pandasql3-0.7.3.tar.gz

pandasql3 ======== `pandasql3` allows you to query `pandas` DataFrames using SQL syntax. It works similarly to `sqldf` in R. `pandasql3` seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or `pandas`. #### Installation ``` $ pip install -U pandasql3 ``` #### Basics The main function used in pandasql3 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 pandasql3 import sqldf pysqldf = lambda q: sqldf(q, globals()) #### Querying `pandasql3` uses [SQLite syntax](http://www.sqlite.org/lang.html). Any `pandas` dataframes will be automatically detected by `pandasql3`. You can query them as you would any regular SQL table. ``` $ python >>> from pandasql3 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 ```