# Announcement Thank you to everyone who has used `prettyplotlib` and made it what it is today! Unfortunately, I no longer have the bandwidth to maintain prettyplotlib. I recommend using [`seaborn`](https://github.com/mwaskom/seaborn). Using `seaborn`, to get the `prettyplotlib` style, do: import seaborn as sns sns.set(style='ticks', palette='Set2') And to remove "chartjunk", do: sns.despine() If you have discrete pull requests, I will accept them, but I personally will no longer fix bugs. If you are a biological scientist looking for ways to analyze your big-ish (20+ samples) data, check out my main project, [`flotilla`](https://github.com/YeoLab/flotilla). [](https://travis-ci.org/olgabot/prettyplotlib) prettyplotlib ============= Python matplotlib-enhancer library which painlessly creates beautiful default `matplotlib` plots. Inspired by [Edward Tufte](http://www.edwardtufte.com/tufte/)'s work on information design and [Cynthia Brewer](http://www.personal.psu.edu/cab38/)'s work on [color perception](http://colorbrewer2.org/). I truly believe that scientific progress is impeded when improper data visualizations are used. I spent a lot of time tweaking my figures to make them more understandable, and realized the scientific world could be a better place if the default parameters for plotting libraries followed recent advances in information design research. And thus `prettyplotlib` was born. Requirements: * [`matplotlib`](http://matplotlib.org/). Can be installed via `pip install matplotlib` or `easy_install matplotlib` * [`brewer2mpl`](https://github.com/jiffyclub/brewer2mpl). Can be installed via `pip install brewer2mpl` or `easy_install brewer2mpl` ## Comparison to `matplotlib`