How to use the pandera.DateTime function in pandera

def test_dataframe_schema():
    schema = DataFrameSchema(
        {
            "a": Column(Int,
                        Check(lambda x: x > 0, element_wise=True)),
            "b": Column(Float,
                        Check(lambda x: 0 <= x <= 10, element_wise=True)),
            "c": Column(String,
                        Check(lambda x: set(x) == {"x", "y", "z"})),
            "d": Column(Bool,
                        Check(lambda x: x.mean() > 0.5)),
            "e": Column(Category,
                        Check(lambda x: set(x) == {"c1", "c2", "c3"})),
            "f": Column(Object,
                        Check(lambda x: x.isin([(1,), (2,), (3,)]))),
            "g": Column(DateTime,
                        Check(lambda x: x >= pd.Timestamp("2015-01-01"),
                              element_wise=True)),
            "i": Column(Timedelta,
                        Check(lambda x: x < pd.Timedelta(10, unit="D"),
                              element_wise=True))
        })
    df = pd.DataFrame(
        {
            "a": [1, 2, 3],
            "b": [1.1, 2.5, 9.9],
            "c": ["z", "y", "x"],
            "d": [True, True, False],
            "e": pd.Series(["c2", "c1", "c3"], dtype="category"),
            "f": [(3,), (2,), (1,)],
            "g": [pd.Timestamp("2015-02-01"),
                  pd.Timestamp("2015-02-02"),

df = pd.DataFrame({
        "column1": [10.0, 20.0, 30.0],
        "column2": ["2018-01-01", "2018-02-01", "2018-03-01"],
        "column3": [1, 2, None],
        "column4": [1., 1., np.nan],
    })
    # specify `coerce` at the Column level
    schema1 = DataFrameSchema({
        "column1": Column(Int, Check(lambda x: x > 0), coerce=True),
        "column2": Column(DateTime, coerce=True),
        "column3": Column(String, coerce=True, nullable=True),
    })
    # specify `coerce` at the DataFrameSchema level
    schema2 = DataFrameSchema({
        "column1": Column(Int, Check(lambda x: x > 0)),
        "column2": Column(DateTime),
        "column3": Column(String, nullable=True),
    }, coerce=True)

    for schema in [schema1, schema2]:
        result = schema.validate(df)
        assert result.column1.dtype == Int.value
        assert result.column2.dtype == DateTime.value
        for _, x in result.column3.iteritems():
            assert pd.isna(x) or isinstance(x, str)

        # make sure that correct error is raised when null values are present
        # in a float column that's coerced to an int
        schema = DataFrameSchema({"column4": Column(Int, coerce=True)})
        with pytest.raises(ValueError):
            schema.validate(df)

def test_multi_index_columns():
    schema = DataFrameSchema({
        ("zero", "foo"): Column(Float, Check(lambda s: (s > 0) & (s < 1))),
        ("zero", "bar"): Column(
            String, Check(lambda s: s.isin(["a", "b", "c", "d"]))),
        ("one", "foo"): Column(Int, Check(lambda s: (s > 0) & (s < 10))),
        ("one", "bar"): Column(
            DateTime, Check(lambda s: s == pd.datetime(2019, 1, 1)))
    })
    validated_df = schema.validate(
        pd.DataFrame({
            ("zero", "foo"): [0.1, 0.2, 0.7, 0.3],
            ("zero", "bar"): ["a", "b", "c", "d"],
            ("one", "foo"): [1, 6, 4, 7],
            ("one", "bar"): pd.to_datetime(["2019/01/01"] * 4)
        })
    )
    assert isinstance(validated_df, pd.DataFrame)

def test_dataframe_schema():
    schema = DataFrameSchema(
        {
            "a": Column(Int,
                        Check(lambda x: x > 0, element_wise=True)),
            "b": Column(Float,
                        Check(lambda x: 0 <= x <= 10, element_wise=True)),
            "c": Column(String,
                        Check(lambda x: set(x) == {"x", "y", "z"})),
            "d": Column(Bool,
                        Check(lambda x: x.mean() > 0.5)),
            "e": Column(Category,
                        Check(lambda x: set(x) == {"c1", "c2", "c3"})),
            "f": Column(Object,
                        Check(lambda x: x.isin([(1,), (2,), (3,)]))),
            "g": Column(DateTime,
                        Check(lambda x: x >= pd.Timestamp("2015-01-01"),
                              element_wise=True)),
            "i": Column(Timedelta,
                        Check(lambda x: x < pd.Timedelta(10, unit="D"),
                              element_wise=True))
        })
    df = pd.DataFrame({
        "a": [1, 2, 3],
        "b": [1.1, 2.5, 9.9],
        "c": ["z", "y", "x"],
        "d": [True, True, False],
        "e": pd.Series(["c2", "c1", "c3"], dtype="category"),
        "f": [(3,), (2,), (1,)],
        "g": [pd.Timestamp("2015-02-01"),
              pd.Timestamp("2015-02-02"),
              pd.Timestamp("2015-02-03")],

def test_check_function_decorators():
    in_schema = DataFrameSchema(
        {
            "a": Column(Int, [
                Check(lambda x: x >= 1, element_wise=True),
                Check(lambda s: s.mean() > 0)]),
            "b": Column(String,
                        Check(lambda x: x in ["x", "y", "z"],
                              element_wise=True)),
            "c": Column(DateTime,
                        Check(lambda x: pd.Timestamp("2018-01-01") <= x,
                              element_wise=True)),
            "d": Column(Float,
                        Check(lambda x: np.isnan(x) or x < 3,
                              element_wise=True),
                        nullable=True)
        },
        transformer=lambda df: df.assign(e="foo")
    )
    out_schema = DataFrameSchema(
        {
            "e": Column(String,
                        Check(lambda s: s == "foo")),
            "f": Column(String,
                        Check(lambda x: x in ["a", "b"], element_wise=True))
        })