pandasql3 0.7.3

Description:

pandasql3 0.7.3

pandasql
This is a fork of the original pandasql, with support of multiple SQL
backends and more convenient interface. See below for more info.
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
In addition to the original pandasql’s sqldf function this fork has
a class PandaSQL, which new users are encouraged to use.

sqldf function
The main function used in original pandasql is sqldf. sqldf
accepts one three parameters: - sql query string, - dict of environment
variables (optional, if not specified assumed to be
{**locals(), **globals()}) - database URI in the same format as in
SQLAlchemy (optional, by default use in-memory SQLite database)


PandaSQL class
The class is more convenient when you need to perform multiple queries
(almost always): - first create the class, specifying db_uri if not
default: pdsql = PandaSQL(db_uri) - to execute queries just call
pdsql(query) (environment can also be specified expicitly)

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.