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from ggplot import *
ggplot(aes(x='date', y='beef'), data=meat) + \
    geom_point(color='lightblue') + \
    stat_smooth(span=.15, color='black', se=True) + \
    ggtitle("Beef: It's What's for Dinner") + \
    xlab("Date") + \
    ylab("Head of Cattle Slaughtered")

Yes, it's another port of ggplot2. One of the biggest reasons why I continue to reach for R instead of Python for data analysis is the lack of an easy to use, high level plotting package like ggplot2. I've tried other libraries like bokeh and d3py but what I really want is ggplot2.

ggplot is just that. It's an extremely un-pythonic package for doing exactly what ggplot2 does. The goal of the package is to mimic the ggplot2 API. This makes it super easy for people coming over from R to use, and prevents you from having to re-learn how to plot stuff.

• same API as ggplot2 for R
• ability to use both American and British English spellings of aesthetics
• tight integration with pandas
• pip installable

This package depends on the following packages, although they should be automatically installed if you use pip:

• matplotlib
• pandas
• numpy
• scipy
• statsmodels
• patsy

Installing ggplot is really easy. Just use pip!

$ pip install ggplot

# run an IPython shell (or don't)
$ ipython
In [1]: from ggplot import *

That's it! You're ready to go!

meat_lng = pd.melt(meat[['date', 'beef', 'pork', 'broilers']], id_vars='date')
ggplot(aes(x='date', y='value', colour='variable'), data=meat_lng) + \
    geom_point() + \
    stat_smooth(color='red')

from ggplot import *
ggplot(diamonds, aes('carat', 'price')) + \
    geom_point(alpha=1/20.) + \
    ylim(0, 20000)

p = ggplot(aes(x='carat'), data=diamonds)
p + geom_histogram() + ggtitle("Histogram of Diamond Carats") + labs("Carats", "Freq")

ggplot(diamonds, aes(x='price', color='cut')) + \
    geom_density()

meat_lng = pd.melt(meat[['date', 'beef', 'broilers', 'pork']], id_vars=['date'])
p = ggplot(aes(x='value', colour='variable', fill=True, alpha=0.3), data=meat_lng)
p + geom_density()

p = ggplot(mtcars, aes('factor(cyl)'))
p + geom_bar()

To generate image test data...

In the base dir, run the tests with python tests.py, afterwards run python visual_tests.py (opens a page in a browser) and compare the plots/ make sure they look as the test intended.

Then copy the missing files from result_images/test_whatever/.png to ggplot/tests/test_whatever/.png. Make sure that you DON'T copy images with filenames ending in -expected.png, as these are the copies from ggplot/tests/test_/*.png which the test images get compared to.

The list is long, but distinguished. We're looking for contributors! Email greg at yhathq.com for more info. For getting started with contributing, check out these docs