diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 659ca228bdcb0..9a813e866e8d0 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3212,6 +3212,9 @@ def ngroup(self, ascending: bool = True):
         would be seen when iterating over the groupby object, not the
         order they are first observed.
 
+        Groups with missing keys (where `pd.isna()` is True) will be labeled with `NaN`
+        and will be skipped from the count.
+
         Parameters
         ----------
         ascending : bool, default True
@@ -3228,38 +3231,38 @@ def ngroup(self, ascending: bool = True):
 
         Examples
         --------
-        >>> df = pd.DataFrame({"A": list("aaabba")})
+        >>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]})
         >>> df
-           A
-        0  a
-        1  a
-        2  a
-        3  b
-        4  b
-        5  a
-        >>> df.groupby('A').ngroup()
-        0    0
-        1    0
-        2    0
-        3    1
-        4    1
-        5    0
-        dtype: int64
-        >>> df.groupby('A').ngroup(ascending=False)
+           color
+        0    red
+        1   None
+        2    red
+        3   blue
+        4   blue
+        5    red
+        >>> df.groupby("color").ngroup()
+        0    1.0
+        1    NaN
+        2    1.0
+        3    0.0
+        4    0.0
+        5    1.0
+        dtype: float64
+        >>> df.groupby("color", dropna=False).ngroup()
         0    1
-        1    1
+        1    2
         2    1
         3    0
         4    0
         5    1
         dtype: int64
-        >>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
-        0    0
+        >>> df.groupby("color", dropna=False).ngroup(ascending=False)
+        0    1
         1    0
         2    1
-        3    3
+        3    2
         4    2
-        5    0
+        5    1
         dtype: int64
         """
         with self._group_selection_context():