ueshin opened a new pull request, #54375:
URL: https://github.com/apache/spark/pull/54375
### What changes were proposed in this pull request?
Support CoW (Copy-on-Write) mode with pandas 3.
### Why are the changes needed?
Pandas 3 is doing copy-on-write for everything.
For example:
```py
>>> pdf = pd.DataFrame(
... [[1, 2], [4, 5], [7, 8]],
... index=["cobra", "viper", "sidewinder"],
... columns=["max_speed", "shield"],
... )
>>>
>>> pser1 = pdf.max_speed
>>> pser2 = pdf.shield
>>>
>>> pdf.loc[["viper", "sidewinder"], ["max_speed", "shield"]] = 10
```
- pandas 2
```py
>>> pdf
max_speed shield
cobra 1 2
viper 10 10
sidewinder 10 10
>>> pser1
cobra 1
viper 10
sidewinder 10
Name: max_speed, dtype: int64
>>> pser2
cobra 2
viper 10
sidewinder 10
Name: shield, dtype: int64
```
- pandas 3
```py
>>> pdf
max_speed shield
cobra 1 2
viper 10 10
sidewinder 10 10
>>> pser1
cobra 1
viper 4
sidewinder 7
Name: max_speed, dtype: int64
>>> pser2
cobra 2
viper 5
sidewinder 8
Name: shield, dtype: int64
```
Or for `Series`:
```py
>>> pdf = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=["cobra",
"viper", "sidewinder"])
>>>
>>> pser = pdf.x
>>> psery = pdf.y
>>>
>>> pser.loc[pser % 2 == 1] = -pser
```
- pandas 2
```py
>>> pdf
x y
cobra -1 4
viper 2 5
sidewinder -3 6
>>> pser
cobra -1
viper 2
sidewinder -3
Name: x, dtype: int64
>>> psery
cobra 4
viper 5
sidewinder 6
Name: y, dtype: int64
```
- pandas 3
```py
>>> pdf
x y
cobra 1 4
viper 2 5
sidewinder 3 6
>>> pser
cobra -1
viper 2
sidewinder -3
Name: x, dtype: int64
>>> psery
cobra 4
viper 5
sidewinder 6
Name: y, dtype: int64
```
### Does this PR introduce _any_ user-facing change?
Yes, it will behave more like pandas 3.
### How was this patch tested?
Updated the related tests to make it clear, but basically the existing tests
should pass.
### Was this patch authored or co-authored using generative AI tooling?
Codex (GPT-5.3-Codex)
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