BUG: Enabling Copy on Write with DataFrame.replace Raises Exception with np.nan as replacement value

[achapkowski]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np
pd.options.mode.copy_on_write = True
df = pd.DataFrame(data={'A' : [1, 2],'B' : ['b', 'i like pandas'],})
df["Name"] = "I Have a Name"
df['Name2'] = 'i like pandas'
df.replace("i like pandas", "i like arrow")
df.replace(np.nan, None) # works
replace_mappings = {
    pd.NA: None,
    pd.NaT: None,
    np.nan: None # Issue Type
}
df.replace(replace_mappings) # fails

Issue Description

When using DataFrame.replace and trying to replace np.nan value when in a dictionary, it raises an error. A standard replace(np.nan, None) will work though.

The error raised:

builtins.ValueError: <weakref at 0x0000023B896B2840; to 'ExtensionBlock' at 0x0000023B896A26F0> is not in list

Expected Behavior

The expected behavior is that the replace to not error out.

Installed Versions

This is reproducible in the v3 development release as of 10/22/25 and with the current public release. I tested in multiple versions

[TeeVee99]

take

[jorisvandenbossche]

@achapkowski thanks for the report. I can't directly reproduce this, though, using Linux (Ubuntu) and in a conda env, both with released 2.3.3 or the main branch:

>>> import pandas as pd
>>> import numpy as np
>>> pd.options.mode.copy_on_write = True
>>> df = pd.DataFrame(data={'A' : [1, 2],'B' : ['b', 'i like pandas'],})
>>> df["Name"] = "I Have a Name"
>>> df['Name2'] = 'i like pandas'
>>> df.replace("i like pandas", "i like arrow")
   A             B           Name         Name2
0  1             b  I Have a Name  i like arrow
1  2  i like arrow  I Have a Name  i like arrow
>>> df.replace(np.nan, None) # works
   A              B           Name          Name2
0  1              b  I Have a Name  i like pandas
1  2  i like pandas  I Have a Name  i like pandas
>>> replace_mappings = {
...     pd.NA: None,
...     pd.NaT: None,
...     np.nan: None # Issue Type
... }
>>> df.replace(replace_mappings) # fails
   A              B           Name          Name2
0  1              b  I Have a Name  i like pandas
1  2  i like pandas  I Have a Name  i like pandas

>>> pd.__version__
'3.0.0.dev0+2236.g3c4586fde9'

[jorisvandenbossche]

Can you give some more details of the environment you are testing in? (eg which version of Python, etc) Can show the output of pd.show_versions()

[achapkowski]

@jorisvandenbossche sure thing.

First Reproducible Environment:

INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.11.11
python-bits           : 64
OS                    : Windows
OS-release            : 10
Version               : 10.0.22631
machine               : AMD64
processor             : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
byteorder             : little
LC_ALL                : None
LANG                  : None
LOCALE                : English_United States.1252

pandas                : 2.2.3
numpy                 : 1.26.4
pytz                  : 2025.1
dateutil              : 2.9.0.post0
pip                   : 23.3.2
Cython                : None
sphinx                : None
IPython               : 8.32.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : 4.12.3
blosc                 : None
bottleneck            : 1.4.2
dataframe-api-compat  : None
fastparquet           : None
fsspec                : 2024.12.0
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : 3.1.5
lxml.etree            : 5.3.0
matplotlib            : 3.9.4
numba                 : None
numexpr               : 2.10.2
odfpy                 : None
openpyxl              : 3.1.5
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : 16.1.0
pyreadstat            : None
pytest                : 7.4.3
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : 1.14.1
sqlalchemy            : 2.0.32
tables                : None
tabulate              : None
xarray                : 2024.11.0
xlrd                  : 2.0.1
xlsxwriter            : None
zstandard             : None
tzdata                : None
qtpy                  : None
pyqt5                 : None

Second Reproducible Environment

INSTALLED VERSIONS
------------------
commit                : 9c8bc3e55188c8aff37207a74f1dd144980b8874
python                : 3.13.7
python-bits           : 64
OS                    : Windows
OS-release            : 11
Version               : 10.0.22631
machine               : AMD64
processor             : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
byteorder             : little
LC_ALL                : None
LANG                  : None
LOCALE                : English_United States.1252

pandas                : 2.3.3
numpy                 : 2.2.0
pytz                  : 2025.1
dateutil              : 2.9.0.post0
pip                   : 25.2
Cython                : None
sphinx                : None
IPython               : None
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : None
blosc                 : None
bottleneck            : 1.4.2
dataframe-api-compat  : None
fastparquet           : None
fsspec                : 2025.5.1
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : None
lxml.etree            : None
matplotlib            : None
numba                 : None
numexpr               : 2.10.2
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : None
pyreadstat            : None
pytest                : None
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : None
sqlalchemy            : None
tables                : None
tabulate              : None
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : None
tzdata                : 2025.2
qtpy                  : None
pyqt5                 : None

I cleared out the v3 environment, and I'll get that one up a bit later.

I bet the issue is numpy possibly.

[jorisvandenbossche]

Thanks, I can reproduce it now with the first environment (just installing that version of python 3.11 + numpy 1.26 + pandas 2.2), but can't reproduce it with the second environment.
Also, if just updating pandas from 2.2 to 2.3 in the first environment, it no longer raises the error ..

I also can't reproduce it in a dev environment with numpy 1.26. Indicating it might already be fixed? But if you can test again with pandas main / nightly, and share that environment if it still reproduces there, that would be great.