BUG: `pd.core.dtypes.common.is_extension_array_dtype` wrong output for some dtypes

[siboehm]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

from pandas.core.dtypes.common import is_extension_array_dtype
from pandas.core.base import ExtensionArray

dt = DatetimeArray(np.array([], dtype='datetime64[ns]'))
assert isinstance(dt, ExtensionArray) # passes
assert is_extension_array_dtype(dt) # fails
assert is_extension_array_dtype(dt.dtype) # fails

inter = pd.Interval(0, 5)
assert com.is_extension_array_dtype(inter) # fails

Similar problems for Period and timedelta64[ns].

Problem description

Some of it (example: DatetimeArray) is due to this commit: be68850#diff-e279f0dcc8af317412f2a0db83d50c2ffedcd0743af7da8545ff272ee6ebe2f3R1529

Where DatetimeArray implements ExtensionArray, but datetime64[ns] is a np dtype and doesn't (yet) implement ExtensionDtype.

Expected Output

Function should return true for all dtypes whose Array implements ExtensionArray.

I need this for #40450.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : a75152784c17a73627a414319f6c89324cfa2840
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-44-generic
Version : #50-Ubuntu SMP Tue Feb 9 06:29:41 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.0.dev0+1084.ga75152784c.dirty
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20201009
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.7.0
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0

[jbrockmendel]

In all the listed cases, the behavior is correct.

DatetimeArray[dt64ns] is an ExtensionArray, but its dtype is a numpy dtype, not an ExtensionDtype. Same for TimedeltaArray.

IntervalDtype and IntervalArray would both give True for is_extension_array_dtype, but pd.Interval is not a dtype, nor does it have a dtype.

[jbrockmendel]

in general I advocate just checking for isinstance(arr, ExtensionArray) or isinstance(dtype, ExtensionDtype) where relevant.

[siboehm]

Agree about pd.Interval, but for DatetimeArray and Timedelta, the docstring of is_extension_array_dtype suggests otherwise:

pandas/pandas/core/dtypes/common.py

Lines 1478 to 1488 in bfe734f

	Notes
	-----
	This checks whether an object implements the pandas extension
	array interface. In pandas, this includes:
	* Categorical
	* Sparse
	* Interval
	* Period
	* DatetimeArray
	* TimedeltaArray