⚡ Bolt: [performance improvement] Replace Pandas iterrows with to_dict('records')#73
⚡ Bolt: [performance improvement] Replace Pandas iterrows with to_dict('records')#73alinelena wants to merge 1 commit into
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Iterating over Pandas DataFrames with `iterrows()` is notoriously slow
because it wraps every row into a Pandas Series object. By converting the
DataFrame to a list of Python dictionaries upfront via `to_dict('records')`,
we bypass Series creation overhead and significantly speed up the
iteration required for structural alignment verification.
Co-authored-by: alinelena <3306823+alinelena@users.noreply.github.com>
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💡 What: Replaced
df.iterrows()withdf.to_dict('records')inverify_processed_omol25.py. Updated downstream Pandas Series method callpq_row.to_dict()to use native Pythondict(pq_row).🎯 Why:
iterrows()creates a bottleneck during DataFrame traversal by allocating Pandas Series objects for every row, adding huge overhead for large datasets.📊 Impact: Massively reduces verification time and memory overhead for structural alignment, as native Python dictionaries are lighter and faster to iterate over than wrapping each row.
🔬 Measurement: Verify by benchmarking
python -m pytest tests/test_verify_processed_omol25.pyand running the script against a large.parquetfile to see the time reduction.PR created automatically by Jules for task 1531267024176874553 started by @alinelena