From d900d5f46185b38bbb008e95b813877aeb66cb0d Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham@Marks-MacBook-Pro.local>
Date: Tue, 17 Nov 2020 17:02:54 +0000
Subject: [PATCH 1/2] add two new examples to the docs

---
 pandas/core/shared_docs.py | 53 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py
index cc918c27b5c2e..8d1f4ac4f9578 100644
--- a/pandas/core/shared_docs.py
+++ b/pandas/core/shared_docs.py
@@ -324,4 +324,57 @@
 0  0.000000   1.000000
 1  1.000000   2.718282
 2  1.414214   7.389056
+
+It is possible to call transform on a GroupBy object:
+
+>>> df = pd.DataFrame({{
+...     "Date": [
+...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
+...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
+...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
+... }})
+>>> df
+         Date  Data
+0  2015-05-08     5
+1  2015-05-07     8
+2  2015-05-06     6
+3  2015-05-05     1
+4  2015-05-08    50
+5  2015-05-07   100
+6  2015-05-06    60
+7  2015-05-05   120
+>>> df.groupby('Date')['Data'].transform('sum')
+0     55
+1    108
+2     66
+3    121
+4     55
+5    108
+6     66
+7    121
+Name: Data, dtype: int64
+
+>>> df = pd.DataFrame({{
+...     "c": [1, 1, 1, 2, 2, 2, 2],
+...     "type": ["m", "n", "o", "m", "m", "n", "n"]
+... }})
+>>> df
+   c type
+0  1    m
+1  1    n
+2  1    o
+3  2    m
+4  2    m
+5  2    n
+6  2    n
+>>> df['size'] = df.groupby('c')['type'].transform(len)
+>>> df
+   c type size
+0  1    m    3
+1  1    n    3
+2  1    o    3
+3  2    m    4
+4  2    m    4
+5  2    n    4
+6  2    n    4
 """

From 5c96a68e149537f76133df918fa8ad0ee0a5091f Mon Sep 17 00:00:00 2001
From: Mark Graham <mark.graham93@btinternet.com>
Date: Thu, 19 Nov 2020 10:16:09 +0000
Subject: [PATCH 2/2] amend wording

---
 pandas/core/shared_docs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py
index 8d1f4ac4f9578..03e39624253b2 100644
--- a/pandas/core/shared_docs.py
+++ b/pandas/core/shared_docs.py
@@ -325,7 +325,7 @@
 1  1.000000   2.718282
 2  1.414214   7.389056
 
-It is possible to call transform on a GroupBy object:
+You can call transform on a GroupBy object:
 
 >>> df = pd.DataFrame({{
 ...     "Date": [