Update README.md

Timeseries with postaggregators and a picture!

README.md

@@ -6,3 +6,39 @@ pydruid exposes a simple API to create, execute, and analyze [Druid](http://drui
 #documentation
 
 #examples
+
+The following exampes show how to execute and analyze the results of three types of queries:timeseries, topN, and groupby. We analyze the twitter data set
+
+## timeseries query
+
+What was the average tweet length, per day, surrounding the 2014 Sochi olympics?
+
+```python
+from pydruid.client import *
+from pylab import plt
+
+query = PyDruid(bard_url_goes_here, 'druid/v2')
+
+ts = query.timeseries(
+    datasource='twitterstream',
+    granularity='day',
+    intervals='2014-02-02/p4w',
+    aggregations={'length': doublesum('tweet_length'), 'count': doublesum('count')},
+    post_aggregations={'avg_tweet_length': (Field('length') / Field('count'))},
+    filter=Dimension('first_hashtag') == 'sochi2014'
+)
+df = query.export_pandas()
+df['timestamp'] = df['timestamp'].map(lambda x: x.split('T')[0])
+df.plot(x='timestamp', y='avg_tweet_length', ylim=(80, 140), rot=20,
+        title='Sochi 2014')
+plt.ylabel('avg tweet length (chars)')
+plt.show()
+```
+
+![alt text](https://github.com/metamx/pydruid/raw/docs/docs/figures/avg_tweet_length.png "Avg. tweet length")
+
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