Description
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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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(optional) I have confirmed this bug exists on the master branch of pandas.
Hi,
The following example of using Series.__setitem__ shows different results on pandas=1.0.5 and pandas=1.1.3:
import pandas as pd
import numpy as np
S1 = pd.Series([0, 1, -12, 3, -10, 5, 6, 7, 8, -11, -13], dtype=np.int32)
idx = pd.Series([4, 9, 2, 10], dtype=np.int64)
value = pd.Series([-10, -11, -12, -13], dtype=np.int32)
S1[idx] = value
S1
It looks like #33643 changed the semantics of setitem operation when assigned values are pandas.Series, i.e. the old-behavior was to first align value with idx and then assign value items to S1 basing on idx.values as index labels, so result was:
>>> S1
0 0
1 1
2 -12
3 3
4 -10
5 5
6 6
7 7
8 8
9 -11
10 -13
dtype: int32
And now the result is as if S1[idx] took precedence before the actual assignment, so that labels in idx.values which are absent in value.index are assigned NaNs:
>>> S1
0 0.0
1 1.0
2 -12.0
3 3.0
4 NaN
5 5.0
6 6.0
7 7.0
8 8.0
9 NaN
10 NaN
dtype: float64
Can you please confirm this was intentional change (as I don't see any tests updated to reflect that, nor there are mentions in "What’s new in 1.1.0" section of the documentation)? From the user perspective it looks like with new implementation, user will have to ensure that value.index == idx.values to achieve the same result, which is not a big deal of course, but still it's some additional code.
First version where the issue appears:
INSTALLED VERSIONS
------------------
commit : 4071c3b872e4ef98a2a2d7c752dd5d2030c4df6e
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0.dev0+1307.g4071c3b87
Kind Regards,
Alexey.