diff --git a/README.rst b/README.rst
index d0b4ee3..d4e83c7 100644
--- a/README.rst
+++ b/README.rst
@@ -7,24 +7,30 @@ standard tools available. This library strives to make this simple workflow
 as easy and seamless as possible, while providing an elegant interface to work
 with your results.
 
-::
+.. code:: python
 
     import relational
     db = relational.Database('postgres://...')
 
     rows = db.query_file('sqls/active-users.sql')
 
-You can grab rows one at a time::
+You can grab rows one at a time:
+
+.. code:: python
 
     >>> rows.next()
     {'username': 'hansolo', 'name': 'Henry Ford', 'active': True, 'timezone': datetime.datetime(2016, 2, 6, 22, 28, 23, 894202), 'user_email': 'hansolo@gmail.com'}
 
-Iterate over them::
+Iterate over them:
+
+.. code:: python
 
     for row in rows:
         spam_user(name=row['name'], email=row['user_email'])
 
-Or fetch all results for later reference::
+Or fetch all results for later reference:
+
+.. code:: pycon
 
     >>> rows.all()
     [{...}, {...}, {...}, ...]
@@ -33,7 +39,7 @@ Relational also feature full Tablib integration, which allows you to export
 your results to CSV, XLS, JSON, or YAML with a single line of code. Excellent
 for sharing data with friends, or generating reports.
 
-::
+.. code:: pycon
 
     >>> print rows.dataset
     username|active|name      |user_email       |timezone
@@ -41,27 +47,34 @@ for sharing data with friends, or generating reports.
     hansolo |True  |Henry Ford|hansolo@gmail.com|2016-02-06 22:28:23.894202
     ...
 
-Export your query to CSV::
+Export your query to CSV:
+
+.. code:: pycon
 
     >>> rows.dataset.csv
     username,active,name,user_email,timezone
     hansolo,True,Henry Ford,hansolo@gmail.com,2016-02-06 22:28:23.894202
     ...
 
-YAML::
+YAML:
+
+.. code:: pycon
 
     >>> rows.dataset.yaml
-    - {active: true, name: Henry Ford, timezone: '2016-02-06 22:28:23.894202', user_email: hansolo@gmail.com,
-  username: hansolo}
+    - {active: true, name: Henry Ford, timezone: '2016-02-06 22:28:23.894202', user_email: hansolo@gmail.com, username: hansolo}
     ...
 
-JSON::
+JSON:
+
+.. code:: pycon
 
     >>> rows.dataset.json
     [{"username": "hansolo", "active": true, "name": "Henry Ford", "user_email": "hansolo@gmail.com", "timezone": "2016-02-06 22:28:23.894202"}, ...]
 
 
-Excel::
+Excel:
+
+.. code:: python
 
     with open('report.xls', 'wb') as f:
         f.write(rows.dataset.xls)