BUG: Compare type int64 with Int64 fails

[ChiQiao]

• 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

pd.api.types.pandas_dtype("int64") == "Int64"

Problem description

The code above fails (TypeError: data type 'Int64' not understood) while the following cases pass:

pd.api.types.pandas_dtype("int64") == "int64"
pd.api.types.pandas_dtype("Int64") == "Int64"
pd.api.types.pandas_dtype("Int64") == "int64"

Expected Output

False

Output of pd.show_versions()

INSTALLED VERSIONS

commit : c7f7443
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None

pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0

[jbrockmendel]

This is an issue with np.dtpye.__eq__, should be fixed by numpy/numpy#19228

[simonjayhawkins]

can't reproduce on pandas 1.2.5 onwards.

>>> pd.__version__
'1.2.5'
>>> pd.api.types.pandas_dtype("int64") == "Int64"
False

Expected Output

True

numpy int dtypes can't hold missing values, so False seem more appropriate.

[ChiQiao]

Thanks @jbrockmendel and @simonjayhawkins for the input

can't reproduce on pandas 1.2.5 onwards.

Since it may be a bug from numpy, I wonder which numpy version you were using?
Also, I wonder if you were able to reproduce the bug given the environment I posted (pandas 1.3.1, numpy 1.20.3)?

numpy int dtypes can't hold missing values, so False seem more appropriate.

Yes, that was a typo on my side. I've updated the description.

[kwhkim]

In my case, no bug found. (But I can not understand how it can be a bug from numpy when numpy does not support integer NAs. Isn't it the reason that they named integer NA as pd.NA?)

>>> pd.api.types.pandas_dtype("int64") == "Int64"
False
>>> pd.__version__
'1.3.1'
>>> np.__version__
'1.21.1'

[mzeitlin11]

See discussion in #43993 - this is fixed for newer versions of numpy, contributions welcome to add a regression test!

[paulvdbles]

take

[ata-turhan]

take

[ata-turhan]

Can you give me an example for how to add a regression test for this issue? Thank you :)

[mzeitlin11]

Can you give me an example for how to add a regression test for this issue? Thank you :)

I'd recommend looking at closed good first issues about testing (for example from https://github.com/pandas-dev/pandas/issues?q=is%3Aissue+label%3A%22good+first+issue%22+is%3Aclosed+label%3A%22Needs+Tests%22). If you look at some pull requests which close the issue, those should be good examples

[simonjayhawkins]

closing as tests added in #44840