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[x] I have checked that this issue has not already been reported.
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[x] I have confirmed this bug exists on the latest version of pandas. Version 1.2.3
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[ ] (optional) I have confirmed this bug exists on the master branch of pandas.
Code Sample, a copy-pastable example
import pandas as pd
df = pd.DataFrame([{"A": 1, "B": 2}, {"A": 3}]).astype({"B": "Int64"})
print(df.dtypes, end="\n\n")
print(df.select_dtypes("Int64")) # Expect to select only column B
Actual output
A int64
B Int64
dtype: object
A B
0 1 2
1 3 <NA>
Problem description
Calling select_dtypes("Int64") (Int with capital "I") selects both int64 and Int64 columns.
Also applies to other int types, e.g. int32 & Int32.
Similar behavior is also obtained with df.select_dtypes(pd.Int64Dtype)
Expected Output
A int64
B Int64
dtype: object
B
0 2
1 <NA>
Output of pd.show_versions()
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : French_Canada.1252
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2