diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index cdbef673643e8..98243431e42df 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1661,6 +1661,40 @@ def take(arr, indices, axis: int = 0, allow_fill: bool = False, fill_value=None)
     return result
 
 
+def _take_preprocess_indexer_and_fill_value(
+    arr, indexer, axis, out, fill_value, allow_fill
+):
+    mask_info = None
+
+    if indexer is None:
+        indexer = np.arange(arr.shape[axis], dtype=np.int64)
+        dtype, fill_value = arr.dtype, arr.dtype.type()
+    else:
+        indexer = ensure_int64(indexer, copy=False)
+        if not allow_fill:
+            dtype, fill_value = arr.dtype, arr.dtype.type()
+            mask_info = None, False
+        else:
+            # check for promotion based on types only (do this first because
+            # it's faster than computing a mask)
+            dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+            if dtype != arr.dtype and (out is None or out.dtype != dtype):
+                # check if promotion is actually required based on indexer
+                mask = indexer == -1
+                needs_masking = mask.any()
+                mask_info = mask, needs_masking
+                if needs_masking:
+                    if out is not None and out.dtype != dtype:
+                        raise TypeError("Incompatible type for fill_value")
+                else:
+                    # if not, then depromote, set fill_value to dummy
+                    # (it won't be used but we don't want the cython code
+                    # to crash when trying to cast it to dtype)
+                    dtype, fill_value = arr.dtype, arr.dtype.type()
+
+    return indexer, dtype, fill_value, mask_info
+
+
 def take_nd(
     arr,
     indexer,
@@ -1700,8 +1734,6 @@ def take_nd(
     subarray : array-like
         May be the same type as the input, or cast to an ndarray.
     """
-    mask_info = None
-
     if fill_value is lib.no_default:
         fill_value = na_value_for_dtype(arr.dtype, compat=False)
 
@@ -1712,31 +1744,9 @@ def take_nd(
     arr = extract_array(arr)
     arr = np.asarray(arr)
 
-    if indexer is None:
-        indexer = np.arange(arr.shape[axis], dtype=np.int64)
-        dtype, fill_value = arr.dtype, arr.dtype.type()
-    else:
-        indexer = ensure_int64(indexer, copy=False)
-        if not allow_fill:
-            dtype, fill_value = arr.dtype, arr.dtype.type()
-            mask_info = None, False
-        else:
-            # check for promotion based on types only (do this first because
-            # it's faster than computing a mask)
-            dtype, fill_value = maybe_promote(arr.dtype, fill_value)
-            if dtype != arr.dtype and (out is None or out.dtype != dtype):
-                # check if promotion is actually required based on indexer
-                mask = indexer == -1
-                needs_masking = mask.any()
-                mask_info = mask, needs_masking
-                if needs_masking:
-                    if out is not None and out.dtype != dtype:
-                        raise TypeError("Incompatible type for fill_value")
-                else:
-                    # if not, then depromote, set fill_value to dummy
-                    # (it won't be used but we don't want the cython code
-                    # to crash when trying to cast it to dtype)
-                    dtype, fill_value = arr.dtype, arr.dtype.type()
+    indexer, dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
+        arr, indexer, axis, out, fill_value, allow_fill
+    )
 
     flip_order = False
     if arr.ndim == 2 and arr.flags.f_contiguous: