. "102            "Must be a pandas Series, Index, or DataFrame."103        )104    return error_formatters.reshape_failure_cases(105        failure_cases, ignore_na=False...

error_formatters.py

Source:error_formatters.py

...44            "index": [None],45            "failure_case": [x],46        }47    )48def reshape_failure_cases(49    failure_cases: Union[pd.DataFrame, pd.Series], ignore_na: bool = True50) -> pd.DataFrame:51    """Construct readable error messages for vectorized_error_message.52    :param failure_cases: The failure cases encountered by the element-wise53        or vectorized validator.54    :param ignore_na: whether or not to ignore null failure cases.55    :returns: DataFrame where index contains failure cases, the "index"56        column contains a list of integer indexes in the validation57        DataFrame that caused the failure, and a "count" column58        representing how many failures of that case occurred.59    """60    if not (61        check_utils.is_table(failure_cases)62        or check_utils.is_field(failure_cases)...

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