DataFrame.replace changes dtype of columns not containing any value to be replaced. #30512
Description
Code Sample, a copy-pastable example if possible
import pandas as pd import numpy as np data = pd.DataFrame({'A':np.nan,'B':1.096, 'C':1}, index=[0]) data.replace(to_replace={np.nan:None}, inplace=True)Problem description
Checking data.dtypes before and after the call to replace shows that the datatype of column B changed from float to object whereas that of C stayed at int. If I remove column A from the original data that does not happen.
When using replace I expected columns without any matching value not to be touched / changed.
Expected Output
Dtype of column 'B' should remain 'float64'.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8