pysqldf 1.2.2

Installation

$ pip install pysqldf

Basics

The main class in pysqldf is SQLDF. SQLDF accepts 1 enviroment variable sets or more parametrs in constructor. - an set of session/environment variables (dictionary of valiables, locals() or globals()) - temporary file type - user defined functions - user defined aggregate functions

pysqldf uses SQLite syntax. Any convertable data to pandas DataFrames will be automatically detected by pysqldf. You can query them as you would any regular SQL table.

$ python
>>> from pysqldf import SQLDF, load_meat, load_births
>>> sqldf = SQLDF(globals())
>>> meat = load_meat()
>>> births = load_births()
>>> print sqldf.execute("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

>>> q = "SELECT m.date, m.beef, b.births FROM meat m INNER JOIN births b ON m.date = b.date;"
>>> print sqldf.execute(q).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 sqldf.execute(q).head()
   year  beef_total
0  1944        8801
1  1945        9936
2  1946        9010
3  1947       10096
4  1948        8766

user defined functions and user defined aggregate functions also supported.

$ python
>>> from pysqldf import SQLDF, load_iris
>>> import math
>>> import numpy
>>> ceil = lambda x: math.ceil(x)
>>> udfs = { "ceil": lambda x: math.ceil(x) }
>>> udafs = { "variance": lambda values: numpy.var(values) }
>>> # or you can also define aggregation function as class
>>> # class variance(object):
... #     def __init__(self):
... #         self.a = []
... #     def step(self, x):
... #         self.a.append(x)
... #     def finalize(self):
... #         return numpy.var(self.a)
...
>>> # udafs={ "variance": variance }
>>> iris = load_iris()
>>> sqldf = SQLDF(globals(), udfs=udfs, udafs=udafs)
>>> sqldf.execute("""
    SELECT
        ceil(sepal_length) AS sepal_length,
        ceil(sepal_width) AS sepal_width,
        ceil(petal_length) AS petal_length,
        ceil(petal_width) AS petal_width,
        species
    FROM iris;
    """).head()
   sepal_length  sepal_width  petal_length  petal_width      species
0             6            4             2            1  Iris-setosa
1             5            3             2            1  Iris-setosa
2             5            4             2            1  Iris-setosa
3             5            4             2            1  Iris-setosa
4             5            4             2            1  Iris-setosa
>>> sqldf.execute("SELECT species, variance(sepal_width) AS var FROM iris GROUP BY species;")
           species       var
0      Iris-setosa  0.142276
1  Iris-versicolor  0.096500
2   Iris-virginica  0.101924

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