Customizing Matplotlib: Configurations and Stylesheets
Matplotlib's default plot settings are often the subject of complaint among its users. While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.
Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer stylesheets feature, which contains some nice sets of default configurations.
Plot Customization by Hand¶
Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default. It's possible to do these customizations for each individual plot. For example, here is a fairly drab default histogram:
import matplotlib.pyplot as plt plt.style.use('classic') import numpy as np %matplotlib inline
x = np.random.randn(1000) plt.hist(x);
We can adjust this by hand to make it a much more visually pleasing plot:
# use a gray background ax = plt.axes(axisbg='#E6E6E6') ax.set_axisbelow(True) # draw solid white grid lines plt.grid(color='w', linestyle='solid') # hide axis spines for spine in ax.spines.values(): spine.set_visible(False) # hide top and right ticks ax.xaxis.tick_bottom() ax.yaxis.tick_left() # lighten ticks and labels ax.tick_params(colors='gray', direction='out') for tick in ax.get_xticklabels(): tick.set_color('gray') for tick in ax.get_yticklabels(): tick.set_color('gray') # control face and edge color of histogram ax.hist(x, edgecolor='#E6E6E6', color='#EE6666');
This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package. But this took a whole lot of effort! We definitely do not want to have to do all that tweaking each time we create a plot. Fortunately, there is a way to adjust these defaults once in a way that will work for all plots.
Changing the Defaults:
Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.
This configuration can be adjusted at any time using the
plt.rc convenience routine.
Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.
We'll start by saving a copy of the current
rcParams dictionary, so we can easily reset these changes in the current session:
IPython_default = plt.rcParams.copy()
Now we can use the
plt.rc function to change some of these settings:
from matplotlib import cycler colors = cycler('color', ['#EE6666', '#3388BB', '#9988DD', '#EECC55', '#88BB44', '#FFBBBB']) plt.rc('axes', facecolor='#E6E6E6', edgecolor='none', axisbelow=True, grid=True, prop_cycle=colors) plt.rc('grid', color='w', linestyle='solid') plt.rc('xtick', direction='out', color='gray') plt.rc('ytick', direction='out', color='gray') plt.rc('patch', edgecolor='#E6E6E6') plt.rc('lines', linewidth=2)
With these settings defined, we can now create a plot and see our settings in action:
Let's see what simple line plots look like with these rc parameters:
for i in range(4): plt.plot(np.random.rand(10))
I find this much more aesthetically pleasing than the default styling. If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes! These settings can be saved in a .matplotlibrc file, which you can read about in the Matplotlib documentation. That said, I prefer to customize Matplotlib using its stylesheets instead.
The version 1.4 release of Matplotlib in August 2014 added a very convenient
style module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the .matplotlibrc files mentioned earlier, but must be named with a .mplstyle extension.
Even if you don't create your own style, the stylesheets included by default are extremely useful.
The available styles are listed in
plt.style.available—here I'll list only the first five for brevity:
['fivethirtyeight', 'seaborn-pastel', 'seaborn-whitegrid', 'ggplot', 'grayscale']
The basic way to switch to a stylesheet is to call
But keep in mind that this will change the style for the rest of the session! Alternatively, you can use the style context manager, which sets a style temporarily:
with plt.style.context('stylename'): make_a_plot()
Let's create a function that will make two basic types of plot:
def hist_and_lines(): np.random.seed(0) fig, ax = plt.subplots(1, 2, figsize=(11, 4)) ax.hist(np.random.randn(1000)) for i in range(3): ax.plot(np.random.rand(10)) ax.legend(['a', 'b', 'c'], loc='lower left')
We'll use this to explore how these plots look using the various built-in styles.
The default style is what we've been seeing so far throughout the book; we'll start with that. First, let's reset our runtime configuration to the notebook default:
# reset rcParams plt.rcParams.update(IPython_default);
Now let's see how it looks:
fivethirtyeight style mimics the graphics found on the popular FiveThirtyEight website.
As you can see here, it is typified by bold colors, thick lines, and transparent axes:
with plt.style.context('fivethirtyeight'): hist_and_lines()
ggplot package in the R language is a very popular visualization tool.
ggplot style mimics the default styles from that package:
with plt.style.context('ggplot'): hist_and_lines()
*Bayesian Methods for Hackers( style¶
There is a very nice short online book called Probabilistic Programming and Bayesian Methods for Hackers; it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.
This style is reproduced in the
with plt.style.context('bmh'): hist_and_lines()
For figures used within presentations, it is often useful to have a dark rather than light background.
dark_background style provides this:
with plt.style.context('dark_background'): hist_and_lines()
Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.
For this, the
grayscale style, shown here, can be very useful:
with plt.style.context('grayscale'): hist_and_lines()
Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in Visualization With Seaborn). As we will see, these styles are loaded automatically when Seaborn is imported into a notebook. I've found these settings to be very nice, and tend to use them as defaults in my own data exploration.
import seaborn hist_and_lines()
With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication. Throughout this book, I will generally use one or more of these style conventions when creating plots.