codeant-ai-for-open-source[bot] commented on code in PR #44679:
URL: https://github.com/apache/superset/pull/44679#discussion_r4300438366
##########
superset/charts/client_processing.py:
##########
@@ -479,6 +479,133 @@ def _apply_show_values_as( # pylint:
disable=too-many-arguments
return numeric / denominator.replace(0, np.nan)
+def _reduce_result_values(values: pd.Series, aggregate: str) -> Any:
+ """Reduce original metric results without aggregating intermediate
summaries."""
+ if values.empty:
+ return None
+ name = aggregate.split(" as Fraction of ")[0]
+ if name == "Count":
+ return len(values)
+ if name == "Count Unique Values":
+ return values.nunique(dropna=False)
+ if name == "List Unique Values":
+ return ", ".join(sorted({str(value) for value in values}))
+ if name in {"First", "Last"}:
+ present = values.dropna()
+ return present.iloc[0 if name == "First" else -1] if len(present) else
None
Review Comment:
✅ **CodeAnt verified this suggestion was addressed in subsequent commits and
marked this thread resolved** as of `71d3b97`.
The `Last` reducer now returns `values.iloc[-1]` directly, preserving a NULL
final result instead of selecting an earlier value.
<sub>If that's not right, unresolve this thread and CodeAnt will leave it
open.</sub>
<!-- codeant-auto-resolve-reply -->
##########
superset/charts/client_processing.py:
##########
@@ -479,6 +479,133 @@ def _apply_show_values_as( # pylint:
disable=too-many-arguments
return numeric / denominator.replace(0, np.nan)
+def _reduce_result_values(values: pd.Series, aggregate: str) -> Any:
+ """Reduce original metric results without aggregating intermediate
summaries."""
+ if values.empty:
+ return None
+ name = aggregate.split(" as Fraction of ")[0]
+ if name == "Count":
+ return len(values)
+ if name == "Count Unique Values":
+ return values.nunique(dropna=False)
+ if name == "List Unique Values":
+ return ", ".join(sorted({str(value) for value in values}))
+ if name in {"First", "Last"}:
+ present = values.dropna()
+ return present.iloc[0 if name == "First" else -1] if len(present) else
None
+ numeric = pd.to_numeric(values, errors="coerce").dropna()
+ if name == "Sum":
+ text = values.dropna()
+ return text.iloc[-1] if numeric.empty and len(text) else numeric.sum()
+ if numeric.empty:
+ return None
+ if name == "Sample Variance":
+ return numeric.var(ddof=1) if len(numeric) > 1 else 0
+ if name == "Sample Standard Deviation":
+ return numeric.std(ddof=1) if len(numeric) > 1 else 0
+ return {
+ "Average": numeric.mean,
+ "Median": numeric.median,
+ "Minimum": numeric.min,
+ "Maximum": numeric.max,
+ }[name]()
+
+
+def _group_result_records(
+ source: pd.DataFrame, dimensions: list[str]
+) -> dict[tuple[str, ...], pd.DataFrame]:
+ """Index contributing records by the dimension prefix of a summary
scope."""
+ if not dimensions:
+ return {(): source}
+ keys = [source[dim].fillna("SUPERSET_PANDAS_NAN").astype(str) for dim in
dimensions]
+ return {
+ key if isinstance(key, tuple) else (key,): group
+ for key, group in source.groupby(keys, sort=False, dropna=False)
+ }
Review Comment:
✅ **CodeAnt verified this suggestion was addressed in subsequent commits and
marked this thread resolved** as of `71d3b97`.
Dimension keys are grouped as original scalar values, and
`_result_scope_key` preserves those typed values instead of converting them to
strings.
<sub>If that's not right, unresolve this thread and CodeAnt will leave it
open.</sub>
<!-- codeant-auto-resolve-reply -->
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