sqldf for pandas
Project description
This repository aims to maintain original pandasql. All credit goes to yhat.
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-lts
Basics
The main function used in pandasql is sqldf. sqldf accepts 2 parametrs - a sql query string - a set of session/environment variables (locals() or globals())
Specifying locals() or globals() can get tedious. You can define a short helper function to fix this.
from pandasql import sqldf pysqldf = lambda q: sqldf(q, globals())
Querying
pandasql uses SQLite syntax. 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 folder or on yhat’s blog.
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