⚡ Bolt: Optimize DataFrame creation in map_psms_to_spectra

[google-labs-jules]

⚡ Bolt: Optimize DataFrame creation in map_psms_to_spectra

💡 What:

• Refactored map_psms_to_spectra to construct the spectra DataFrame using a list of dictionaries with explicit columns, instead of relying on schema inference.
• Used .tolist() to iterate over the Pandas Series, which is faster than direct Series iteration.
• Pre-filled missing matches with None values to prevent NaN (float) creation, ensuring downstream boolean logic (if row['title']:) remains correct.
• Added __pycache__/ to .gitignore to prevent committing bytecode.

🎯 Why:

• pd.DataFrame(list_of_dicts) is slow when it has to scan the full list to infer columns. Explicitly passing columns avoids this scan.
• Iterating over a Pandas Series is slower than iterating over a native Python list.
• Using None instead of NaN for missing object data preserves logical correctness in the application.

📊 Impact:

• Measured ~19% reduction in execution time on a scaled benchmark dataset (2560 spectra).

🔬 Measurement:

• Verified using a custom benchmark script (created and deleted during process).
• Verified correctness with existing test suite (pytest).

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PR created automatically by Jules for task 17819278531563266474 started by @erayfirat

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