pandasql 0.3.1

sqldf for pandas

pandasql
========

pandasql allows you to query pandas DataFrames using SQL syntax. It works similarly to sqldf in R. pandasql seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or pandas.

Installation
===========
.. code:: python
$ pip install -U pandasql

Bascis
===========
The main function used in pandasql is sqldf. sqldf accepts 2 parametrs
- a sql query string
- an set of session/environment variables (locals() or globals())

Specifying locals() or globals() can get tedious. You can defined a short helper function to fix this.

.. code:: python
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())

Querying
===========
pandasql uses <a href="http://www.sqlite.org/lang.html">SQLite syntax</a>. Any pandas dataframes will be automatically detected by pandasql. You can query them as you would any regular SQL table.

.. code:: python
>>> from pandasql import sqldf, load_meat, load_births
>>> pysqldf = lambda q: sqldf(q, globals())
>>> meat = load_meat()
>>> births = load_births()
>>> print pysqldf("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

.. code:: python
>>> q = """SELECT
m.date, m.beef, b.births
FROM
meats m
INNER JOIN
births b
ON m.date = b.date;"""
>>> joined = pyqldf(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 pysqldf(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 available in the [examples](https://github.com/yhat/pandasql/blob/master/examples/demo.py) folder or on [our blog](http://blog.yhathq.com/posts/pandasql-sql-for-pandas-dataframes.html).