CLN: clean core.construction._extract_index

pandas/core/internals/construction.py

@@ -580,63 +580,59 @@ def _extract_index(data) -> Index:
     """
     Try to infer an Index from the passed data, raise ValueError on failure.
     """
-    index = None
+    index: Index
     if len(data) == 0:
-        index = Index([])
-    else:
-        raw_lengths = []
-        indexes: list[list[Hashable] | Index] = []
-
-        have_raw_arrays = False
-        have_series = False
-        have_dicts = False
-
-        for val in data:
-            if isinstance(val, ABCSeries):
-                have_series = True
-                indexes.append(val.index)
-            elif isinstance(val, dict):
-                have_dicts = True
-                indexes.append(list(val.keys()))
-            elif is_list_like(val) and getattr(val, "ndim", 1) == 1:
-                have_raw_arrays = True
-                raw_lengths.append(len(val))
-            elif isinstance(val, np.ndarray) and val.ndim > 1:
-                raise ValueError("Per-column arrays must each be 1-dimensional")
-
-        if not indexes and not raw_lengths:
-            raise ValueError("If using all scalar values, you must pass an index")
+        return Index([])
 
-        if have_series:
-            index = union_indexes(indexes)
-        elif have_dicts:
-            index = union_indexes(indexes, sort=False)
+    raw_lengths = []
+    indexes: list[list[Hashable] | Index] = []
 
-        if have_raw_arrays:
-            lengths = list(set(raw_lengths))
-            if len(lengths) > 1:
-                raise ValueError("All arrays must be of the same length")
+    have_raw_arrays = False
+    have_series = False
+    have_dicts = False
 
-            if have_dicts:
-                raise ValueError(
-                    "Mixing dicts with non-Series may lead to ambiguous ordering."
-                )
+    for val in data:
+        if isinstance(val, ABCSeries):
+            have_series = True
+            indexes.append(val.index)
+        elif isinstance(val, dict):
+            have_dicts = True
+            indexes.append(list(val.keys()))
+        elif is_list_like(val) and getattr(val, "ndim", 1) == 1:
+            have_raw_arrays = True
+            raw_lengths.append(len(val))
+        elif isinstance(val, np.ndarray) and val.ndim > 1:
+            raise ValueError("Per-column arrays must each be 1-dimensional")
+
+    if not indexes and not raw_lengths:
+        raise ValueError("If using all scalar values, you must pass an index")
+
+    if have_series:
+        index = union_indexes(indexes)
+    elif have_dicts:
+        index = union_indexes(indexes, sort=False)
+
+    if have_raw_arrays:
+        lengths = list(set(raw_lengths))
+        if len(lengths) > 1:
+            raise ValueError("All arrays must be of the same length")
+
+        if have_dicts:
+            raise ValueError(
+                "Mixing dicts with non-Series may lead to ambiguous ordering."
+            )
 
-            if have_series:
-                assert index is not None  # for mypy
-                if lengths[0] != len(index):
-                    msg = (
-                        f"array length {lengths[0]} does not match index "
-                        f"length {len(index)}"
-                    )
-                    raise ValueError(msg)
-            else:
-                index = default_index(lengths[0])
+        if have_series:
+            if lengths[0] != len(index):
+                msg = (
+                    f"array length {lengths[0]} does not match index "
+                    f"length {len(index)}"
+                )
+                raise ValueError(msg)
+        else:
+            index = default_index(lengths[0])
 
-    # error: Argument 1 to "ensure_index" has incompatible type "Optional[Index]";
-    # expected "Union[Union[Union[ExtensionArray, ndarray], Index, Series],
-    # Sequence[Any]]"
-    return ensure_index(index)  # type: ignore[arg-type]
+    return ensure_index(index)
 
 
 def reorder_arrays(