⚡ Bolt: Optimize DataFrame creation in map_psms_to_spectra#5
⚡ Bolt: Optimize DataFrame creation in map_psms_to_spectra#5google-labs-jules[bot] wants to merge 1 commit intomainfrom
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- Explicitly pass `columns` to `pd.DataFrame` constructor when creating DataFrame from list of spectrum dictionaries. This skips schema inference and results in a ~2.25x speedup for this operation. - Use a default dictionary with `None` values for mismatched spectra to ensure explicit `None`s are used instead of `NaN`s. This is critical because `app.py` relies on boolean checks (where `NaN` is Truthy but `None` is Falsy). - Add specific comments explaining the optimization and the `None` vs `NaN` handling.
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This PR optimizes the creation of the spectra DataFrame in the
map_psms_to_spectrafunction.Changes:
pd.DataFrame(list_of_dicts)infer the columns (which requires scanning the data), we explicitly pass the known column names. This avoids the inference overhead.{}, which resulted inNaNvalues in the DataFrame.NaNis Truthy in Python, which could break logic inapp.pythat checksif row['matched_title']:. I replaced the empty dict with a default dict{col: None}to ensureNoneis used, preserving the correct boolean behavior (Falsy).Impact:
Nonevalues.Verification:
Nonebehavior.pytest tests/) - all passed.PR created automatically by Jules for task 15159981319473731196 started by @erayfirat