⚡ Bolt: Optimize iterrows in verify_processed_omol25.py - #96
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Converted dataframe rows directly to a list of dicts before iterating over them to build the dictionaries for properties and structures by sha and argonne_rel. In `verify_processed_omol25.py`, iterating using `df.iterrows()` inside a dictionary comprehension results in thousands of calls dynamically instantiating `pd.Series` row by row which takes significantly longer to run than allocating the raw dictionaries all at once. 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🎯 Why: Using
df.iterrows()is extremely slow because it yields a new Pandas Series object for every row.📊 Impact: Processing large datasets will be much faster by converting straight to native Python dictionaries first and skipping Series object instantiation overhead.
🔬 Measurement: Run the command
verify_processed_omol25 --parquet data/example.parquet --extxyz data/example.xyzon a large dataset before and after the change and measure the total elapsed time.PR created automatically by Jules for task 2067830842663480287 started by @alinelena