codeant-ai-for-open-source[bot] commented on code in PR #40885:
URL: https://github.com/apache/superset/pull/40885#discussion_r3601316015
##########
superset/charts/client_processing.py:
##########
@@ -310,6 +310,33 @@ def table(
}
+def _is_default_index_column(series: pd.Series) -> bool:
+ return series.tolist() == list(range(len(series)))
+
+
+def _read_excel_for_client_processing(
+ data: bytes,
+ form_data: dict[str, Any],
+) -> pd.DataFrame:
+ df = pd.read_excel(BytesIO(data))
+ if len(df.columns) == 0:
+ return df
+
+ first_column = df.columns[0]
+ expected_columns = {
+ *get_column_names(form_data.get("columns")),
+ *get_metric_names(form_data.get("metrics")),
+ }
+
+ if first_column in expected_columns:
+ return df
+
+ if _is_default_index_column(df.iloc[:, 0]):
+ return df.iloc[:, 1:].reset_index(drop=True)
+
+ return df.set_index(first_column)
Review Comment:
**Suggestion:** The XLSX re-import logic infers expected columns only from
`columns` and `metrics`, but pivot-table charts use
`groupbyRows`/`groupbyColumns`; this causes real dimension columns to be
misclassified as index columns and removed before post-processing, which can
trigger missing-column failures or incorrect output. Build the expected-column
set with the pivot-table field names as well (and only strip an index column
when it is explicitly recognized as an export-added index). [incorrect
condition logic]
<details>
<summary><b>Severity Level:</b> Critical 🚨</summary>
```mdx
❌ Pivot-table XLSX reports fail with missing-column errors.
❌ Excel attachments omit pivot dimensions or crash generation.
⚠️ XLSX exports inconsistent with CSV and JSON pivot output.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. Create a chart with viz_type "pivot_table_v2" using non-empty groupbyRows
/
groupbyColumns and metrics, then schedule an XLSX email report so
BaseReportState._get_notification_content() runs the tabular chart path
(superset/commands/report/execute.py:26-68).
2. When report_format is ReportDataFormat.XLSX and a chart is present,
_get_notification_content() calls self._get_data(ChartDataResultFormat.XLSX)
(superset/commands/report/execute.py:57-60), which builds a POST_PROCESSED
request payload
and sends it to ChartDataRestApi.get_data (verified in tests at
tests/unit_tests/commands/report/execute_test.py:124-37).
3. In ChartDataRestApi._send_chart_response()
(superset/charts/data/api.py:11-18), when
result_type == ChartDataResultType.POST_PROCESSED it invokes
apply_client_processing(result, form_data, datasource), and for XLSX
result_format
apply_client_processing() (superset/charts/client_processing.py:81-137)
passes the Excel
bytes into _read_excel_for_client_processing(data, form_data) (lines 58-78).
4. _read_excel_for_client_processing() builds expected_columns only from
columns and
metrics (superset/charts/client_processing.py:67-70), so for pivot_table_v2
where
dimensions live in groupbyRows/groupbyColumns the first dimension column is
not in
expected_columns, causing df.set_index(first_column) (line 78);
pivot_table_v2() then
calls pivot_df() with rows from groupbyRows (lines 40-55), but those row
labels no longer
exist as DataFrame columns, triggering a KeyError during df.pivot_table(...)
and causing
the XLSX report generation request to fail.
```
</details>
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<details>
<summary><b>Prompt for AI Agent 🤖 </b></summary>
```mdx
This is a comment left during a code review.
**Path:** superset/charts/client_processing.py
**Line:** 326:337
**Comment:**
*Incorrect Condition Logic: The XLSX re-import logic infers expected
columns only from `columns` and `metrics`, but pivot-table charts use
`groupbyRows`/`groupbyColumns`; this causes real dimension columns to be
misclassified as index columns and removed before post-processing, which can
trigger missing-column failures or incorrect output. Build the expected-column
set with the pivot-table field names as well (and only strip an index column
when it is explicitly recognized as an export-added index).
Validate the correctness of the flagged issue. If correct, How can I resolve
this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask
user if the user wants to fix the rest of the comments as well. if said yes,
then fetch all the comments validate the correctness and implement a minimal fix
```
</details>
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