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Showing content from https://github.com/pandas-dev/pandas/issues/28385 below:

Nullable Int64 column changes type after some (cumsum) operations · Issue #28385 · pandas-dev/pandas · GitHub

import pandas as pd

s1 = pd.Series([1, 2, 3, 4], dtype='Int64')
s2 = pd.Series([1, 2, float('nan'), 3, 4], dtype='Int64')

cs1 = s1.cumsum()
cs2 = s1.cumsum()

print(str(s1.dtype), str(cs1.dtype))
print(str(s2.dtype), str(cs2.dtype))

Int64 object
Int64 object

After an operation like a cumulative sum on a column/series with an Int64 dtype, the dtype of the result is downcast to object. The contents (integers and nans) of the result still qualify for it to have an Int64 dtype.

cummax, cummin and cumprod have the same behaviour.

commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.13-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.utf8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.1.0
Cython : 0.29.13
pytest : 5.1.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : 0.12.3
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
import pandas as pd

s = pd.Series([1, 2, 3, 4], dtype='Int64')
cs = s.cumsum()

print(str(s.dtype))
print(str(cs.dtype))
import pandas as pd
​

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