The code for plotting graphs is considered the frontend in Matplotlib terminology. We first mentioned backends in Chapter 6, Hello Plotting World!, when we were talking about output formats. In reality, Matplotlib backends differ much more than just in the support of graphical formats. Backends handle so many things behind the scenes! And that determines the support for plotting capabilities. For example, LaTeX text layout is only supported by AGG, PDF, PGF, and PS backends.

We have been using non-interactive backends so far, which include AGG, Cairo, GDK, PDF, PGF, PS, and SVG. Most of these backends work without extra dependencies, yet Cairo and GDK would require the Cairo graphics library or GIMP Drawing Kit, respectively, to work.

Non-interactive backends can be further classified into two groups--vector and raster. Vector graphics describe images in terms of points, paths, and shapes that are calculated using mathematical formulas. A vector graphic will always appear smooth, irrespective of scale and its size is usually much smaller than its raster counterpart. PDF, PGF, PS, and SVG backends belong to the "vector" group.

Raster graphics describe images in terms of a finite number of tiny color blocks (pixels). So, if we zoom in enough, we start to see a blurry image, or in other words, pixelation. By increasing the resolution or Dots Per Inch (DPI) of the image, we are less likely to observe pixelation. AGG, Cairo, and GDK belong to this group of backends. This table summarizes the key functionalities and differences among the non-interactive backends:

Normally, we don't need to manually select a backend, as the default choice would work great for most tasks. On the other hand, we can specify a backend through the matplotlib.use() method before importing matplotlib.pyplot:

import matplotlib
matplotlib.use('SVG') # Change to SVG backend
import matplotlib.pyplot as plt
import textwrap # Standard library for text wraping


# Create a figure
fig, ax = plt.subplots(figsize=(6,7))

# Create a list of x ticks positions
ind = range(df.shape[0])

# Plot a bar chart of median usual weekly earnings by educational
# attainments
rects = ax.barh(ind, df["Median usual weekly earnings ($)"], height=0.5)

# Set the x-axis label
ax.set_xlabel('Median weekly earnings (USD)')

# Label the x ticks
# The tick labels are a bit too long, let's wrap them in 15-char lines
ylabels=[textwrap.fill(label,15) for label in df["Educational attainment"]]
ax.set_yticks(ind)
ax.set_yticklabels(ylabels)

# Give extra margin at the bottom to display the tick labels
fig.subplots_adjust(left=0.3)

# Save the figure in SVG format
plt.savefig("test.svg")