diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
index 4cde1fed344a8..484efd12c5d78 100644
--- a/doc/source/groupby.rst
+++ b/doc/source/groupby.rst
@@ -1014,6 +1014,23 @@ Regroup columns of a DataFrame according to their sum, and sum the aggregated on
    df
    df.groupby(df.sum(), axis=1).sum()
 
+Groupby by Indexer to 'resample' data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Resampling produces new hypothetical samples(resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.
+
+In order to resample to work on indices that are non-datetimelike , the following procedure can be utilized.
+
+In the following examples, **df.index // 5** returns a binary array which is used to determine what get's selected for the groupby operation.
+
+.. note:: The below example shows how we can downsample by consolidation of samples into fewer samples. Here by using **df.index // 5**, we are aggregating the samples in bins. By applying **std()** function, we aggregate the information contained in many samples into a small subset of values which is their standard deviation thereby reducing the number of samples.
+
+.. ipython:: python
+
+   df = pd.DataFrame(np.random.randn(10,2))
+   df
+   df.index // 5
+   df.groupby(df.index // 5).std()
 
 Returning a Series to propagate names
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~