⚡ Bolt: [performance improvement] Optimize pandas DataFrame iteration speed

ml_peg/calcs/bulk_crystal/elasticity/calc_elasticity.py

@@ -289,7 +289,7 @@ 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 not isinstance(struct, Structure):
             continue

ml_peg/calcs/conformers/MPCONF196/calc_MPCONF196.py

@@ -86,9 +86,9 @@ 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

@@ -84,9 +84,9 @@ 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

@@ -106,7 +106,7 @@ 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.to_dict("records"), dataset, total=df_refs.shape[0]):
             atoms_list = []
             identifier = row["Reaction"]
             reactions = row["Stoichiometry"].split(",")  # Parse stoichiometry string.