ggplot 0.1.8

ggplot for python

from ggplot import *

ggplot(aes(x='date', y='beef'), data=meat) + \
    geom_point(color='lightblue') + \
    geom_line(alpha=0.25) + \
    stat_smooth(span=.05, color='black') + \
    ggtitle("Beef: It's What's for Dinner") + \
    xlab("Date") + \
    ylab("Head of Cattle Slaughtered")

What is it?

Yes, it’s another implementation of `ggplot2 <https://github.com/hadley/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 ggplot. I’ve tried other libraries like Bockah 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.

Goals

• same API as ggplot2 for R

• tight integration with `pandas <https://github.com/pydata/pandas>`__

• pip installable

Getting Started

Dependencies

• matplotlib

• pandas

• numpy

• scipy

• statsmodels

Installation

# matplotlibrc from Huy Nguyen (http://www.huyng.com/posts/sane-color-scheme-for-matplotlib/)
$ curl https://github.com/yhat/ggplot/raw/master/matplotlibrc.zip > matplotlibrc.zip
$ unzip matplotlibrc.zip -d ~/
# install ggplot using pip
$ pip install ggplot

Loading ggplot

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

That’s it! You’re ready to go!

Examples

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()

geom_point

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

geom_hist

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

geom_density

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

geom_bar

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

TODO

• finish README

• add matplotlibrc to build script

• distribute on PyPi (DONE)

• come up with better name (DONE)

• handle NAs gracefully (DONE)

• make aes guess what the user actually means (DONE)

• aes:

  • size

  • se for stat_smooth (DONE)

  • fix fill/colour (color DONE)

• geoms:

  • geom_abline (DONE)

  • geom_area (DONE)

  • geom_bar (IN PROGRESS)

  • geom_boxplot

  • geom_hline (DONE)

  • geom_ribbon (same as geom_ribbon?)

  • geom_vline (DONE)

  • stat_bin2d (DONE)

  • geom_jitter

  • stat_smooth (bug)

• scales:

  • scale_colour_brewer

  • scale_colour_gradient

  • scale_colour_gradient2

  • scale_x_continuous

  • scale_x_discrete

  • scale_y_continuous

• facets:

  • facet_grid (DONE)