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⚡ Bolt: Replace slow .iterrows() loop with .apply() in conversation optimizer #109

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⚡ Bolt: Replace slow .iterrows() loop with .apply() in conversation optimizer #109

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1 change: 1 addition & 0 deletions .Jules/bolt.md
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## 2026-03-31 - Replace slow DataFrame .iterrows() loop with .apply() | Learning: Iterating through pandas DataFrames using .iterrows() is notoriously slow (essentially a Python loop over rows), whereas vectorization or .apply() is significantly faster and more idiomatic for pandas. | Action: Use .apply() with axis=1 for applying complex operations to DataFrame rows instead of explicit iteration.
10 changes: 4 additions & 6 deletions monitoring/conversation_recommendation_optimizer.py
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Expand Up @@ -164,12 +164,10 @@ def _establish_quality_benchmarks(
benchmarks = {}

# Calculate quality scores for all conversations
quality_scores = []
for _, conv in conversations.iterrows():
score = self._calculate_conversation_quality_score(conv)
quality_scores.append(score)

conversations["quality_score"] = quality_scores
# ⚡ Bolt: Replaced slow .iterrows() loop with .apply() for significant performance gain
conversations["quality_score"] = conversations.apply(
self._calculate_conversation_quality_score, axis=1
)

# Get top 10% as benchmark
top_10_percent = conversations.nlargest(
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