Sandals

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Use SQL to transform pandas_ dataframes.

Unlike pandasql_, sandals does not use sqlite. It manipulates the dataframes directly, avoiding the overhead of moving data to and from sqlite.

This is a work in progress. The goal is to support all examples documented on pandas' comparison with sql__.

.. _pandas: http://pandas.pydata.org/ .. _pandasql: https://github.com/yhat/pandasql .. __: http://pandas.pydata.org/pandas-docs/dev/comparison_with_sql.html

Usage

Some examples:

.. code-block::

import pandas as pd import sandals tips = pd.read_csv("tests/data/tips.csv") sandals.sql("SELECT total_bill, sex FROM tips LIMIT 5", locals()) total_bill sex 0 16.99 Female 1 10.34 Male 2 21.01 Male 3 23.68 Male 4 24.59 Female sandals.sql("SELECT * FROM tips WHERE time = 'Dinner' AND tip > 5.00 LIMIT 3", locals()) total_bill tip sex smoker day time size 23 39.42 7.58 Male No Sat Dinner 4 44 30.40 5.60 Male No Sun Dinner 4 47 32.40 6.00 Male No Sun Dinner 4 sandals.sql("SELECT * FROM tips ORDER BY tips.size DESC, total_bill LIMIT 3", locals()) total_bill tip sex smoker day time size 143 27.05 5.0 Female No Thur Lunch 6 125 29.80 4.2 Female No Thur Lunch 6 141 34.30 6.7 Male No Thur Lunch 6 sandals.sql("SELECT tips.day, AVG(tip), COUNT() FROM tips GROUP BY tips.day;", locals()) tip day day Fri 2.734737 19 Sat 2.993103 87 Sun 3.255132 76 Thur 2.771452 62 sandals.sql("SELECT smoker, tips.day, COUNT(), AVG(tip) FROM tips GROUP BY smoker, tips.day;", locals()) tip smoker smoker day No Fri 2.812500 4 Sat 3.102889 45 Sun 3.167895 57 Thur 2.673778 45 Yes Fri 2.714000 15 Sat 2.875476 42 Sun 3.516842 19 Thur 3.030000 17