diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 51edc218cc2bd..1f9ada185f9a3 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -231,6 +231,10 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
         -k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -to_json -transpose -values -xs -to_clipboard"
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
+    MSG='Doctests groupby.py' ; echo $MSG
+    pytest -q --doctest-modules pandas/core/groupby/groupby.py -k"-cumcount -describe -pipe"
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+
     MSG='Doctests top-level reshaping functions' ; echo $MSG
     pytest -q --doctest-modules \
         pandas/core/reshape/concat.py \
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index bd8a8852964e3..2eb9171668d35 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1117,7 +1117,6 @@ def mean(self, *args, **kwargs):
         each group.
 
         >>> df.groupby('A').mean()
-        >>>
              B         C
         A
         1  3.0  1.333333
@@ -1126,7 +1125,6 @@ def mean(self, *args, **kwargs):
         Groupby two columns and return the mean of the remaining column.
 
         >>> df.groupby(['A', 'B']).mean()
-        >>>
                C
         A B
         1 2.0  2
@@ -1138,7 +1136,6 @@ def mean(self, *args, **kwargs):
         the group.
 
         >>> df.groupby('A')['B'].mean()
-        >>>
         A
         1    3.0
         2    4.0
@@ -2145,7 +2142,7 @@ def head(self, n=5):
         --------
 
         >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
-                              columns=['A', 'B'])
+        ...                   columns=['A', 'B'])
         >>> df.groupby('A', as_index=False).head(1)
            A  B
         0  1  2
@@ -2172,7 +2169,7 @@ def tail(self, n=5):
         --------
 
         >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
-                              columns=['A', 'B'])
+        ...                   columns=['A', 'B'])
         >>> df.groupby('A').tail(1)
            A  B
         1  a  2