Description
Pandas version checks
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I have checked that this issue has not already been reported.
-
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
>>> import pandas as pd, numpy as np
>>> pd.__version__; np.__version__
'2.2.2'
'2.1.0'
>>> my_series = pd.Series([1.0, 2.0, np.nan, 0.0, 1.0])
>>> my_series
0 1.0
1 2.0
2 NaN
3 0.0
4 1.0
dtype: float64
>>> np.ma.fix_invalid(my_series)
masked_array(data=[1.0, 2.0, --, 0.0, 1.0],
mask=[False, False, True, False, False],
fill_value=1e+20)
>>> my_series
0 1.000000e+00
1 2.000000e+00
2 1.000000e+20
3 0.000000e+00
4 1.000000e+00
dtype: float64Issue Description
Copying the description of: numpy/numpy#27253
numpy.ma.fix_invalid behaves differently between NumPy 2.1.0 and NumPy 2.0.0. Specifically, when passing a pandas Series containing a numpy.nan value, numpy.ma.fix_invalid now makes changes in-place, even if the copy argument is set to its default value of True. This issue occurs only with pandas Series, not with NumPy arrays, for example.
Expected Behavior
>>> pd.__version__; np.__version__
'2.2.2'
'2.0.0'
>>>
>>> my_series = pd.Series([1.0, 2.0, np.nan, 0.0, 1.0])
>>> my_series
0 1.0
1 2.0
2 NaN
3 0.0
4 1.0
dtype: float64
>>> np.ma.fix_invalid(my_series)
masked_array(data=[1.0, 2.0, --, 0.0, 1.0],
mask=[False, False, True, False, False],
fill_value=1e+20)
>>> my_series
0 1.0
1 2.0
2 NaN
3 0.0
4 1.0
dtype: float64
Installed Versions
INSTALLED VERSIONS
commit : d9cdd2e
python : 3.11.9.final.0
python-bits : 64
OS : Darwin
OS-release : 23.5.0
Version : Darwin Kernel Version 23.5.0: Wed May 1 20:13:18 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6030
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 2.2.2
numpy : 2.1.0
pytz : 2024.1
dateutil : 2.9.0
setuptools : 69.5.1
pip : 24.0
Cython : None
pytest : 8.3.1
hypothesis : 6.108.4
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.26.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.1
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None