[sohailshk]

Fixes #41283

Series.skew() and Series.kurtosis() merge per-partition moments in combine_moments. The result used combined_n, which is only assigned when two non-empty partitions are merged, so when only one partition holds data (for example a small input processed in one bundle) the call raised UnboundLocalError: cannot access local variable 'combined_n'. After the merge loop n already holds the total count (it equals combined_n after every merge), so the result now uses n; results for several partitions are unchanged.

Tests: test_series_skew_kurtosis_single_partition evaluates both on one partition (distributed=False); it fails on master with the UnboundLocalError and passes with the change. pytest apache_beam/dataframe/frames_test.py: 460 passed, 19 skipped. With to_dataframe on a beam.Create of 6 floats, both calls failed on master on the default runner and on the FnApiRunner; after the change they return 0.9191843544212499 and -0.25414322711619963, the same as pandas.

[github-actions]

📋 Review Summary

This PR resolves an UnboundLocalError in DeferredSeries.skew() and DeferredSeries.kurtosis() when all non-empty data resides within a single partition. Replacing combined_n with n in the final return expressions of combine_moments is mathematically sound and cleanly handles both single-partition and multi-partition inputs.

🔍 General Feedback

• The fix in sdks/python/apache_beam/dataframe/frames.py accurately uses n, which holds the total sample count after iterating through all partitions regardless of whether the merge branch (else) was entered.
• The added unit test in sdks/python/apache_beam/dataframe/frames_test.py with distributed=False directly covers the single-partition execution path.

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[github-actions]

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