import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

Getting set up to export text correctly

If you’re exporting text, you need to make sure matplotlib is exporting editable text, otherwise Illustrator will treat every single character as a shape instead of text. By default matplotlib exports “Type 3 fonts” which Adobe Illustrator doesn’t understand, so you need to change matplotlib to export Type 2/TrueType fonts.

This setting is, for some reason, the number 42. Run this once at the top of your code and you’ll be set for everything else in the script/notebook.

import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42

If you don’t want to type this all of the time (which you shouldn’t), there is a solution! Run the following code from the command line - it creates a matplotlib startup file that will run the above commands every time matplotlib is started.

mkdir -p ~/.matplotlib
echo 'pdf.fonttype: 42' >> ~/.matplotlib/matplotlibrc
echo 'ps.fonttype: 42' >> ~/.matplotlib/matplotlibrc

Reading in our data

df = pd.read_csv("../countries.csv")
df.head()

Exporting

Be sure to read the section above above about exporting text.

After you make your graphic, use plt.savefig("filename.pdf") to save it as a vector-graphic .pdf. Do not save as png or jpg or anythign else. You could save as svg but I’ve found pdf generally works better.

You’ll also need to pass transparent=True when using .savefig to get rid of white backgrounds. It makes your file much easier to work with in Illustrator.

Even though it’s just plt.savefig again and again, I’ve included several examples below.

Example 1, bar chart

Setting linewidth=0 allows you to easily remove the lines around bars when in Illustrator. plt.savefig is the bit that saves your graph.

ax = df.groupby("Continent")['life_expectancy'].median().sort_values().plot(kind='barh', linewidth=0)
ax.set_xlabel("Life expectancy")

# Remember: transparent=True
plt.savefig("output-bargraph.pdf", transparent=True)

Example 2, scatterplot

Here I’ve passed a lot of arguments in to the .scatter method. It’s still plt.savefig to save, though.

Use linewidth=0 to remove the outline of the circles (although it will still be there, invisible, for you to remove in Illustrator).

df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', linewidth=0, xlim=(0, 70000), ylim=(30, 85), figsize=(10, 6))
plt.savefig("output-scatter.pdf", transparent=True)

Example 3, grouped scatterplot

Setting markeredgewidth=0 allows you to easily remove the invisible borders around the circles when in Illustrator. It has the same effect as linewidth=0 in the previous example, but the code is different because we’re directly using matplotlib instead of going through pandas (ax.plot instead of df.plot).

But despite it being crazy complicated: plt.savefig once again.

fig, ax = plt.subplots(figsize=(10,6))
for category, selection in df.groupby('Continent'):
    ax.plot(selection['GDP_per_capita'], selection['life_expectancy'], marker='o', markersize=5, alpha=0.5, linestyle='', label=category, markeredgewidth=0)

ax.set_ylabel("Life expectancy")
ax.set_xlabel("Per capita GDP")
ax.set_ylim((30,85))
ax.legend(loc='lower right')

# Remember: transparent=True
plt.savefig("output-scatter-grouped.pdf", transparent=True)