[BUG] fix auto_format_on_load crash for RangeIndex time series (#413
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eyasir2047 wants to merge 1 commit intosktime:mainfrom
Open
[BUG] fix auto_format_on_load crash for RangeIndex time series (#413eyasir2047 wants to merge 1 commit intosktime:mainfrom
eyasir2047 wants to merge 1 commit intosktime:mainfrom
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…e#390) The format_data_handle method assumed all indexes are datetime-like, calling y.index.freq, pd.infer_freq(), and pd.date_range() unconditionally. This crashed with 'RangeIndex object has no attribute freq' for valid integer-indexed time series data. Fix: - Add guard to skip datetime-specific frequency inference for RangeIndex and non-datetime indexes, setting 'Integer' as the frequency label - Guard the explicit freq assignment (step 5) against integer indexes - Guard metadata frequency access against RangeIndex Tests: - test_format_data_handle_rangeindex_no_crash: direct unit test - test_format_data_handle_rangeindex_metadata: verify 'Integer' label - test_load_data_source_rangeindex_end_to_end: full pipeline test
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@eyasir2047 You made a duplicate PR to a already linked PR. Please check the issue if there are any attached linked PR's |
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@biru-codeastromer Thanks for letting me know! I'll take a look at the linked PR and close this one if it already covers the fix. |
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Reference Issues/PRs
Fixes #390
What does this implement/fix? Explain your changes.
I ran into this while testing load_data_source with a simple dict-based dataset (no datetime column). The auto-format step was crashing internally with 'RangeIndex' object has no attribute 'freq' — even though the request returned success=True, the formatting silently failed behind the scenes.
The root cause is in format_data_handle(): it calls y.index.freq, pd.infer_freq() and pd.date_range() without checking whether the index is actually datetime-like. A plain integer/RangeIndex doesn't have a .freq attribute, so it blows up.
The fix is a simple guard at the start of the frequency inference block — if the index is a RangeIndex, we skip all the datetime logic and just label the frequency as "Integer". Nothing else changes for datetime-indexed data.
Does your contribution introduce a new dependency? If yes, which one?
No, just uses pd.RangeIndex which is already available.
What should a reviewer concentrate their feedback on?
Mostly the guard condition around line 790 in executor.py— want to make sure it covers all the non-datetime index cases cleanly.
Any other comments?
Added 3 tests covering the unit-level fix, metadata output, and the full end-to-end load_data_sourcecall. Full test suite passes, ruff and format checks are clean.
PR checklist
For all contributions
[x] I've added unit tests and made sure they pass locally.