Skip to main content

sqldf for pandas

Project description

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

Bascis

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. Any pandas dataframes will be automatically detected by pandasql. You can query them as you would any regular SQL table.

>>> 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 our blog.

Analytics

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pandasql-0.4.0.tar.gz (24.2 kB view details)

Uploaded Source

Built Distribution

pandasql-0.4.0-py2.7.egg (27.2 kB view details)

Uploaded Egg

File details

Details for the file pandasql-0.4.0.tar.gz.

File metadata

  • Download URL: pandasql-0.4.0.tar.gz
  • Upload date:
  • Size: 24.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pandasql-0.4.0.tar.gz
Algorithm Hash digest
SHA256 ae9ea6cf4698ce868b61702a587bf94199a4058793c80fd3b9e13275cb47ab3b
MD5 15ce5783bd607bbb1d5334db6455b9e0
BLAKE2b-256 6b4921125db7aad2028f8a73b8e21bc641c31dc803950479161b24961ef5dbfb

See more details on using hashes here.

File details

Details for the file pandasql-0.4.0-py2.7.egg.

File metadata

  • Download URL: pandasql-0.4.0-py2.7.egg
  • Upload date:
  • Size: 27.2 kB
  • Tags: Egg
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pandasql-0.4.0-py2.7.egg
Algorithm Hash digest
SHA256 77b986bdebc9da5144bf6fd2a0f579268de6c09c8533326461071414fbf34df0
MD5 5a9f3b857a36c36478047fbf9df550fb
BLAKE2b-256 2e2ec42174a540f796953da2322cc6b83afaffc5e940d800d510c961aa0c2a4a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page