diff --git a/pandas/core/strings/accessor.py b/pandas/core/strings/accessor.py
index 1461c52d5cb65..43df34a7ecbb2 100644
--- a/pandas/core/strings/accessor.py
+++ b/pandas/core/strings/accessor.py
@@ -3034,22 +3034,6 @@ def cat_core(list_of_columns: List, sep: str):
     return np.sum(arr_with_sep, axis=0)
 
 
-def _groups_or_na_fun(regex):
-    """Used in both extract_noexpand and extract_frame"""
-    empty_row = [np.nan] * regex.groups
-
-    def f(x):
-        if not isinstance(x, str):
-            return empty_row
-        m = regex.search(x)
-        if m:
-            return [np.nan if item is None else item for item in m.groups()]
-        else:
-            return empty_row
-
-    return f
-
-
 def _result_dtype(arr):
     # workaround #27953
     # ideally we just pass `dtype=arr.dtype` unconditionally, but this fails
@@ -3087,41 +3071,31 @@ def _get_group_names(regex: Pattern) -> List[Hashable]:
     return [names.get(1 + i, i) for i in range(regex.groups)]
 
 
-def _str_extract_noexpand(arr: ArrayLike, pat: str, flags=0):
+def _str_extract(arr: ArrayLike, pat: str, flags=0, expand: bool = True):
     """
     Find groups in each string in the array using passed regular expression.
 
-    This function is called from str_extract(expand=False) when there is a single group
-    in the regex.
-
     Returns
     -------
-    np.ndarray
+    np.ndarray or list of lists is expand is True
     """
     regex = re.compile(pat, flags=flags)
-    groups_or_na = _groups_or_na_fun(regex)
-
-    result = np.array([groups_or_na(val)[0] for val in np.asarray(arr)], dtype=object)
-    return result
-
 
-def _str_extract_expand(arr: ArrayLike, pat: str, flags: int = 0) -> List[List]:
-    """
-    Find groups in each string in the array using passed regular expression.
-
-    For each subject string in the array, extract groups from the first match of
-    regular expression pat. This function is called from str_extract(expand=True) or
-    str_extract(expand=False) when there is more than one group in the regex.
+    empty_row = [np.nan] * regex.groups
 
-    Returns
-    -------
-    list of lists
+    def f(x):
+        if not isinstance(x, str):
+            return empty_row
+        m = regex.search(x)
+        if m:
+            return [np.nan if item is None else item for item in m.groups()]
+        else:
+            return empty_row
 
-    """
-    regex = re.compile(pat, flags=flags)
-    groups_or_na = _groups_or_na_fun(regex)
+    if expand:
+        return [f(val) for val in np.asarray(arr)]
 
-    return [groups_or_na(val) for val in np.asarray(arr)]
+    return np.array([f(val)[0] for val in np.asarray(arr)], dtype=object)
 
 
 def str_extract(accessor: StringMethods, pat: str, flags: int = 0, expand: bool = True):
@@ -3143,7 +3117,7 @@ def str_extract(accessor: StringMethods, pat: str, flags: int = 0, expand: bool
             result = DataFrame(columns=columns, dtype=result_dtype)
 
         else:
-            result_list = _str_extract_expand(obj.array, pat, flags=flags)
+            result_list = _str_extract(obj.array, pat, flags=flags, expand=returns_df)
 
             result_index: Optional["Index"]
             if isinstance(obj, ABCSeries):
@@ -3157,7 +3131,7 @@ def str_extract(accessor: StringMethods, pat: str, flags: int = 0, expand: bool
 
     else:
         name = _get_single_group_name(regex)
-        result_arr = _str_extract_noexpand(obj.array, pat, flags=flags)
+        result_arr = _str_extract(obj.array, pat, flags=flags, expand=returns_df)
         # not dispatching, so we have to reconstruct here.
         result = pd_array(result_arr, dtype=result_dtype)
     return accessor._wrap_result(result, name=name)