How to use the pandera.SeriesSchema function in pandera

def test_series_schema():
    int_schema = SeriesSchema(
        Int, Check(lambda x: 0 <= x <= 100, element_wise=True))
    assert isinstance(int_schema.validate(
        pd.Series([0, 30, 50, 100])), pd.Series)

    str_schema = SeriesSchema(
        String, Check(lambda s: s.isin(["foo", "bar", "baz"])),
        nullable=True, coerce=True)
    assert isinstance(str_schema.validate(
        pd.Series(["foo", "bar", "baz", None])), pd.Series)
    assert isinstance(str_schema.validate(
        pd.Series(["foo", "bar", "baz", np.nan])), pd.Series)

    # error cases
    for data in [-1, 101, 50.1, "foo"]:
        with pytest.raises(errors.SchemaError):
            int_schema.validate(pd.Series([data]))

def test_series_schema():
    schema = SeriesSchema(
        Int, Check(lambda x: 0 <= x <= 100, element_wise=True))
    validated_series = schema.validate(pd.Series([0, 30, 50, 100]))
    assert isinstance(validated_series, pd.Series)

    # error cases
    for data in [-1, 101, 50.1, "foo"]:
        with pytest.raises(errors.SchemaError):
            schema.validate(pd.Series([data]))

    for data in [-1, {"a": 1}, -1.0]:
        with pytest.raises(TypeError):
            schema.validate(TypeError)

    non_duplicate_schema = SeriesSchema(
        Int, allow_duplicates=False)
    with pytest.raises(errors.SchemaError):
        non_duplicate_schema.validate(pd.Series([0, 1, 2, 3, 4, 1]))

    # when series name doesn't match schema
    named_schema = SeriesSchema(Int, name="my_series")
    with pytest.raises(
            errors.SchemaError,
            match=r"^Expected .+ to have name"):
        named_schema.validate(pd.Series(range(5), name="your_series"))

    # when series floats are declared to be integer
    with pytest.raises(
            errors.SchemaError,
            match=r"^after dropping null values, expected values in series"):
        SeriesSchema(Int, nullable=True).validate(

def test_no_dtype_series():
    schema = SeriesSchema(nullable=False)
    validated_series = schema.validate(pd.Series([0, 1, 2, 3, 4, 1]))
    assert isinstance(validated_series, pd.Series)

    schema = SeriesSchema(nullable=True)
    validated_series = schema.validate(pd.Series([0, 1, 2, None, 4, 1]))
    assert isinstance(validated_series, pd.Series)

    with pytest.raises(errors.SchemaError):
        schema = SeriesSchema(nullable=False)
        schema.validate(pd.Series([0, 1, 2, None, 4, 1]))

def test_series_schema_multiple_validators():
    schema = SeriesSchema(
        Int, [
            Check(lambda x: 0 <= x <= 50, element_wise=True),
            Check(lambda s: (s == 21).any())])
    validated_series = schema.validate(pd.Series([1, 5, 21, 50]))
    assert isinstance(validated_series, pd.Series)

    # raise error if any of the validators fails
    with pytest.raises(errors.SchemaError):
        schema.validate(pd.Series([1, 5, 20, 50]))

def test_no_dtype_series():
    schema = SeriesSchema(nullable=False)
    validated_series = schema.validate(pd.Series([0, 1, 2, 3, 4, 1]))
    assert isinstance(validated_series, pd.Series)

    schema = SeriesSchema(nullable=True)
    validated_series = schema.validate(pd.Series([0, 1, 2, None, 4, 1]))
    assert isinstance(validated_series, pd.Series)

    with pytest.raises(errors.SchemaError):
        schema = SeriesSchema(nullable=False)
        schema.validate(pd.Series([0, 1, 2, None, 4, 1]))

Int, allow_duplicates=False)
    with pytest.raises(errors.SchemaError):
        non_duplicate_schema.validate(pd.Series([0, 1, 2, 3, 4, 1]))

    # when series name doesn't match schema
    named_schema = SeriesSchema(Int, name="my_series")
    with pytest.raises(
            errors.SchemaError,
            match=r"^Expected .+ to have name"):
        named_schema.validate(pd.Series(range(5), name="your_series"))

    # when series floats are declared to be integer
    with pytest.raises(
            errors.SchemaError,
            match=r"^after dropping null values, expected values in series"):
        SeriesSchema(Int, nullable=True).validate(
            pd.Series([1.1, 2.3, 5.5, np.nan]))

    # when series contains null values when schema is not nullable
    with pytest.raises(
            errors.SchemaError,
            match=r"^non-nullable series .+ contains null values"):
        SeriesSchema(Float, nullable=False).validate(
            pd.Series([1.1, 2.3, 5.5, np.nan]))

    # when series contains null values when schema is not nullable in addition
    # to having the wrong data type
    with pytest.raises(
            errors.SchemaError,
            match=(
                r"^expected series '.+' to have type .+, got .+ and "
                "non-nullable series contains null values")):

def test_series_schema():
    int_schema = SeriesSchema(
        Int, Check(lambda x: 0 <= x <= 100, element_wise=True))
    assert isinstance(int_schema.validate(
        pd.Series([0, 30, 50, 100])), pd.Series)

    str_schema = SeriesSchema(
        String, Check(lambda s: s.isin(["foo", "bar", "baz"])),
        nullable=True, coerce=True)
    assert isinstance(str_schema.validate(
        pd.Series(["foo", "bar", "baz", None])), pd.Series)
    assert isinstance(str_schema.validate(
        pd.Series(["foo", "bar", "baz", np.nan])), pd.Series)

    # error cases
    for data in [-1, 101, 50.1, "foo"]:
        with pytest.raises(errors.SchemaError):
            int_schema.validate(pd.Series([data]))

    for data in [-1, {"a": 1}, -1.0]:
        with pytest.raises(TypeError):
            int_schema.validate(TypeError)

def test_vectorized_checks():
    schema = SeriesSchema(
        Int, Check(
            lambda s: s.value_counts() == 2, element_wise=False))
    validated_series = schema.validate(pd.Series([1, 1, 2, 2, 3, 3]))
    assert isinstance(validated_series, pd.Series)

    # error case
    with pytest.raises(errors.SchemaError):
        schema.validate(pd.Series([1, 2, 3]))