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How to use _flatten_dtypesdict method in pandera

Best Python code snippet using pandera_python

test_dtypes.py

Source:test_dtypes.py

...274    assert df.equals(coerced_df)275    assert expected_dtype.check(276        pandas_engine.Engine.dtype(coerced_df["col"].dtype)277    )278def _flatten_dtypesdict(*dtype_kinds):279    return [280        (datatype, pd_dtype)281        for dtype_kind in dtype_kinds282        for datatype, pd_dtype in dtype_kind.items()283    ]284numeric_dtypes = _flatten_dtypesdict(285    int_dtypes,286    uint_dtypes,287    float_dtypes,288    complex_dtypes,289    boolean_dtypes,290)291nullable_numeric_dtypes = _flatten_dtypesdict(292    nullable_int_dtypes,293    nullable_uint_dtypes,294    NULLABLE_FLOAT_DTYPES,295    nullable_boolean_dtypes,296    *([NULLABLE_FLOAT_DTYPES] if NULLABLE_FLOAT_DTYPES else []),297)298nominal_dtypes = _flatten_dtypesdict(299    string_dtypes,300    nullable_string_dtypes,301    category_dtypes,302)303@pytest.mark.parametrize(304    "dtypes, examples",305    [306        (numeric_dtypes, [1]),307        (nullable_numeric_dtypes, [1, None]),308        (nominal_dtypes, ["A", "B"]),309    ],310)311@hypothesis.given(st.data())312def test_coerce_cast(dtypes, examples, data):...

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