Fix breaking changes from pandas v2

pymatgen/analysis/magnetism/heisenberg.py

@@ -327,13 +327,13 @@ def _get_exchange_df(self):
                             ex_row.at[sgraph_index, j_ji] -= s_i * s_j
 
                 # Ignore the row if it is a duplicate to avoid singular matrix
-                if ex_mat.append(ex_row)[j_columns].equals(
-                    ex_mat.append(ex_row)[j_columns].drop_duplicates(keep="first")
-                ):
+                # Create a temporary DataFrame with the new row
+                temp_df = pd.concat([ex_mat, ex_row], ignore_index=True)
+                if temp_df[j_columns].equals(temp_df[j_columns].drop_duplicates(keep="first")):
                     e_index = self.ordered_structures.index(sgraph.structure)
                     ex_row.at[sgraph_index, "E"] = self.energies[e_index]
                     sgraph_index += 1
-                    ex_mat = ex_mat.append(ex_row)
+                    ex_mat = pd.concat([ex_mat, ex_row], ignore_index=True)
                     # if sgraph_index == num_nn_j:  # check for zero columns
                     #     zeros = [b for b in (ex_mat[j_columns] == 0).all(axis=0)]
                     #     if True in zeros:

pymatgen/io/lammps/data.py

@@ -552,7 +552,7 @@ def disassemble(self, atom_labels=None, guess_element=True, ff_label="ff_map"):
         for um, s in zip(unique_masses, symbols):
             masses.loc[masses["mass"] == um, "element"] = s
         if atom_labels is None:  # add unique labels based on elements
-            for el, vc in masses["element"].value_counts().iteritems():
+            for el, vc in masses["element"].value_counts().items():
                 masses.loc[masses["element"] == el, "label"] = [f"{el}{c}" for c in range(1, vc + 1)]
         assert masses["label"].nunique(dropna=False) == len(masses), "Expecting unique atom label for each type"
         mass_info = [(row.label, row.mass) for row in masses.itertuples()]

pymatgen/io/qchem/outputs.py

@@ -1368,13 +1368,7 @@ def _read_optimization_data(self):
             self.data["energy_trajectory"] = []
             if read_pattern(self.text, {"key": r"Error in back_transform"}, terminate_on_match=True).get("key") == [[]]:
                 self.data["errors"] += ["back_transform_error"]
-            elif read_pattern(
-                self.text,
-                {"key": r"pinv\(\)\: svd failed"},
-                terminate_on_match=True,
-            ).get(
-                "key"
-            ) == [[]]:
+            elif read_pattern(self.text, {"key": r"pinv\(\)\: svd failed"}, terminate_on_match=True).get("key") == [[]]:
                 self.data["errors"] += ["svd_failed"]
         else:
             real_energy_trajectory = np.zeros(len(temp_energy_trajectory))
@@ -2668,24 +2662,22 @@ def parse_perturbation_energy(lines: list[str]) -> list[pd.DataFrame]:
     Parse the perturbation energy section of NBO output.
 
     Args:
-            lines: QChem output lines.
+        lines: QChem output lines.
 
     Returns:
-            Data frame of formatted output.
+        Data frame of formatted output.
 
     Raises:
-            RuntimeError
+        RuntimeError
     """
     no_failures = True
     e2_dfs = []
 
     while no_failures:
         # 2nd order perturbation theory analysis
         try:
-            lines = jump_to_header(
-                lines,
-                "SECOND ORDER PERTURBATION THEORY ANALYSIS OF FOCK MATRIX IN NBO BASIS",
-            )
+            header_str = "SECOND ORDER PERTURBATION THEORY ANALYSIS OF FOCK MATRIX IN NBO BASIS"
+            lines = jump_to_header(lines, header_str)
         except RuntimeError:
             no_failures = False