BUG: np.nan_to_num change pandas DataFrame in place when copy = True

[lil-blue-curve]

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 numpy as np
import pandas as pd

x = pd.DataFrame([np.nan])
np.nan_to_num(x, copy=True)
print(x)

Issue Description

Result x is changed to 0.0 even when nan_to_num is called with copy=True.

This issue is originally reported to numpy here. Looks like pandas ignores copy=True NumPy passes?

Expected Behavior

x should not be changed when copy=True.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.11.8
python-bits : 64
OS : Linux
OS-release : 5.10.0-smp-1100.466.602.1
Version : #1 [v5.10.0-1100.466.602.1] SMP @1708156698
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 2.1.1
pytz : 2024.1
dateutil : 2.8.1
pip : None
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : 5.3.0
matplotlib : None
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlsxwriter : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None

[asishm]

Thanks, this seems to be related to #57739

[chaoyihu]

This issue no longer exists on the main branch as of 2a10e04. The script returns:

    0
0 NaN

[asishm]

This issue no longer exists on the main branch as of 2a10e04. The script returns:

    0
0 NaN

I saw in one of your edits that you have numpy 1.26.4
This issue only should only occur with numpy 2.0+

[chaoyihu]

I saw in one of your edits that you have numpy 1.26.4 This issue only should only occur with numpy 2.0+

I see. Sorry for the noise.