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    "info": {
        "author": "Ryoji Ishii",
        "author_email": "airtoxin@icloud.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: SQL",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Utilities"
        ],
        "description": "pysqldf\n=======\n\n|Build Status| |PyPI Version| |PyPI Monthly Downloads| |PyPI License|\n\n``pysqldf`` allows you to query ``pandas`` DataFrames using SQL syntax.\nIt works similarly to ``sqldf`` in R. ``pysqldf`` seeks to provide a\nmore familiar way of manipulating and cleaning data for people new to\nPython or ``pandas``.\n\nInstallation\n------------\n\n``$ pip install pysqldf``\n\nBasics\n------\n\nThe main class in pysqldf is ``SQLDF``. ``SQLDF`` accepts 1 enviroment\nvariable sets or more parametrs in constructor. - an set of\nsession/environment variables (dictionary of valiables, ``locals()`` or\n``globals()``) - temporary file type - user defined functions - user\ndefined aggregate functions\n\n``pysqldf`` uses `SQLite syntax <http://www.sqlite.org/lang.html>`__.\nAny convertable data to ``pandas`` DataFrames will be automatically\ndetected by ``pysqldf``. You can query them as you would any regular SQL\ntable.\n\n.. code:: python\n\n    $ python\n    >>> from pysqldf import SQLDF, load_meat, load_births\n    >>> sqldf = SQLDF(globals())\n    >>> meat = load_meat()\n    >>> births = load_births()\n    >>> print sqldf.execute(\"SELECT * FROM meat LIMIT 10;\").head()\n                      date  beef  veal  pork  lamb_and_mutton broilers other_chicken turkey\n    0  1944-01-01 00:00:00   751    85  1280               89     None          None   None\n    1  1944-02-01 00:00:00   713    77  1169               72     None          None   None\n    2  1944-03-01 00:00:00   741    90  1128               75     None          None   None\n    3  1944-04-01 00:00:00   650    89   978               66     None          None   None\n    4  1944-05-01 00:00:00   681   106  1029               78     None          None   None\n\n    >>> q = \"SELECT m.date, m.beef, b.births FROM meat m INNER JOIN births b ON m.date = b.date;\"\n    >>> print sqldf.execute(q).head()\n                        date    beef  births\n    403  2012-07-01 00:00:00  2200.8  368450\n    404  2012-08-01 00:00:00  2367.5  359554\n    405  2012-09-01 00:00:00  2016.0  361922\n    406  2012-10-01 00:00:00  2343.7  347625\n    407  2012-11-01 00:00:00  2206.6  320195\n\n    >>> q = \"SELECT strftime('%Y', date) AS year, SUM(beef) AS beef_total FROM meat GROUP BY year;\"\n    >>> print sqldf.execute(q).head()\n       year  beef_total\n    0  1944        8801\n    1  1945        9936\n    2  1946        9010\n    3  1947       10096\n    4  1948        8766\n\nuser defined functions and user defined aggregate functions also\nsupported.\n\n.. code:: python\n\n    $ python\n    >>> from pysqldf import SQLDF, load_iris\n    >>> import math\n    >>> import numpy\n    >>> ceil = lambda x: math.ceil(x)\n    >>> udfs = { \"ceil\": lambda x: math.ceil(x) }\n    >>> udafs = { \"variance\": lambda values: numpy.var(values) }\n    >>> # or you can also define aggregation function as class\n    >>> # class variance(object):\n    ... #     def __init__(self):\n    ... #         self.a = []\n    ... #     def step(self, x):\n    ... #         self.a.append(x)\n    ... #     def finalize(self):\n    ... #         return numpy.var(self.a)\n    ...\n    >>> # udafs={ \"variance\": variance }\n    >>> iris = load_iris()\n    >>> sqldf = SQLDF(globals(), udfs=udfs, udafs=udafs)\n    >>> sqldf.execute(\"\"\"\n        SELECT\n            ceil(sepal_length) AS sepal_length,\n            ceil(sepal_width) AS sepal_width,\n            ceil(petal_length) AS petal_length,\n            ceil(petal_width) AS petal_width,\n            species\n        FROM iris;\n        \"\"\").head()\n       sepal_length  sepal_width  petal_length  petal_width      species\n    0             6            4             2            1  Iris-setosa\n    1             5            3             2            1  Iris-setosa\n    2             5            4             2            1  Iris-setosa\n    3             5            4             2            1  Iris-setosa\n    4             5            4             2            1  Iris-setosa\n    >>> sqldf.execute(\"SELECT species, variance(sepal_width) AS var FROM iris GROUP BY species;\")\n               species       var\n    0      Iris-setosa  0.142276\n    1  Iris-versicolor  0.096500\n    2   Iris-virginica  0.101924\n\nDocuments\n---------\n\n``SQLDF(env, inmemory=True, udfs={}, udafs={})``\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n``env``: variable mapping dictionary of sql executed enviroment. key is\nsql variable name and value is your program variable. ``locals()`` or\n``globals()`` is used for simple assign.\n\n``inmemory``: sqlite db option.\n\n``udfs``: dictionary of user defined functions. dictionary key is\nfunction name, dictionary value is function. see `sqlite3\ndocument <https://docs.python.org/2.7/library/sqlite3.html#sqlite3.Connection.create_function>`__\n\n``udafs``: dictionary of user defined aggregate functions. dictionary\nkey is function name, dictionary value is aggregate function or class.\nIf value is function, function gets one argument that is list of column\nvalues and it should return aggregated a value. Another case(value is\nclass), see `sqlite3\ndocument <https://docs.python.org/2.7/library/sqlite3.html#sqlite3.Connection.create_aggregate>`__.\n\n``load_iris()``, ``load_meat()``, ``load_births()``\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nload example DataFrame data.\n\n-  iris: `data\n   description <https://archive.ics.uci.edu/ml/datasets/Iris>`__\n-  meat: `data\n   description <http://www.ers.usda.gov/data-products/livestock-meat-domestic-data.aspx>`__\n-  births: `data\n   description <http://data.un.org/Data.aspx?d=POP&f=tableCode:55>`__\n\n.. |Build Status| image:: https://travis-ci.org/airtoxin/pysqldf.svg\n   :target: https://travis-ci.org/airtoxin/pysqldf\n.. |PyPI Version| image:: https://img.shields.io/pypi/v/pysqldf.svg\n   :target: https://pypi.python.org/pypi/pysqldf\n.. |PyPI Monthly Downloads| image:: https://img.shields.io/pypi/dm/pysqldf.svg\n   :target: https://pypi.python.org/pypi/pysqldf\n.. |PyPI License| image:: https://img.shields.io/pypi/l/pysqldf.svg\n   :target: https://pypi.python.org/pypi/pysqldf",
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