diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 267499edfae6f..8c80fa7052dd5 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -668,8 +668,9 @@ column, which produces an aggregated result with a hierarchical column index:
    grouped[["C", "D"]].agg(["sum", "mean", "std"])
 
 
-The resulting aggregations are named after the functions themselves. If you
-need to rename, then you can add in a chained operation for a ``Series`` like this:
+The resulting aggregations are named after the functions themselves.
+
+For a ``Series``, if you need to rename, you can add in a chained operation like this:
 
 .. ipython:: python
 
@@ -679,8 +680,19 @@ need to rename, then you can add in a chained operation for a ``Series`` like th
        .rename(columns={"sum": "foo", "mean": "bar", "std": "baz"})
    )
 
+Or, you can simply pass a list of tuples each with the name of the new column and the aggregate function:
+
+.. ipython:: python
+
+   (
+      grouped["C"]
+      .agg([("foo", "sum"), ("bar", "mean"), ("baz", "std")])
+   )
+
 For a grouped ``DataFrame``, you can rename in a similar manner:
 
+By chaining ``rename`` operation,
+
 .. ipython:: python
 
    (
@@ -689,6 +701,16 @@ For a grouped ``DataFrame``, you can rename in a similar manner:
        )
    )
 
+Or, passing a list of tuples,
+
+.. ipython:: python
+
+   (
+      grouped[["C", "D"]].agg(
+         [("foo", "sum"), ("bar", "mean"), ("baz", "std")]
+      )
+   )
+
 .. note::
 
    In general, the output column names should be unique, but pandas will allow