Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
s1 = pd.Series([pd.Timestamp(1000223), np.nan], dtype = object)
a1 = s1.replace("abcdef", pd.NaT, regex=True)
a2 = s1.replace("abcdef", pd.NaT, regex=False)
# Why?
assert a1[1] is pd.NaT # Here np.nan is implicitly converted to pd.NaT
assert a2[1] is np.nan # Here np.nan is NOT implicitly converted
Issue Description
np.nan
are implicitly converted to pd.NaT
when all doing:
Series.replace
with regex = True flag- all values are either
pd.Timestamp
ornp.nan
Expected Behavior
This behaviour is inconsistent. The documentation of Series.replace
suggests that the function only replaces strings matching regex with the to_replace value. I did not expect for it to implicitly coerce values.
Expected behaviour is like the a2
case in the example: np.nan
keeps being np.nan
and does not get converted.
Where it happens
In L763-764 blocks.py, the block
variable in L763 has np.nan
. But after running block.convert(...)
, the result has pd.NaT
. I believe the coercion happens specifically in L490 blocks.py:
attempt to coerce any object types to better types return a copy
of the block (if copy = True) by definition we are not an ObjectBlock
here!
Installed Versions
INSTALLED VERSIONS
commit : 4bfe3d0
python : 3.9.13.final.0
python-bits : 64
OS : Darwin
OS-release : 21.5.0
Version : Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:29 PDT 2022; root:xnu-8020.121.3~4/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.4.2
numpy : 1.22.3
pytz : 2022.1
dateutil : 2.8.2
pip : 22.0.4
setuptools : 62.1.0
Cython : None
pytest : 7.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : 0.8.1
fsspec : 2022.02.0
gcsfs : None
markupsafe : 2.1.1
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 6.0.1
pyreadstat : None
pyxlsb : 1.0.9
s3fs : 0.4.2
scipy : None
snappy : None
sqlalchemy : 1.4.36
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : None
zstandard : None