⚡ Bolt: [performance improvement] Fast pandas dataframe iteration

.jules/bolt.md

@@ -5,3 +5,7 @@
 ## 2024-05-19 - Caching YAML Load for Framework Registry
 **Learning:** `yaml.safe_load` on `frameworks.yml` within `load_framework_registry()` was taking ~2-3 ms per call and it was repeatedly called for every framework entry via `get_framework_config()`. This was a micro-bottleneck, especially when dealing with lists or multiple frameworks.
 **Action:** Applied the `@lru_cache` and `deepcopy` pattern successfully again to `load_framework_registry()` and `get_framework_config()` to avoid caching a mutable dictionary directly and avoid repeated YAML I/O parsing.
+
+## 2024-05-20 - Fast DataFrame Iteration Avoids Overhead
+**Learning:** Iterating over Pandas DataFrames using `.iterrows()` introduces significant overhead because it creates a new `pd.Series` object for every single row. In calculation scripts (like elasticity, solvMPCONF196, etc.), this causes notable slowdowns when processing large datasets or many files.
+**Action:** Replace `.iterrows()` with `.itertuples(index=False)` (or `name=None` if accessing by index `row[0]`) for purely positional or namedtuple-based attribute access, which is drastically faster. If variable column names require string keys, use `for row in df.to_dict('records'):`. Always check if `.iterrows()` is a bottleneck and optimize it.

ml_peg/calcs/bulk_crystal/elasticity/calc_elasticity.py

@@ -235,7 +235,7 @@ def run_elasticity_benchmark(
 
     # Save relaxed structures to extxyz for visualisation
     atoms_list = []
-    for _, row in results.iterrows():
+    for row in results.to_dict("records"):
         struct = row.get("final_structure")
         if struct is not None:
             atoms = AseAtomsAdaptor.get_atoms(struct).copy()

ml_peg/calcs/conformers/MPCONF196/calc_MPCONF196.py

@@ -85,9 +85,9 @@ def get_ref_energies(data_path: Path) -> dict[str, float]:
     )
     ref_energies = {}
 
-    for row in df.iterrows():
-        label = row[1][0]
-        ref_energies[label] = float(row[1][2]) * KCAL_TO_EV
+    for row in df.itertuples(index=False, name=None):
+        label = row[0]
+        ref_energies[label] = float(row[2]) * KCAL_TO_EV
 
     return ref_energies
 

ml_peg/calcs/conformers/solvMPCONF196/calc_solvMPCONF196.py

@@ -83,9 +83,9 @@ def get_ref_energies(data_path: Path) -> dict[str, float]:
     )
     ref_energies = {}
 
-    for row in df.iterrows():
-        label = row[1][0]
-        e_ref = float(row[1][1]) * units.Hartree
+    for row in df.itertuples(index=False, name=None):
+        label = row[0]
+        e_ref = float(row[1]) * units.Hartree
         ref_energies[label] = e_ref
 
     return ref_energies

ml_peg/calcs/utils/gscdb138.py

@@ -105,11 +105,13 @@ def run_gscdb138(
         df_refs["Reference"] *= units.Hartree
 
         # Calculate relative energy for each entry.
-        for _, row in tqdm(df_refs.iterrows(), dataset, total=df_refs.shape[0]):
+        for row in tqdm(
+            df_refs.itertuples(index=False), dataset, total=df_refs.shape[0]
+        ):
             atoms_list = []
-            identifier = row["Reaction"]
-            reactions = row["Stoichiometry"].split(",")  # Parse stoichiometry string.
-            e_rel_ref = row["Reference"]
+            identifier = row.Reaction
+            reactions = row.Stoichiometry.split(",")  # Parse stoichiometry string.
+            e_rel_ref = row.Reference
             num_species = len(reactions) // 2  # Each species has coefficient and name.
 
             e_rel_model = 0