Skip to main content

sqldf for pandas

Project description

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).

Project details


Release history Release notifications

History Node

0.7.3

History Node

0.7.2

History Node

0.7.1

History Node

0.7.0

History Node

0.6.3

History Node

0.6.2

History Node

0.6.1

History Node

0.6.0

History Node

0.5.1

History Node

0.5.0

History Node

0.4.3

History Node

0.4.2

History Node

0.4.1

History Node

0.4.0

This version
History Node

0.3.1

History Node

0.3.0

History Node

0.2.1

History Node

0.2.0

History Node

0.1.3

History Node

0.1.2

History Node

0.1.1

History Node

0.1.0

History Node

0.0.9

History Node

0.0.8

History Node

0.0.7

History Node

0.0.6

History Node

0.0.5

History Node

0.0.4

History Node

0.0.3

History Node

0.0.2

History Node

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
pandasql-0.3.1-py2.7.egg (26.9 kB) Copy SHA256 hash SHA256 Egg 2.7 May 23, 2013
pandasql-0.3.1.tar.gz (23.3 kB) Copy SHA256 hash SHA256 Source None May 23, 2013

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page