DOC: Added extra sentences to clarify series.GroupBy snippets in examples

pandas/core/series.py

@@ -1815,14 +1815,30 @@ def _set_name(
         Parrot     30.0
         Parrot     20.0
         Name: Max Speed, dtype: float64
+
+        We can pass a list of values to group the Series data by custom labels:
+
         >>> ser.groupby(["a", "b", "a", "b"]).mean()
         a    210.0
         b    185.0
         Name: Max Speed, dtype: float64
+
+        Grouping by numeric labels yields similar results:
+
+        >>> ser.groupby([0, 1, 0, 1]).mean()
+        0    210.0
+        1    185.0
+        Name: Max Speed, dtype: float64
+
+        We can group by a level of the index:
+
         >>> ser.groupby(level=0).mean()
         Falcon    370.0
         Parrot     25.0
         Name: Max Speed, dtype: float64
+
+        We can group by a condition applied to the Series values:
+
         >>> ser.groupby(ser > 100).mean()
         Max Speed
         False     25.0
@@ -1845,11 +1861,16 @@ def _set_name(
         Parrot  Captive     30.0
                 Wild        20.0
         Name: Max Speed, dtype: float64
+
         >>> ser.groupby(level=0).mean()
         Animal
         Falcon    370.0
         Parrot     25.0
         Name: Max Speed, dtype: float64
+
+        We can also group by the 'Type' level of the hierarchical index
+        to get the mean speed for each type:
+
         >>> ser.groupby(level="Type").mean()
         Type
         Captive    210.0
@@ -1865,12 +1886,17 @@ def _set_name(
         b    3
         dtype: int64
 
+        To include `NA` values in the group keys, set `dropna=False`:
+
         >>> ser.groupby(level=0, dropna=False).sum()
         a    3
         b    3
         NaN  3
         dtype: int64
 
+        We can also group by a custom list with NaN values to handle
+        missing group labels:
+
         >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
         >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
         >>> ser.groupby(["a", "b", "a", np.nan]).mean()