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DISCLAIMER

This project is not maintained. It is merely a fork of `yhat/pandasql <https://github.com/yhat/pandasql>`__ and all credit goes to the group. This fork just resolves an issue of compatibility with SQLAlchemy v2.x.x. A PR was requested for this to be included in the main pandasql project but it seems to be dormant. This sparked the creation of this fork.

pansql

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

Installation

$ pip install -U pansql

Basics

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

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

from pansql import sqldf
pysqldf = lambda q: sqldf(q, globals())

Querying

pansql uses SQLite syntax. Any pandas dataframes will be automatically detected by pansql. You can query them as you would any regular SQL table.

$ python
>>> from pansql 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 folder or on our blog.

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