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https://github.com/yhat/pandasql
sqldf for pandas
https://github.com/yhat/pandasql
Last synced: 20 days ago
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sqldf for pandas
- Host: GitHub
- URL: https://github.com/yhat/pandasql
- Owner: yhat
- License: mit
- Created: 2013-02-18T01:53:56.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2024-04-07T08:26:45.000Z (7 months ago)
- Last Synced: 2024-05-22T11:06:03.079Z (6 months ago)
- Language: Python
- Size: 121 KB
- Stars: 1,305
- Watchers: 49
- Forks: 185
- Open Issues: 61
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.txt
- License: LICENSE.txt
- Authors: AUTHORS.md
Awesome Lists containing this project
- awesome-hacking-lists - yhat/pandasql - sqldf for pandas (Python)
- awesome-python-machine-learning-resources - GitHub - 65% open · ⏱️ 01.02.2017): (数据容器和结构)
README
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
- 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](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).
[](https://github.com/yhat/pandasql)