⚡ Bolt: [performance improvement] Optimize Pandas DataFrame Iteration

.jules/bolt.md

@@ -8,3 +8,7 @@
 ## 2024-03-29 - ASE Custom JSON encoding vs standard JSON
 **Learning:** ASE's custom JSON encoder (`ase.io.jsonio.encode`) will generate dicts with special keys like `__ndarray__` or `__complex__` (e.g. `{"__ndarray__": [[5], "int64", ...]}`). When optimizing JSON deserialization using faster alternatives like `orjson`, it's critical to realize that a normal `json.loads` or `orjson.loads` will deserialize this into a Python dictionary, while ASE's custom `decode` will properly reconstruct the underlying numpy array. Bypassing ASE's decoder without checking for these keys leads to downstream type errors (e.g. `KeyError: '__ndarray__'`).
 **Action:** When replacing or wrapping ASE's jsonio with `orjson`, always fall back to ASE's `decode` if the payload string contains `__ndarray__` or `__complex__` markers, to ensure custom objects are correctly reconstructed.
+
+## 2024-06-28 - Optimizing Pandas DataFrame Iteration
+**Learning:** Iterating over a DataFrame using `df.iterrows()` wraps every single row into a Pandas Series object. This abstraction is incredibly slow and a notorious bottleneck in Python performance, particularly for larger dataframes or within comprehension expressions.
+**Action:** When a full pass iteration through a Pandas dataframe is necessary (e.g. creating dictionary mappings), immediately convert the dataframe to a list of native Python dictionaries using `df.to_dict("records")`. Iterating over this list skips the Pandas Series wrapper overhead, providing massive performance boosts for lookup generations.

src/lavello_mlips/verify_processed_omol25.py

@@ -77,8 +77,15 @@ def main() -> None:
         )
 
     logger.info(f"Loaded {len(df)} records from Parquet.")
-    parquet_by_sha = {row["geom_sha1"]: row for _, row in df.iterrows()}
-    parquet_by_argone_rel = {row["argonne_rel"]: row for _, row in df.iterrows()}
+
+    # PERFORMANCE OPTIMIZATION:
+    # Converting the DataFrame to a list of dictionaries using `to_dict("records")`
+    # and iterating over the list is significantly faster than using `df.iterrows()`.
+    # `iterrows()` adds massive overhead by wrapping every row into a Pandas Series.
+    df_records = df.to_dict("records")
+    parquet_by_sha = {row["geom_sha1"]: row for row in df_records}
+    parquet_by_argone_rel = {row["argonne_rel"]: row for row in df_records}
+
     logger.info(f"Loading ExtXYZ file from {args.extxyz} (this may take a moment)...")
     all_atoms = read(str(args.extxyz), index=":")
     if not isinstance(all_atoms, list):
@@ -108,7 +115,7 @@ def get_dump_entry(at):
             info = dict(at.info)
             rel = info.get("argonne_rel")
             pq_row = parquet_by_argone_rel.get(rel)
-            pq_data = pq_row.to_dict() if pq_row is not None else None
+            pq_data = pq_row
             return {"xyz": info, "parquet": pq_data}
 
         duplicates = {