BUG: Fix .rolling().mean() returning NaNs on reassignment (#61841)

[abujabarmubarak]

What does this PR do?

Fixes issue #61841 where .rolling().mean() unexpectedly returns all NaNs when the same assignment is executed more than once, even with .copy() used on the DataFrame.

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Problem

When using:

df["SMA20"] = df["Close"].rolling(20).mean()
df["SMA20"] = df["Close"].rolling(20).mean()  # Unexpectedly returns all NaNs

Only the first assignment works as expected. The second assignment results in a column full of NaNs. This bug is caused by slicing the output with [:: self.step] inside _apply_columnwise(), which alters the result's shape and breaks alignment during reassignment.

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Fix

This PR removes the problematic slicing from _apply_columnwise():

Before (buggy):

return self._apply_columnwise(...)[:: self.step]

After (fixed):

result = self._apply_columnwise(...)
return result

This change:

• Preserves result shape and index alignment
• Ensures .rolling().mean() works even on repeated assignment
• Matches behavior in Pandas 2.3.x and above

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Testing

Reproduced and verified the fix using both real-world and synthetic data:

import pandas as pd

df = pd.DataFrame({"Close": range(1, 31)})
df = df.copy()
df["SMA20"] = df["Close"].rolling(20).mean()
df["SMA20"] = df["Close"].rolling(20).mean()  # ✅ Now works correctly

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Notes

• This was confirmed to be broken in Pandas 2.2.x and still reproducible in main without this patch.
• Newer versions avoid the issue due to deeper internal refactors, but this fix explicitly prevents the bug in current code.

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Let me know if anything needs improvement. Thanks for reviewing!