sqldf for pandas
Project description
pysqldf allows you to query pandas DataFrames using SQL syntax. It works similarly to sqldf in R. pysqldf seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or pandas.
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
Documents
SQLDF(env, inmemory=True, udfs={}, udafs={})
env: variable mapping dictionary of sql executed enviroment. key is sql variable name and value is your program variable. locals() or globals() is used for simple assign.
inmemory: sqlite db option.
udfs: dictionary of user defined functions. dictionary key is function name, dictionary value is function. see sqlite3 document
udafs: dictionary of user defined aggregate functions. dictionary key is function name, dictionary value is aggregate function or class. If value is function, function gets one argument that is list of column values and it should return aggregated a value. Another case(value is class), see sqlite3 document.
load_iris(), load_meat(), load_births()
load example DataFrame data.
iris: data description
meat: data description
births: data description
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