⚡ Bolt: [performance improvement] optimize dataframe iteration#76
⚡ Bolt: [performance improvement] optimize dataframe iteration#76alinelena wants to merge 1 commit into
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…fast iteration
Replaced `df.iterrows()` with `df.to_dict('records')` in the verification script.
Iterating over a DataFrame using `iterrows` is an anti-pattern as it instantiates a
new pandas Series object for each row, adding significant overhead on large datasets.
By converting the DataFrame to a list of native Python dictionaries first, we eliminate
this overhead, resulting in drastically faster dictionary comprehension. Downstream
references were also updated to utilize the native dict structure directly.
Co-authored-by: alinelena <3306823+alinelena@users.noreply.github.com>
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💡 What:
Replaced
df.iterrows()withdf.to_dict('records')when constructing dictionary lookups from the Parquet dataset inverify_processed_omol25.py. Updated downstreampq_row.to_dict()calls todict(pq_row)since the objects are now native python dictionaries instead of pandas Series.🎯 Why:
Iterating over large pandas DataFrames using
.iterrows()is a known performance anti-pattern. It creates a new Pandas Series object for every single row, adding significant overhead. By converting the entire DataFrame to a list of native Python dictionaries upfront, we completely bypass this serialization overhead during iteration.📊 Impact:
Significantly faster dictionary comprehension and lower memory overhead when cross-referencing large Parquet datasets, potentially reducing alignment step time by an order of magnitude for millions of records.
🔬 Measurement:
Run
python -m pytest tests/test_verify_processed_omol25.pyto ensure core correctness logic remains fully unbroken. For benchmarking on real data, run theverify_processed_omol25.pyscript on a large Parquet/ExtXYZ pair and measure the time spent in the "Loading ExtXYZ file..." step (which occurs immediately after the DataFrame iteration).PR created automatically by Jules for task 5717298047122085002 started by @alinelena