Test code:


# TODO(https://github.com/googleapis/python-db-dtypes-pandas/issues/78): Add
# compliance tests for reduction operations.

# https://github.com/pandas-dev/pandas/blob/5b2fb093f6abd6f5022fe5459af8327c216c5808/pandas/conftest.py#L1030
_all_numeric_reductions = [
    "sum",
    "max",
    "min",
    "mean",
    "prod",
    "std",
    "var",
    "median",
    "kurt",
    "skew",
]


@pytest.fixture(params=_all_numeric_reductions)
def all_numeric_reductions(request):
    """
    Fixture for numeric reduction names.
    """
    return request.param


_all_boolean_reductions = ["all", "any"]


@pytest.fixture(params=_all_boolean_reductions)
def all_boolean_reductions(request):
    """
    Fixture for boolean reduction names.
    """
    return request.param

class TestNumericReduce(base.BaseNumericReduceTests):
    pass


class TestBooleanReduce(base.BaseBooleanReduceTests):
    pass

test output

============================= test session starts ==============================
platform darwin -- Python 3.9.5, pytest-6.2.5, py-1.10.0, pluggy-0.13.1
rootdir: /Users/swast/src/github.com/googleapis/python-db-dtypes-pandas
plugins: cov-2.12.1, asyncio-0.15.1, anyio-3.3.0, requests-mock-1.9.3
collected 160 items

tests/compliance/date/test_date_compliance.py .......................... [ 16%]
........................................................................ [ 61%]
...xx.................................FFFFFFFFFFFFFFFFFFFFFFFF           [100%]

=================================== FAILURES ===================================
________________ TestNumericReduce.test_reduce_series[sum-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1199f6cd0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'sum', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11077: in sum
    return NDFrame.sum(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10797: in sum
    return self._min_count_stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10779: in _min_count_stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'sum', skipna = True, kwargs = {'min_count': 0}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'sum'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[sum-False] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1199c4f10>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'sum', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11077: in sum
    return NDFrame.sum(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10797: in sum
    return self._min_count_stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10779: in _min_count_stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'sum', skipna = False, kwargs = {'min_count': 0}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'sum'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
________________ TestNumericReduce.test_reduce_series[max-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x119a060a0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'max', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
_______________ TestNumericReduce.test_reduce_series[max-False] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1a2229a00>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'max', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
________________ TestNumericReduce.test_reduce_series[min-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x119e18490>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'min', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
_______________ TestNumericReduce.test_reduce_series[min-False] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1a23887c0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'min', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
_______________ TestNumericReduce.test_reduce_series[mean-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1a22fe970>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'mean', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11127: in mean
    return NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10697: in mean
    return self._stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10649: in _stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'mean', skipna = True, kwargs = {}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'mean'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[mean-False] _______________

self = <test_date_compliance.TestNumericReduce object at 0x1199c4790>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'mean', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11127: in mean
    return NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10697: in mean
    return self._stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10649: in _stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'mean', skipna = False, kwargs = {}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'mean'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[prod-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x119b080a0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'prod', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11102: in prod
    return NDFrame.prod(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10810: in prod
    return self._min_count_stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10779: in _min_count_stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'prod', skipna = True, kwargs = {'min_count': 0}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'prod'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[prod-False] _______________

self = <test_date_compliance.TestNumericReduce object at 0x1a22a4c70>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'prod', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11102: in prod
    return NDFrame.prod(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10810: in prod
    return self._min_count_stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10779: in _min_count_stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'prod', skipna = False, kwargs = {'min_count': 0}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'prod'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
________________ TestNumericReduce.test_reduce_series[std-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1a229a640>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'std', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10997: in std
    return NDFrame.std(self, axis, skipna, level, ddof, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10600: in std
    return self._stat_function_ddof(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10561: in _stat_function_ddof
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'std', skipna = True, kwargs = {'ddof': 1}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'std'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[std-False] ________________

self = <test_date_compliance.TestNumericReduce object at 0x119757cd0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'std', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10997: in std
    return NDFrame.std(self, axis, skipna, level, ddof, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10600: in std
    return self._stat_function_ddof(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10561: in _stat_function_ddof
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'std', skipna = False, kwargs = {'ddof': 1}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'std'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
________________ TestNumericReduce.test_reduce_series[var-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1a238ae50>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'var', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10973: in var
    return NDFrame.var(self, axis, skipna, level, ddof, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10587: in var
    return self._stat_function_ddof(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10561: in _stat_function_ddof
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'var', skipna = True, kwargs = {'ddof': 1}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'var'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[var-False] ________________

self = <test_date_compliance.TestNumericReduce object at 0x119cfc820>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'var', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10973: in var
    return NDFrame.var(self, axis, skipna, level, ddof, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10587: in var
    return self._stat_function_ddof(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10561: in _stat_function_ddof
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'var', skipna = False, kwargs = {'ddof': 1}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'var'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
______________ TestNumericReduce.test_reduce_series[median-True] _______________

self = <test_date_compliance.TestNumericReduce object at 0x1a21b2cd0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'median', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
______________ TestNumericReduce.test_reduce_series[median-False] ______________

self = <test_date_compliance.TestNumericReduce object at 0x1a26a3dc0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'median', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
_______________ TestNumericReduce.test_reduce_series[kurt-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x119f21340>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'kurt', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11174: in kurt
    return NDFrame.kurt(self, axis, skipna, level, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10733: in kurt
    return self._stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10649: in _stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'kurt', skipna = True, kwargs = {}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'kurt'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[kurt-False] _______________

self = <test_date_compliance.TestNumericReduce object at 0x1199f6310>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'kurt', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11174: in kurt
    return NDFrame.kurt(self, axis, skipna, level, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10733: in kurt
    return self._stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10649: in _stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'kurt', skipna = False, kwargs = {}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'kurt'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[skew-True] ________________

self = <test_date_compliance.TestNumericReduce object at 0x1a22a4e80>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'skew', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11149: in skew
    return NDFrame.skew(self, axis, skipna, level, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10721: in skew
    return self._stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10649: in _stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'skew', skipna = True, kwargs = {}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'skew'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
_______________ TestNumericReduce.test_reduce_series[skew-False] _______________

self = <test_date_compliance.TestNumericReduce object at 0x1a21b26a0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_numeric_reductions = 'skew', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_numeric_reductions, skipna):
        op_name = all_numeric_reductions
        s = pd.Series(data)
    
        # min/max with empty produce numpy warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
>           self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:17: in check_reduce
    result = getattr(s, op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:11149: in skew
    return NDFrame.skew(self, axis, skipna, level, numeric_only, **kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10721: in skew
    return self._stat_function(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:10649: in _stat_function
    return self._reduce(
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/series.py:4459: in _reduce
    return delegate._reduce(name, skipna=skipna, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
name = 'skew', skipna = False, kwargs = {}, meth = None

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.
    
        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.
    
        Returns
        -------
        scalar
    
        Raises
        ------
        TypeError : subclass does not define reductions
        """
        meth = getattr(self, name, None)
        if meth is None:
>           raise TypeError(
                f"'{type(self).__name__}' with dtype {self.dtype} "
                f"does not support reduction '{name}'"
            )
E           TypeError: 'DateArray' with dtype dbdate does not support reduction 'skew'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:1369: TypeError
________________ TestBooleanReduce.test_reduce_series[all-True] ________________

self = <test_date_compliance.TestBooleanReduce object at 0x1a2475af0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_boolean_reductions = 'all', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_boolean_reductions, skipna):
        op_name = all_boolean_reductions
        s = pd.Series(data)
>       self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
_______________ TestBooleanReduce.test_reduce_series[all-False] ________________

self = <test_date_compliance.TestBooleanReduce object at 0x1a23447f0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_boolean_reductions = 'all', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_boolean_reductions, skipna):
        op_name = all_boolean_reductions
        s = pd.Series(data)
>       self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
________________ TestBooleanReduce.test_reduce_series[any-True] ________________

self = <test_date_compliance.TestBooleanReduce object at 0x119b144f0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_boolean_reductions = 'any', skipna = True

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_boolean_reductions, skipna):
        op_name = all_boolean_reductions
        s = pd.Series(data)
>       self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
_______________ TestBooleanReduce.test_reduce_series[any-False] ________________

self = <test_date_compliance.TestBooleanReduce object at 0x1a203dcd0>
data = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
all_boolean_reductions = 'any', skipna = False

    @pytest.mark.parametrize("skipna", [True, False])
    def test_reduce_series(self, data, all_boolean_reductions, skipna):
        op_name = all_boolean_reductions
        s = pd.Series(data)
>       self.check_reduce(s, op_name, skipna)

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/tests/extension/base/reduce.py:18: in check_reduce
    expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/generic.py:5920: in astype
    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419: in astype
    return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304: in apply
    applied = getattr(b, f)(**kwargs)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580: in astype
    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292: in astype_array_safe
    new_values = astype_array(values, dtype, copy=copy)
/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1234: in astype_array
    values = values.astype(dtype, copy=copy)
db_dtypes/__init__.py:284: in astype
    return super().astype(dtype, copy=copy)
db_dtypes/core.py:85: in astype
    return super().astype(dtype, copy=copy)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <DateArray>
[ datetime.date(1900, 1, 1),  datetime.date(1902, 1, 2),
  datetime.date(1904, 1, 3),  datetime.date(1906,...2, 19), datetime.date(2094, 2, 19),
 datetime.date(2096, 2, 20), datetime.date(2098, 2, 20)]
Length: 100, dtype: dbdate
dtype = dtype('float64'), copy = True

    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
        """
        Cast to a NumPy array or ExtensionArray with 'dtype'.
    
        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.
    
        Returns
        -------
        array : np.ndarray or ExtensionArray
            An ExtensionArray if dtype is ExtensionDtype,
            Otherwise a NumPy ndarray with 'dtype' for its dtype.
        """
    
        dtype = pandas_dtype(dtype)
        if is_dtype_equal(dtype, self.dtype):
            if not copy:
                return self
            else:
                return self.copy()
    
        if isinstance(dtype, ExtensionDtype):
            cls = dtype.construct_array_type()
            return cls._from_sequence(self, dtype=dtype, copy=copy)
    
>       return np.array(self, dtype=dtype, copy=copy)
E       TypeError: float() argument must be a string or a number, not 'datetime.date'

/usr/local/Caskroom/miniconda/base/envs/dev-3.9/lib/python3.9/site-packages/pandas/core/arrays/base.py:584: TypeError
=========================== short test summary info ============================
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[sum-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[sum-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[max-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[max-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[min-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[min-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[mean-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[mean-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[prod-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[prod-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[std-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[std-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[var-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[var-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[median-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[median-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[kurt-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[kurt-False]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[skew-True]
FAILED tests/compliance/date/test_date_compliance.py::TestNumericReduce::test_reduce_series[skew-False]
FAILED tests/compliance/date/test_date_compliance.py::TestBooleanReduce::test_reduce_series[all-True]
FAILED tests/compliance/date/test_date_compliance.py::TestBooleanReduce::test_reduce_series[all-False]
FAILED tests/compliance/date/test_date_compliance.py::TestBooleanReduce::test_reduce_series[any-True]
FAILED tests/compliance/date/test_date_compliance.py::TestBooleanReduce::test_reduce_series[any-False]
================== 24 failed, 134 passed, 2 xfailed in 6.33s ===================

support reduction operations #78

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