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How to use test_dtype_coercion method in pandera

Best Python code snippet using pandera_python

test_deprecations.py

Source:test_deprecations.py

...455        np.integer, np.unsignedinteger, np.signedinteger,456        # character is a deprecated S1 special case:457        np.character,458    ]459    def test_dtype_coercion(self):460        for scalar_type in self.deprecated_types:461            self.assert_deprecated(np.dtype, args=(scalar_type,))462    def test_array_construction(self):463        for scalar_type in self.deprecated_types:464            self.assert_deprecated(np.array, args=([], scalar_type,))465    def test_not_deprecated(self):466        # All specific types are not deprecated:467        for group in np.sctypes.values():468            for scalar_type in group:469                self.assert_not_deprecated(np.dtype, args=(scalar_type,))470        for scalar_type in [type, dict, list, tuple]:471            # Typical python types are coerced to object currently:472            self.assert_not_deprecated(np.dtype, args=(scalar_type,))473class BuiltInRoundComplexDType(_DeprecationTestCase):...

test_pyfftw_dask_interface.py

Source:test_pyfftw_dask_interface.py

...359                    s += i * 2360                    i *= i361                self.validate(dtype_tuple[1],362                        test_shape, dtype, s, kwargs)363    def test_dtype_coercion(self):364        # Make sure we input a dtype that needs to be coerced365        if functions[self.func] == 'r2c':366            dtype_tuple = self.io_dtypes['complex']367        else:368            dtype_tuple = self.io_dtypes['r2c']369        for dtype in dtype_tuple[0]:370            for test_shape, s, kwargs in self.test_data:371                s = None372                self.validate(dtype_tuple[1],373                        test_shape, dtype, s, kwargs)374    def test_input_maintained(self):375        '''Test to make sure the input is maintained by default.376        '''377        dtype_tuple = self.io_dtypes[functions[self.func]]...

test_schemas_on_modin.py

Source:test_schemas_on_modin.py

...240    schema(mpd.DataFrame({"another_field": [1, 2, 3]}))241@pytest.mark.parametrize("from_dtype", [str])242@pytest.mark.parametrize("to_dtype", [float, int, str, bool])243@hypothesis.given(st.data())244def test_dtype_coercion(from_dtype, to_dtype, data):245    """Test the datatype coercion provides informative errors."""246    from_schema = pa.DataFrameSchema({"field": pa.Column(from_dtype)})247    to_schema = pa.DataFrameSchema({"field": pa.Column(to_dtype, coerce=True)})248    pd_sample = data.draw(from_schema.strategy(size=3))249    sample = mpd.DataFrame(pd_sample)250    if from_dtype is to_dtype:251        assert isinstance(to_schema(sample), mpd.DataFrame)252        return253    if from_dtype is str and to_dtype in {int, float}:254        try:255            result = to_schema(sample)256            assert result["field"].dtype == to_dtype257        except pa.errors.SchemaError as err:258            for x in err.failure_cases.failure_case:...

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