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).
[![Analytics](https://ga-beacon.appspot.com/UA-46996803-1/pandasql/README.md)](https://github.com/yhat/pandasql)
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sqldf for pandas
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pandas 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.
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pandasql-master.zip (19个子文件)
萝莉酱.jpeg 120KB
pandasql-master
pandasql
__init__.py 417B
data
births_by_month.csv 11KB
meat.csv 61KB
births.csv 1KB
tests
__init__.py 0B
test_utils.py 1KB
test_pandasql.py 7KB
sqldf.py 6KB
setup.py 580B
LICENSE.txt 1KB
README.rst 3KB
examples
demo.py 2KB
CHANGES.txt 867B
MANIFEST.in 61B
.gitignore 314B
release.sh 95B
README.md 3KB
AUTHORS.md 111B
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