diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index c3705fada724a..0fa02d54b5b78 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1634,10 +1634,10 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
 
     Parameters
     ----------
-    arr : ndarray
+    arr : ndarray or ExtensionArray
     n : int
         number of periods
-    axis : int
+    axis : {0, 1}
         axis to shift on
     stacklevel : int
         The stacklevel for the lost dtype warning.
@@ -1651,7 +1651,8 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
     na = np.nan
     dtype = arr.dtype
 
-    if dtype.kind == "b":
+    is_bool = is_bool_dtype(dtype)
+    if is_bool:
         op = operator.xor
     else:
         op = operator.sub
@@ -1677,17 +1678,15 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
             dtype = arr.dtype
 
     is_timedelta = False
-    is_bool = False
     if needs_i8_conversion(arr.dtype):
         dtype = np.int64
         arr = arr.view("i8")
         na = iNaT
         is_timedelta = True
 
-    elif is_bool_dtype(dtype):
+    elif is_bool:
         # We have to cast in order to be able to hold np.nan
         dtype = np.object_
-        is_bool = True
 
     elif is_integer_dtype(dtype):
         # We have to cast in order to be able to hold np.nan
@@ -1708,45 +1707,26 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
     dtype = np.dtype(dtype)
     out_arr = np.empty(arr.shape, dtype=dtype)
 
-    na_indexer = [slice(None)] * arr.ndim
+    na_indexer = [slice(None)] * 2
     na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
     out_arr[tuple(na_indexer)] = na
 
-    if arr.ndim == 2 and arr.dtype.name in _diff_special:
+    if arr.dtype.name in _diff_special:
         # TODO: can diff_2d dtype specialization troubles be fixed by defining
         #  out_arr inside diff_2d?
         algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
     else:
         # To keep mypy happy, _res_indexer is a list while res_indexer is
         #  a tuple, ditto for lag_indexer.
-        _res_indexer = [slice(None)] * arr.ndim
+        _res_indexer = [slice(None)] * 2
         _res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
         res_indexer = tuple(_res_indexer)
 
-        _lag_indexer = [slice(None)] * arr.ndim
+        _lag_indexer = [slice(None)] * 2
         _lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
         lag_indexer = tuple(_lag_indexer)
 
-        # need to make sure that we account for na for datelike/timedelta
-        # we don't actually want to subtract these i8 numbers
-        if is_timedelta:
-            res = arr[res_indexer]
-            lag = arr[lag_indexer]
-
-            mask = (arr[res_indexer] == na) | (arr[lag_indexer] == na)
-            if mask.any():
-                res = res.copy()
-                res[mask] = 0
-                lag = lag.copy()
-                lag[mask] = 0
-
-            result = res - lag
-            result[mask] = na
-            out_arr[res_indexer] = result
-        elif is_bool:
-            out_arr[res_indexer] = arr[res_indexer] ^ arr[lag_indexer]
-        else:
-            out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
+        out_arr[res_indexer] = op(arr[res_indexer], arr[lag_indexer])
 
     if is_timedelta:
         out_arr = out_arr.view("timedelta64[ns]")