Querying

Any pandas dataframes will be automatically detected by pandasql and you can query them as you would any regular SQL table.

$ python
>>> from pandasql import PandaSQL, load_meat, load_births
>>> meat = load_meat()
>>> births = load_births()
>>> pdsql = PandaSQL()
>>> print pdsql("SELECT * FROM meat LIMIT 10;").head()
                  date  beef  veal  pork  lamb_and_mutton broilers other_chicken turkey
0  1944-01-01 00:00:00   751    85  1280               89     None          None   None
1  1944-02-01 00:00:00   713    77  1169               72     None          None   None
2  1944-03-01 00:00:00   741    90  1128               75     None          None   None
3  1944-04-01 00:00:00   650    89   978               66     None          None   None
4  1944-05-01 00:00:00   681   106  1029               78     None          None   None

joins and aggregations are also supported

>>> q = """SELECT
        m.date, m.beef, b.births
     FROM
        meats m
     INNER JOIN
        births b
           ON m.date = b.date;"""
>>> joined = pdsql(q)
>>> print joined.head()
                    date    beef  births
403  2012-07-01 00:00:00  2200.8  368450
404  2012-08-01 00:00:00  2367.5  359554
405  2012-09-01 00:00:00  2016.0  361922
406  2012-10-01 00:00:00  2343.7  347625
407  2012-11-01 00:00:00  2206.6  320195

>>> q = "select
           strftime('%Y', date) as year
           , SUM(beef) as beef_total
           FROM
              meat
           GROUP BY
              year;"
>>> print pdsql(q).head()
   year  beef_total
0  1944        8801
1  1945        9936
2  1946        9010
3  1947       10096
4  1948        8766

More information and code samples (by the author of the original version) available in the examples folder or on his blog.