legacy structure (I hope)

bind/minipandas.py

@@ -144,19 +144,23 @@ class MiniFrame:
     def copy(self) -> "MiniFrame":
         return MiniFrame({k: v.copy() for k, v in self._data.items()})
 
-    def to_pandas(self):
+    def to_pandas(self, indiv_cols: bool = True):
         import pandas as pd
-        df = pd.DataFrame()
+        out = {}
         for k, v in self._data.items():
             if v.ndim == 1:
-                df[k] = v
+                out[k] = v  # 1D numeric column
-            elif v.ndim == 2:
+            elif v.ndim == 2 and indiv_cols:
-                # expand fixed-size vectors into wide columns
+                # expand into wide columns
                 for i in range(v.shape[1]):
-                    df[f"{k}_{i}"] = v[:, i]
+                    out[f"{k}_{i}"] = v[:, i]
+            elif v.ndim == 2 and not indiv_cols:
+                # legacy nested: one Python list per row (object dtype)
+                # NOTE: v.tolist() -> List[List[...]] which most legacy code expects
+                out[k] = pd.Series(v.tolist(), dtype=object)
             else:
                 raise ValueError(f"Column '{k}' has ndim={v.ndim}, not supported.")
-        return df
+        return pd.DataFrame(out)
 
     # convenience constructor
     @staticmethod

bind/test_minipandas.py

@@ -49,4 +49,44 @@ print("\n=== Describe ===")
 desc = mf_all.describe()
 print(desc)
 
-print("\nAll basic minipandas features appear to work correctly.")
+print("\nAll basic minipandas features appear to work correctly.")
+
+
+
+# --- Create a simple MiniFrame ---
+N, M = 3, 4
+mf = mpd.MiniFrame({
+    "run": 1,                              # scalar -> broadcast
+    "event": np.arange(N),                 # 1D scalar column
+    "energy": np.arange(N * M).reshape(N, M),  # 2D vector column
+})
+
+# --- Case 1: indiv_cols=True (default) ---
+df_wide = mf.to_pandas(indiv_cols=True)
+print("=== Expanded columns ===")
+print(df_wide)
+
+# Expected shape: N rows, 1 scalar column ('event'), 1 broadcasted column ('run'), and M expanded columns
+assert list(df_wide.columns) == ["run", "event"] + [f"energy_{i}" for i in range(M)]
+assert df_wide.shape == (N, 2 + M)
+assert np.allclose(df_wide["energy_0"], [0, 4, 8])
+
+# --- Case 2: indiv_cols=False (legacy nested format) ---
+df_nested = mf.to_pandas(indiv_cols=False)
+print("\n=== Nested legacy format ===")
+print(df_nested)
+
+# Expected columns: run, event, energy
+assert list(df_nested.columns) == ["run", "event", "energy"]
+assert isinstance(df_nested.loc[0, "energy"], list)
+assert df_nested.loc[0, "energy"] == [0, 1, 2, 3]
+
+# --- Round-trip compatibility check ---
+mf2 = mpd.MiniFrame({
+    "run": df_nested["run"].to_numpy(),
+    "event": df_nested["event"].to_numpy(),
+    "energy": np.stack(df_nested["energy"].to_numpy()),
+})
+
+assert np.allclose(mf["energy"], mf2["energy"])
+print("\n✅ All checks passed successfully!")