pandasql3

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 <https://github.com/yhat/pandasql/blob/master/examples/demo.py>__ folder or on his blog <http://blog.yhathq.com/posts/pandasql-sql-for-pandas-dataframes.html>__.