CLN: core.internals (#31179)

Author: jbrockmendel

pandas/core/frame.py

@@ -3610,12 +3610,12 @@ def reindexer(value):
             # Explicitly copy here, instead of in sanitize_index,
             # as sanitize_index won't copy an EA, even with copy=True
             value = value.copy()
-            value = sanitize_index(value, self.index, copy=False)
+            value = sanitize_index(value, self.index)
 
         elif isinstance(value, Index) or is_sequence(value):
 
             # turn me into an ndarray
-            value = sanitize_index(value, self.index, copy=False)
+            value = sanitize_index(value, self.index)
             if not isinstance(value, (np.ndarray, Index)):
                 if isinstance(value, list) and len(value) > 0:
                     value = maybe_convert_platform(value)

pandas/core/internals/blocks.py

@@ -2994,7 +2994,6 @@ def make_block(values, placement, klass=None, ndim=None, dtype=None):
 
 def _extend_blocks(result, blocks=None):
     """ return a new extended blocks, given the result """
-    from pandas.core.internals import BlockManager
 
     if blocks is None:
         blocks = []
@@ -3004,9 +3003,8 @@ def _extend_blocks(result, blocks=None):
                 blocks.extend(r)
             else:
                 blocks.append(r)
-    elif isinstance(result, BlockManager):
-        blocks.extend(result.blocks)
     else:
+        assert isinstance(result, Block), type(result)
         blocks.append(result)
     return blocks
 

pandas/core/internals/construction.py

@@ -74,7 +74,7 @@ def arrays_to_mgr(arrays, arr_names, index, columns, dtype=None):
     return create_block_manager_from_arrays(arrays, arr_names, axes)
 
 
-def masked_rec_array_to_mgr(data, index, columns, dtype, copy):
+def masked_rec_array_to_mgr(data, index, columns, dtype, copy: bool):
     """
     Extract from a masked rec array and create the manager.
     """
@@ -143,7 +143,7 @@ def init_ndarray(values, index, columns, dtype=None, copy=False):
     ):
 
         if not hasattr(values, "dtype"):
-            values = prep_ndarray(values, copy=copy)
+            values = _prep_ndarray(values, copy=copy)
             values = values.ravel()
         elif copy:
             values = values.copy()
@@ -166,7 +166,7 @@ def init_ndarray(values, index, columns, dtype=None, copy=False):
 
     # by definition an array here
     # the dtypes will be coerced to a single dtype
-    values = prep_ndarray(values, copy=copy)
+    values = _prep_ndarray(values, copy=copy)
 
     if dtype is not None:
         if not is_dtype_equal(values.dtype, dtype):
@@ -257,7 +257,7 @@ def init_dict(data, index, columns, dtype=None):
 # ---------------------------------------------------------------------
 
 
-def prep_ndarray(values, copy=True) -> np.ndarray:
+def _prep_ndarray(values, copy: bool = True) -> np.ndarray:
     if not isinstance(values, (np.ndarray, ABCSeries, Index)):
         if len(values) == 0:
             return np.empty((0, 0), dtype=object)
@@ -598,29 +598,24 @@ def convert(arr):
 # Series-Based
 
 
-def sanitize_index(data, index, copy=False):
+def sanitize_index(data, index: Index):
     """
     Sanitize an index type to return an ndarray of the underlying, pass
     through a non-Index.
     """
 
-    if index is None:
-        return data
-
     if len(data) != len(index):
         raise ValueError("Length of values does not match length of index")
 
-    if isinstance(data, ABCIndexClass) and not copy:
+    if isinstance(data, ABCIndexClass):
         pass
     elif isinstance(data, (ABCPeriodIndex, ABCDatetimeIndex)):
         data = data._values
-        if copy:
-            data = data.copy()
 
     elif isinstance(data, np.ndarray):
 
         # coerce datetimelike types
         if data.dtype.kind in ["M", "m"]:
-            data = sanitize_array(data, index, copy=copy)
+            data = sanitize_array(data, index, copy=False)
 
     return data