BUG: groupby.shift returns different columns when fill_value is specified

asv_bench/benchmarks/groupby.py

@@ -369,6 +369,18 @@ def time_category_size(self):
         self.draws.groupby(self.cats).size()
 
 
+class Shift:
+    def setup(self):
+        N = 18
+        self.df = DataFrame({"g": ["a", "b"] * 9, "v": list(range(N))})
+
+    def time_defaults(self):
+        self.df.groupby("g").shift()
+
+    def time_fill_value(self):
+        self.df.groupby("g").shift(fill_value=99)
+
+
 class FillNA:
     def setup(self):
         N = 100

doc/source/whatsnew/v1.4.0.rst

@@ -166,6 +166,7 @@ Performance improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 - Performance improvement in :meth:`.GroupBy.sample`, especially when ``weights`` argument provided (:issue:`34483`)
 - Performance improvement in :meth:`.GroupBy.transform` for user-defined functions (:issue:`41598`)
+- Performance improvement in :meth:`GroupBy.shift` when ``fill_value`` argument is provided (:issue:`26615`)
 
 .. ---------------------------------------------------------------------------
 
@@ -260,6 +261,7 @@ Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 - Fixed bug in :meth:`SeriesGroupBy.apply` where passing an unrecognized string argument failed to raise ``TypeError`` when the underlying ``Series`` is empty (:issue:`42021`)
 - Bug in :meth:`Series.rolling.apply`, :meth:`DataFrame.rolling.apply`, :meth:`Series.expanding.apply` and :meth:`DataFrame.expanding.apply` with ``engine="numba"`` where ``*args`` were being cached with the user passed function (:issue:`42287`)
+- Bug in :meth:`GroupBy.shift` that would return the grouping columns if ``fill_value`` was not None (:issue:`41556`)
 -
 
 Reshaping

pandas/core/groupby/groupby.py

@@ -2822,6 +2822,7 @@ def _get_cythonized_result(
         result_is_index: bool = False,
         pre_processing=None,
         post_processing=None,
+        fill_value=None,
         **kwargs,
     ):
         """
@@ -2872,6 +2873,8 @@ def _get_cythonized_result(
             second argument, i.e. the signature should be
             (ndarray, Type). If `needs_nullable=True`, a third argument should be
             `nullable`, to allow for processing specific to nullable values.
+        fill_value : any, default None
+            The scalar value to use for newly introduced missing values.
         **kwargs : dict
             Extra arguments to be passed back to Cython funcs
 
@@ -2970,7 +2973,7 @@ def _get_cythonized_result(
                 result = result.reshape(-1)
 
             if result_is_index:
-                result = algorithms.take_nd(values, result)
+                result = algorithms.take_nd(values, result, fill_value=fill_value)
 
             if post_processing:
                 result = post_processing(result, inferences)
@@ -3017,7 +3020,7 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
         tshift : Shift the time index, using the index’s frequency
             if available.
         """
-        if freq is not None or axis != 0 or not isna(fill_value):
+        if freq is not None or axis != 0:
             return self.apply(lambda x: x.shift(periods, freq, axis, fill_value))
 
         return self._get_cythonized_result(
@@ -3027,6 +3030,7 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
             needs_ngroups=True,
             result_is_index=True,
             periods=periods,
+            fill_value=fill_value,
         )
 
     @final

pandas/tests/groupby/test_groupby_shift_diff.py

@@ -55,7 +55,7 @@ def test_group_shift_with_fill_value():
         columns=["Z"],
         index=None,
     )
-    result = g.shift(-1, fill_value=0)[["Z"]]
+    result = g.shift(-1, fill_value=0)
 
     tm.assert_frame_equal(result, expected)