⚡ Bolt: Replace df.iterrows() with zip() for faster batch evaluation#2
⚡ Bolt: Replace df.iterrows() with zip() for faster batch evaluation#2dhanush342 wants to merge 1 commit intomainfrom
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Co-authored-by: dhanush342 <187305764+dhanush342@users.noreply.github.com>
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Pull request overview
This PR optimizes the Streamlit batch evaluation loop by removing pandas.DataFrame.iterrows() usage in run_batch, aiming to reduce per-row overhead during large batch runs in the dashboard.
Changes:
- Replaced
df.iterrows()with parallel-column iteration usingzip()inrun_batch. - Added a
.jules/bolt.mdnote documentingdf.iterrows()as a pandas performance anti-pattern and suggesting alternatives.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| web/streamlit_dashboard.py | Refactors batch loop iteration to avoid iterrows() overhead. |
| .jules/bolt.md | Adds a repository note about avoiding df.iterrows() in performance-sensitive code. |
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| problem_series = df[problem_col].astype(str).tolist() | ||
| reference_series = df[answer_col].tolist() if answer_col and answer_col in df.columns else [None] * total |
| reference = row[answer_col] | ||
| # ⚡ Bolt Optimization: Replacing slow df.iterrows() with fast iteration over parallel arrays/series | ||
| # This avoids the high overhead of creating a Series object for every row | ||
| # and safely handles dynamic column names and indices. |
💡 What: Replaced the slow
df.iterrows()loop inweb/streamlit_dashboard.pywith fast iteration over parallel lists usingzip().🎯 Why:
df.iterrows()is a known performance bottleneck in Pandas because it constructs a newSeriesobject for every row. This slows down the batch evaluation process.📊 Impact: Significantly reduces row iteration overhead, speeding up the data processing loop (often 10-100x faster than iterrows for large dataframes).
🔬 Measurement: Verify by running the Streamlit dashboard (
streamlit run web/streamlit_dashboard.py) and processing a large batch CSV. The overhead between LLM inferences will be noticeably lower.PR created automatically by Jules for task 915598282802158971 started by @dhanush342