fix: use Series.iloc for positional access in generate_join_column

[eschutho]

What

Silences a pandas FutureWarning that fires on every chart render using time-shift joins:

FutureWarning: Series.__getitem__ treating keys as positions is deprecated.
In a future version, integer keys will always be treated as labels
(consistent with DataFrame behavior). To access a value by position,
use `ser.iloc[pos]`
  value = row[column_index]

Source: superset/models/helpers.py — ExploreMixin.generate_join_column

Change

-        value = row[column_index]
+        value = row.iloc[column_index]

df.apply(..., axis=1) produces a pd.Series per row whose index labels are the DataFrame column names (always strings from SQL result sets). row[0] was being resolved positionally, which pandas now warns against. .iloc[column_index] is the explicit positional API and matches the original intent exactly.

No behavior change

row[0] and row.iloc[0] return the same value for any DataFrame whose columns are string-labeled (the only case that can reach this code path). This is a pure deprecation fix.

Test plan

• Existing tests in tests/unit_tests/common/test_time_shifts.py cover generate_join_column via all four time grains and pass on this branch.
• No new tests needed: the integer-label edge case cannot arise in production (all SQL result set columns are string-named).

[bito-code-review]

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 64.17%. Comparing base (2b58411) to head (556b453).
⚠️ Report is 2 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #40936   +/-   ##
=======================================
  Coverage   64.17%   64.17%           
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  Files        2654     2654           
  Lines      143763   143763           
  Branches    33161    33161           
=======================================
  Hits        92255    92255           
  Misses      49890    49890           
  Partials     1618     1618           

Flag	Coverage Δ
hive	39.47% <0.00%> (ø)
mysql	58.21% <100.00%> (ø)
postgres	58.28% <100.00%> (ø)
presto	41.06% <0.00%> (ø)
python	59.75% <100.00%> (ø)
sqlite	57.90% <100.00%> (ø)
unit	100.00% <ø> (ø)

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[rebenitez1802]

LGTM