REF: move putmask internals in array_algos.putmask

pandas/core/array_algos/putmask.py

@@ -120,3 +120,37 @@ def _putmask_preserve(new_values: np.ndarray, new, mask: np.ndarray):
     except (IndexError, ValueError):
         new_values[mask] = new
     return new_values
+
+
+def putmask_without_repeat(values: np.ndarray, mask: np.ndarray, new: Any) -> None:
+    """
+    np.putmask will truncate or repeat if `new` is a listlike with
+    len(new) != len(values).  We require an exact match.
+
+    Parameters
+    ----------
+    values : np.ndarray
+    mask : np.ndarray[bool]
+    new : Any
+    """
+    if getattr(new, "ndim", 0) >= 1:
+        new = new.astype(values.dtype, copy=False)
+
+    # TODO: this prob needs some better checking for 2D cases
+    nlocs = mask.sum()
+    if nlocs > 0 and is_list_like(new) and getattr(new, "ndim", 1) == 1:
+        if nlocs == len(new):
+            # GH#30567
+            # If length of ``new`` is less than the length of ``values``,
+            # `np.putmask` would first repeat the ``new`` array and then
+            # assign the masked values hence produces incorrect result.
+            # `np.place` on the other hand uses the ``new`` values at it is
+            # to place in the masked locations of ``values``
+            np.place(values, mask, new)
+            # i.e. values[mask] = new
+        elif mask.shape[-1] == len(new) or len(new) == 1:
+            np.putmask(values, mask, new)
+        else:
+            raise ValueError("cannot assign mismatch length to masked array")
+    else:
+        np.putmask(values, mask, new)

pandas/core/internals/blocks.py

@@ -54,7 +54,11 @@
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
 
 import pandas.core.algorithms as algos
-from pandas.core.array_algos.putmask import putmask_inplace, putmask_smart
+from pandas.core.array_algos.putmask import (
+    putmask_inplace,
+    putmask_smart,
+    putmask_without_repeat,
+)
 from pandas.core.array_algos.replace import compare_or_regex_search, replace_regex
 from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays import (
@@ -1030,38 +1034,7 @@ def putmask(self, mask, new, axis: int = 0) -> List["Block"]:
             if transpose:
                 new_values = new_values.T
 
-            # If the default repeat behavior in np.putmask would go in the
-            # wrong direction, then explicitly repeat and reshape new instead
-            if getattr(new, "ndim", 0) >= 1:
-                new = new.astype(new_values.dtype, copy=False)
-
-            # we require exact matches between the len of the
-            # values we are setting (or is compat). np.putmask
-            # doesn't check this and will simply truncate / pad
-            # the output, but we want sane error messages
-            #
-            # TODO: this prob needs some better checking
-            # for 2D cases
-            if (
-                is_list_like(new)
-                and np.any(mask[mask])
-                and getattr(new, "ndim", 1) == 1
-            ):
-                if mask[mask].shape[-1] == len(new):
-                    # GH 30567
-                    # If length of ``new`` is less than the length of ``new_values``,
-                    # `np.putmask` would first repeat the ``new`` array and then
-                    # assign the masked values hence produces incorrect result.
-                    # `np.place` on the other hand uses the ``new`` values at it is
-                    # to place in the masked locations of ``new_values``
-                    np.place(new_values, mask, new)
-                    # i.e. new_values[mask] = new
-                elif mask.shape[-1] == len(new) or len(new) == 1:
-                    np.putmask(new_values, mask, new)
-                else:
-                    raise ValueError("cannot assign mismatch length to masked array")
-            else:
-                np.putmask(new_values, mask, new)
+            putmask_without_repeat(new_values, mask, new)
 
         # maybe upcast me
         elif mask.any():