# Daily Archives: August 29, 2017

## Visual Communication in Python: Pie Charts with Matplotlib

A personal story about how I started using Python for my research: when I was a post-doc at Microsoft, I was embarrassed to ask them to buy me Matlab. But I knew how to plot things in Matlab and I didn’t have time to learn how to make a graphic look nice with Excel or whatever the preferred Microsoft tool was at the time. Matplotlib to the rescue. It was free, it looked *better* than Matlab, and then it was done.

As readers of this blog may know, I have come to use Python extensive in my research by now. But one thing that I have not changed in the 10 years since that post-doc experience is using matplotlib like it was Matlab. It might be time to change.

I recently read a short blog on the modern approach to using Matplotlib, http://pbpython.com/effective-matplotlib.html, and it seems worth a try. Do you remember a talk on data visualization I gave last fall? https://github.com/aflaxman/iths-communicating-results-visually-2

I’m going to try remaking the plots I spoke on with my old school mpl and the modern approach. Here is the first, a pie chart.

My old-fashioned way is in a notebook from my talk, and looks like this:

```plt.figure(figsize=(9,8))
plt.subplots_adjust(hspace=.3, right=.8, left=.1)
plt.pie([2,98], labels=['Survived\n2%', 'Died\n98%'], colors=colors, startangle=0)
```

The new way is built up in this notebook here, and ends up being comparable:

```fig, ax = plt.subplots(figsize=(9,8))
s.plot(kind='pie', colors=colors, startangle=0)
fig.subplots_adjust(hspace=.3, right=.8, left=.1)
ax.set_ylabel('')
```

Is that cooler? I’m not convinced, but I’ll keep trying.

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