How to use _test_datatype_with_schema method in pandera

Best Python code snippet using pandera_python

test_schemas_on_koalas.py

Source:test_schemas_on_koalas.py

...75            "str_column": list("aabbcceedd"),76        }77    )78    assert isinstance(schema.validate(kdf), ks.DataFrame)79def _test_datatype_with_schema(80    dtype: pandas_engine.DataType,81    schema: typing.Union[pa.DataFrameSchema, pa.SeriesSchema],82    data: st.DataObject,83):84    """Test pandera datatypes against koalas data containers.85    Handle case where koalas can't handle datetimes before 1900-01-01 00:04:00,86    raising an overflow87    """88    data_container_cls = {89        pa.DataFrameSchema: ks.DataFrame,90        pa.SeriesSchema: ks.Series,91        pa.Column: ks.DataFrame,92    }[type(schema)]93    # pandas automatically upcasts numeric datatypes when defining Indexes,94    # so we want to skip this pytest.raises expectation for types that are95    # technically unsupported by koalas96    if dtype in KOALAS_UNSUPPORTED:97        with pytest.raises(TypeError):98            sample = data.draw(schema.strategy(size=3))99            data_container_cls(sample)100        return101    sample = data.draw(schema.strategy(size=3))102    if dtype is pandas_engine.DateTime or isinstance(103        dtype, pandas_engine.DateTime104    ):105        if MIN_TIMESTAMP is not None and (sample < MIN_TIMESTAMP).any(106            axis=None107        ):108            with pytest.raises(109                OverflowError, match="mktime argument out of range"110            ):111                data_container_cls(sample)112            return113    else:114        assert isinstance(data_container_cls(sample), data_container_cls)115@pytest.mark.parametrize("dtype_cls", DTYPES)116@pytest.mark.parametrize("coerce", [True, False])117@hypothesis.given(st.data())118def test_dataframe_schema_dtypes(119    dtype_cls: pandas_engine.DataType,120    coerce: bool,121    data: st.DataObject,122):123    """124    Test that all supported koalas data types work as expected for dataframes.125    """126    if dtype_cls in UNSUPPORTED_STRATEGY_DTYPE_CLS:127        pytest.skip(128            f"type {dtype_cls} currently not supported by the strategies "129            "module"130        )131    schema = pa.DataFrameSchema({"column": pa.Column(dtype_cls)})132    schema.coerce = coerce133    _test_datatype_with_schema(dtype_cls, schema, data)134@pytest.mark.parametrize("dtype_cls", DTYPES)135@pytest.mark.parametrize("coerce", [True, False])136@pytest.mark.parametrize("schema_cls", [pa.SeriesSchema, pa.Column])137@hypothesis.given(st.data())138def test_field_schema_dtypes(139    dtype_cls: pandas_engine.DataType,140    coerce: bool,141    schema_cls,142    data: st.DataObject,143):144    """145    Test that all supported koalas data types work as expected for series.146    """147    if dtype_cls in UNSUPPORTED_STRATEGY_DTYPE_CLS:148        pytest.skip(149            f"type {dtype_cls} currently not supported by the strategies "150            "module"151        )152    schema = schema_cls(dtype_cls, name="field")153    schema.coerce = coerce154    _test_datatype_with_schema(dtype_cls, schema, data)155@pytest.mark.parametrize(156    "dtype",157    [158        int,159        float,160        bool,161        str,162        pandas_engine.DateTime,163    ],164)165@pytest.mark.parametrize("coerce", [True, False])166@pytest.mark.parametrize("schema_cls", [pa.Index, pa.MultiIndex])167@hypothesis.given(st.data())168def test_index_dtypes(...

test_schemas_on_modin.py

Source:test_schemas_on_modin.py

...80            "str_column": list("aabbcceedd"),81        }82    )83    assert isinstance(schema.validate(mdf), mpd.DataFrame)84def _test_datatype_with_schema(85    schema: typing.Union[pa.DataFrameSchema, pa.SeriesSchema],86    data: st.DataObject,87):88    """Test pandera datatypes against modin data containers."""89    data_container_cls = {90        pa.DataFrameSchema: mpd.DataFrame,91        pa.SeriesSchema: mpd.Series,92        pa.Column: mpd.DataFrame,93    }[type(schema)]94    sample = data.draw(schema.strategy(size=3))95    assert isinstance(schema(data_container_cls(sample)), data_container_cls)96@pytest.mark.parametrize("dtype_cls", TEST_DTYPES_ON_MODIN)97@pytest.mark.parametrize("coerce", [True, False])98@hypothesis.given(st.data())99def test_dataframe_schema_dtypes(100    dtype_cls: pandas_engine.DataType,101    coerce: bool,102    data: st.DataObject,103):104    """105    Test that all supported modin data types work as expected for dataframes.106    """107    dtype = pandas_engine.Engine.dtype(dtype_cls)108    schema = pa.DataFrameSchema({"column": pa.Column(dtype)}, coerce=coerce)109    with pytest.warns(110        UserWarning, match="Distributing .+ object. This may take some time."111    ):112        _test_datatype_with_schema(schema, data)113@pytest.mark.parametrize("dtype_cls", TEST_DTYPES_ON_MODIN)114@pytest.mark.parametrize("coerce", [True, False])115@pytest.mark.parametrize("schema_cls", [pa.SeriesSchema, pa.Column])116@hypothesis.given(st.data())117def test_field_schema_dtypes(118    dtype_cls: pandas_engine.DataType,119    coerce: bool,120    schema_cls,121    data: st.DataObject,122):123    """124    Test that all supported modin data types work as expected for series.125    """126    schema = schema_cls(dtype_cls, name="field", coerce=coerce)127    _test_datatype_with_schema(schema, data)128@pytest.mark.parametrize(129    "dtype",130    [131        int,132        float,133        bool,134        str,135        pandas_engine.DateTime,136    ],137)138@pytest.mark.parametrize("coerce", [True, False])139@pytest.mark.parametrize("schema_cls", [pa.Index, pa.MultiIndex])140@hypothesis.given(st.data())141def test_index_dtypes(...

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