Metadata-Version: 1.0
Name: ggplot
Version: 0.2.8
Summary: ggplot for python
Home-page: https://github.com/yhat/ggplot/
Author: Greg Lamp
Author-email: greg@yhathq.com
License: BSD
Description: {ggplot} from `Yhat <http://yhathq.com>`__
        ==========================================
        
        read more on our
        `blog <http://blog.yhathq.com/posts/ggplot-for-python.html>`__
        
        ::
        
            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 port 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
        ``ggplot2``. I've tried other libraries like
        ```bokeh`` <https://github.com/continuumio/bokeh>`__ and
        ```d3py`` <https://github.com/mikedewar/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``
        -  never use matplotlib again
        -  ability to use both American and British English spellings of
           aesthetics
        -  tight integration with
           ```pandas`` <https://github.com/pydata/pandas>`__
        -  pip installable
        
        Getting Started
        ~~~~~~~~~~~~~~~
        
        Dependencies
        ^^^^^^^^^^^^
        
        I realize that these are not fun to install. My best luck has always
        been using ``brew`` if you're on a Mac or just using `the
        binaries <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`__ if you're on
        Windows. If you're using Linux then this should be relatively painless.
        You should be able to ``apt-get`` or ``yum`` all of these. -
        ``matplotlib`` - ``pandas`` - ``numpy`` - ``scipy`` - ``statsmodels`` -
        ``patsy``
        
        Installation
        ^^^^^^^^^^^^
        
        Ok the hard part is over. Installing ``ggplot`` is really easy. Just use
        ``pip``! An item on the TODO is to add the matplotlibrc files to the pip
        installable (let me know if you'd like to help!).
        
        ::
        
            # 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(color='red')
        
        ``geom_point``
        ^^^^^^^^^^^^^^
        
        ::
        
            from ggplot import *
            ggplot(diamonds, aes('carat', 'price')) + \
                geom_point(alpha=1/20.) + \
                ylim(0, 20000)
        
        ``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()
        
        ::
        
            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()
        
        ``geom_bar``
        ^^^^^^^^^^^^
        
        ::
        
            p = ggplot(mtcars, aes('factor(cyl)'))
            p + geom_bar()
        
        TODO
        ~~~~
        
        The list is long, but
        distinguished.\ `TODO <https://github.com/yhat/ggplot/blob/master/TODO.md>`__
        
Platform: UNKNOWN
