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
^^^^^^^^^^^^
::
$ pip install -U pandasql
Basics
^^^^^^
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.
::
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())
Querying
^^^^^^^^
pandasql
uses SQLite syntax <http://www.sqlite.org/lang.html>
__.
Any pandas
dataframes will be automatically detected by
pandasql
. You can query them as you would any regular SQL table.
::
$ 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
::
>>> 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>
__.
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