Closed
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
import pandas as pd
import numpy as np
pdf = pd.DataFrame({"x": [np.nan, 2, 3, 4, np.nan, 6], "y": [np.nan, 2, 3, 4, np.nan, 6]})
pser = pdf.x
# Didn't make a copy write to existing (it's unexpected behavior)
pdf.fillna(0, inplace=True)
>>> pser
0 0.0
1 2.0
2 3.0
3 4.0
4 0.0
5 6.0
import pandas as pd
import numpy as np
pdf = pd.DataFrame({"x": [np.nan, 2, 3, 4, np.nan, 6], "y": [np.nan, 2, 3, 4, np.nan, 6]})
pser = pdf.x
# Make a copy and doesn't write to existing (it's expected behavior)
>>> pdf.fillna({"x": -1, "y": -2}, inplace=True)
>>> pser
0 NaN
1 2.0
2 3.0
3 4.0
4 NaN
5 6.0
Name: x, dtype: float64Issue Description
Looks like behavior is a little bit differet in current pandas.
Expected Behavior
Always make a copy and doesn't write to existing array for Do in-place operation in all functions with inplace (such like eval/update/fillna).
Installed Versions
1.4+