How to use get_registered_dtypes method in pandera

Best Python code snippet using pandera_python

test_numpy_engine.py

Source:test_numpy_engine.py

2import numpy as np3import pytest4from pandera.engines import numpy_engine5@pytest.mark.parametrize(6    "data_type", list(numpy_engine.Engine.get_registered_dtypes())7)8def test_numpy_data_type(data_type):9    """Test base numpy engine DataType."""10    numpy_engine.Engine.dtype(data_type)11    numpy_engine.Engine.dtype(data_type.type)12    numpy_engine.Engine.dtype(str(data_type.type))13    with pytest.warns(UserWarning):14        np_dtype = numpy_engine.DataType(data_type.type)15    with pytest.warns(UserWarning):16        np_dtype_from_str = numpy_engine.DataType(str(data_type.type))17    assert np_dtype == np_dtype_from_str18@pytest.mark.parametrize("data_type", ["foo", "bar", 1, 2, 3.14, np.void])19def test_numpy_engine_dtype_exceptions(data_type):20    """Test invalid inputs to numpy data-types."""...

test_pandas_engine.py

Source:test_pandas_engine.py

...3import pytest4from pandera.engines import pandas_engine5from pandera.errors import ParserError6@pytest.mark.parametrize(7    "data_type", list(pandas_engine.Engine.get_registered_dtypes())8)9def test_pandas_data_type(data_type):10    """Test numpy engine DataType base class."""11    if data_type.type is None:12        # don't test data types that require parameters e.g. Category13        return14    pandas_engine.Engine.dtype(data_type)15    pandas_engine.Engine.dtype(data_type.type)16    pandas_engine.Engine.dtype(str(data_type.type))17    with pytest.warns(UserWarning):18        pd_dtype = pandas_engine.DataType(data_type.type)19    with pytest.warns(UserWarning):20        pd_dtype_from_str = pandas_engine.DataType(str(data_type.type))21    assert pd_dtype == pd_dtype_from_str22    assert not pd_dtype.check("foo")23@pytest.mark.parametrize(24    "data_type", list(pandas_engine.Engine.get_registered_dtypes())25)26def test_pandas_data_type_coerce(data_type):27    """28    Test that pandas data type coercion will raise a ParserError. on failure.29    """30    if data_type.type is None:31        # don't test data types that require parameters e.g. Category32        return33    try:34        data_type().try_coerce(pd.Series(["1", "2", "a"]))35    except ParserError as exc:...

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