REF: tighten signature on maybe_convert_platform

pandas/core/arrays/interval.py

@@ -53,10 +53,10 @@
 )
 from pandas.core.dtypes.dtypes import IntervalDtype
 from pandas.core.dtypes.generic import (
+    ABCDataFrame,
     ABCDatetimeIndex,
     ABCIntervalIndex,
     ABCPeriodIndex,
-    ABCSeries,
 )
 from pandas.core.dtypes.missing import (
     is_valid_na_for_dtype,
@@ -206,10 +206,7 @@ def __new__(
         verify_integrity: bool = True,
     ):
 
-        if isinstance(data, (ABCSeries, ABCIntervalIndex)) and is_interval_dtype(
-            data.dtype
-        ):
-            data = data._values  # TODO: extract_array?
+        data = extract_array(data, extract_numpy=True)
 
         if isinstance(data, cls):
             left = data._left
@@ -1616,7 +1613,15 @@ def _maybe_convert_platform_interval(values) -> ArrayLike:
         # empty lists/tuples get object dtype by default, but this is
         # prohibited for IntervalArray, so coerce to integer instead
         return np.array([], dtype=np.int64)
+    elif not is_list_like(values) or isinstance(values, ABCDataFrame):
+        # This will raise later, but we avoid passing to maybe_convert_platform
+        return values
     elif is_categorical_dtype(values):
         values = np.asarray(values)
+    elif not hasattr(values, "dtype") and not isinstance(values, (list, tuple, range)):
+        # TODO: should we just cast these to list?
+        return values
+    else:
+        values = extract_array(values, extract_numpy=True)
 
     return maybe_convert_platform(values)

pandas/core/dtypes/cast.py

@@ -119,16 +119,21 @@
 _int64_max = np.iinfo(np.int64).max
 
 
-def maybe_convert_platform(values):
+def maybe_convert_platform(
+    values: Union[list, tuple, range, np.ndarray, ExtensionArray]
+) -> ArrayLike:
     """ try to do platform conversion, allow ndarray or list here """
     if isinstance(values, (list, tuple, range)):
-        values = construct_1d_object_array_from_listlike(values)
-    if getattr(values, "dtype", None) == np.object_:
-        if hasattr(values, "_values"):
-            values = values._values
-        values = lib.maybe_convert_objects(values)
+        arr = construct_1d_object_array_from_listlike(values)
+    else:
+        # The caller is responsible for ensuring that we have np.ndarray
+        #  or ExtensionArray here.
+        arr = values
 
-    return values
+    if arr.dtype == object:
+        arr = lib.maybe_convert_objects(arr)
+
+    return arr
 
 
 def is_nested_object(obj) -> bool:

pandas/core/internals/construction.py

@@ -400,7 +400,15 @@ def _prep_ndarray(values, copy: bool = True) -> np.ndarray:
             return arr[..., np.newaxis]
 
         def convert(v):
-            return maybe_convert_platform(v)
+            if not is_list_like(v) or isinstance(v, ABCDataFrame):
+                return v
+            elif not hasattr(v, "dtype") and not isinstance(v, (list, tuple, range)):
+                # TODO: should we cast these to list?
+                return v
+
+            v = extract_array(v, extract_numpy=True)
+            res = maybe_convert_platform(v)
+            return res
 
         # we could have a 1-dim or 2-dim list here
         # this is equiv of np.asarray, but does object conversion