| Name | pandasql JSON |
| Version |
0.7.3
JSON |
| download |
| home_page | https://github.com/yhat/pandasql/ |
| Summary | sqldf for pandas |
| upload_time | 2016-04-20 21:52:47 |
| maintainer | None |
| docs_url | None |
| author | Greg Lamp |
| requires_python | None |
| license | Copyright (c) 2013 Yhat, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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|
pandasql
========
``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
^^^^^^
The main function used in pandasql is ``sqldf``. ``sqldf`` accepts 2
parametrs - a sql query string - an set of session/environment variables
(``locals()`` or ``globals()``)
Specifying ``locals()`` or ``globals()`` can get tedious. You can
defined a short helper function to fix this.
::
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())
Querying
^^^^^^^^
``pandasql`` uses `SQLite syntax <http://www.sqlite.org/lang.html>`__.
Any ``pandas`` dataframes will be automatically detected by
``pandasql``. You can query them as you would any regular SQL table.
::
$ python
>>> from pandasql 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 <https://github.com/yhat/pandasql/blob/master/examples/demo.py>`__
folder or on `our
blog <http://blog.yhathq.com/posts/pandasql-sql-for-pandas-dataframes.html>`__.
|Analytics|
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:target: https://github.com/yhat/pandasql
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"description": "pandasql\n========\n\n``pandasql`` allows you to query ``pandas`` DataFrames using SQL syntax.\nIt works similarly to ``sqldf`` in R. ``pandasql`` seeks to provide a\nmore familiar way of manipulating and cleaning data for people new to\nPython or ``pandas``.\n\nInstallation\n^^^^^^^^^^^^\n\n::\n\n $ pip install -U pandasql\n\nBasics\n^^^^^^\n\nThe main function used in pandasql is ``sqldf``. ``sqldf`` accepts 2\nparametrs - a sql query string - an set of session/environment variables\n(``locals()`` or ``globals()``)\n\nSpecifying ``locals()`` or ``globals()`` can get tedious. You can\ndefined a short helper function to fix this.\n\n::\n\n from pandasql import sqldf\n pysqldf = lambda q: sqldf(q, globals())\n\nQuerying\n^^^^^^^^\n\n``pandasql`` uses `SQLite syntax <http://www.sqlite.org/lang.html>`__.\nAny ``pandas`` dataframes will be automatically detected by\n``pandasql``. You can query them as you would any regular SQL table.\n\n::\n\n $ python\n >>> from pandasql import sqldf, load_meat, load_births\n >>> pysqldf = lambda q: sqldf(q, globals())\n >>> meat = load_meat()\n >>> births = load_births()\n >>> print pysqldf(\"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\njoins and aggregations are also supported\n\n::\n\n >>> q = \"\"\"SELECT\n m.date, m.beef, b.births\n FROM\n meats m\n INNER JOIN\n births b\n ON m.date = b.date;\"\"\"\n >>> joined = pyqldf(q)\n >>> print joined.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\n strftime('%Y', date) as year\n , SUM(beef) as beef_total\n FROM\n meat\n GROUP BY\n year;\"\n >>> print pysqldf(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\nMore information and code samples available in the\n`examples <https://github.com/yhat/pandasql/blob/master/examples/demo.py>`__\nfolder or on `our\nblog <http://blog.yhathq.com/posts/pandasql-sql-for-pandas-dataframes.html>`__.\n\n|Analytics|\n\n.. |Analytics| image:: https://ga-beacon.appspot.com/UA-46996803-1/pandasql/README.md\n :target: https://github.com/yhat/pandasql",
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