fix(dataframe): handle pandas dimensionality reduction in .xs() for single-item matches

[ManvithPanyam]

What does this PR do?

Fixes .xs() on DeferredDataFrame/DeferredSeries when a key matches
exactly one row and all index levels are selected.

The bug: pandas reduces dimensionality on single-item .xs() matches
(DataFrame → Series, Series → scalar), but Beam's implementation
assumed a static output shape across partitions. Non-matching partitions
return an empty container of the original type, so when the matching
partition returned a dimensionality-reduced result, cross-partition
pd.concat either raised TypeError (scalar concat) or silently produced
a corrupted schema (NaN columns from a shape mismatch).

The fix: when key_size >= nlevels, matching partitions are routed
through a wrapped singleton stage that mirrors pandas' actual runtime
output, then unwrapped to the real return type — instead of assuming the
proxy shape holds at execution time.

Known limitation (documented in code): the proxy schema (computed
at graph-construction time from a 0-row template) can't know whether a
key will match 1 row or several at runtime, so it always assumes the
dimensionality-reduced type. If a key has duplicate matches, pandas
returns the non-reduced container instead — this is fundamentally
undecidable at proxy time, same class of limitation as sort_values(),
describe(), and other data-dependent-shape operations already in this
module. Tests exercising duplicate-match keys use check_proxy=False
accordingly, with the reasoning documented inline.

Fixes

Fixes #28559

Tests

Added regression coverage in frames_test.py for all reported failure
modes: single-level index single match, MultiIndex 0-levels-remaining
single match (both unique and duplicate-key datasets), and Series
single-item .xs(). Full frames_test.py suite: 452 passed, 19 skipped,
zero regressions.

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Assigning reviewers:

R: @jrmccluskey for label python.

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[ManvithPanyam]

@tvalentyn — opened a fix for this. Root cause: Beam's .xs() assumed
static output shape across partitions, but pandas collapses dimensionality
on single-row matches, breaking cross-partition concat.

One thing flagged for review: the proxy can't distinguish single-match
vs. duplicate-match keys at graph-construction time (no row data
available yet), so it always predicts the dimensionality-reduced type.
Documented this as a known limitation in the code — same class of
tradeoff as other data-dependent-shape ops in this module. Open to
feedback if there's a cleaner way to handle it.

[tvalentyn]

dtype.type() will crash on some Pandas types like Categorical Type.

will smth like this work?

        proxy_frame = reindexed._expr.proxy()
        
        dummy_index = (
            pd.MultiIndex.from_tuples([k_val], names=proxy_frame.index.names) if
            isinstance(k_val, tuple) else pd.Index([k_val],
                                                   name=proxy_frame.index.name))
        
        dummy_obj = proxy_frame.reindex(dummy_index)
        
        xs_proxy = dummy_obj.xs(k_val, **kwargs)
        if isinstance(xs_proxy, (pd.DataFrame, pd.Series)):
            xs_proxy = xs_proxy.iloc[:0] 

[tvalentyn]

thanks for the contribution. are you a Beam Dataframes user?

[ManvithPanyam]

Not really an active Beam DataFrames user day-to-day — found this while
working through the dataframe module after my last PR (#39581, the
empty-CSV restriction tracker fix). This was the next issue that stood
out while digging deeper into the module.

Thanks for the catch on dtype.type() — confirmed it breaks on
Categorical (TypeError: type.__new__() takes exactly 3 arguments) and
tz-aware datetime (TypeError: function missing required argument 'year').

Applied your reindex() approach for the DataFrame branch — works
cleanly and is simpler than the manual per-column construction. For the
Series branch (single-match → scalar case), reindex().iloc[0] alone
silently changes dtype on plain numeric types (e.g. int64 → float64,
since reindex introduces NaN), so I kept dtype.type() as the primary
path there and only fall back to reindex().iloc[0] on the TypeError
extension-dtype case.

Also had to handle proxy indexes with duplicate labels — reindex()
raises on non-unique index, so I drop duplicates on the proxy copy
before reindexing (doesn't affect real execution, this is proxy-only).

Added regression tests for Categorical, tz-aware datetime, and nullable
Int64 columns. Full suite: 452 passed, 19 skipped, zero regressions.