Using style sheets to format charts consistently

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An introduction to using matplotlib in Python to create charts Part six of a six-part series of blogs
If you want to create charts in Python, the chances are that you'll do it using the matplotlib module. This blog will get you started and explain some of its foibles! Getting started with matplotlib Subplots, figures and axes (explicit references) An introduction to formatting matplotlib charts Working with different chart types Saving charts Using style sheets to format charts consistently (this blog)

An introduction to using matplotlib in Python to create charts

Part six of a six-part series of blogs

If you want to create charts in Python, the chances are that you'll do it using the matplotlib module. This blog will get you started and explain some of its foibles!

Getting started with matplotlib
Subplots, figures and axes (explicit references)
An introduction to formatting matplotlib charts
Working with different chart types
Saving charts
Using style sheets to format charts consistently (this blog)

Posted by Andy Brown on 02 June 2025

In this blog

Using existing styles
Creating a custom style
Getting help for writing styles

To avoid re-inventing wheels I'd strongly recommend creating styles to manage your Python chart fromatting.

This recommendation assumes that you are going to be working with charts a fair bit. It isn't worth investing in learning styles if you're just going to be creating a few ad hoc charts.

Using existing styles

Styles make a big difference to a chart. Consider these two examples:

Two identical charts, except that the bottom one has had the ggplot style applied to it.

Here's how to apply a style like this:

import numpy as np

import matplotlib.pyplot as plt

# Wikipedia list of most populous countries in the world

countries = ["India","China","United States","Indonesia",

"Pakistan","Nigeria","Brazil"]

populations = [1413,1408,340,282,241,224,213]

# apply a style

plt.style.use('ggplot')

# create a chart based on these countries amd figures

fig, ax = plt.subplots(figsize = (8,3))

# create and format the bar chart on the left

ax.bar(countries,populations)

ax.set_title("Most populous countries")

ax.set_xlabel("Countries")

ax.set_ylabel("Populations (m)")

# make things fit in better

fig.tight_layout()

plt.show()

You seem to have to apply styles before you do any charting. You can list out all of the built-in styles available like this:

import matplotlib.pyplot as plt

# list out the styles available

print(plt.style.available)

On my laptop, here's the list of styles I get:

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'petroff10', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

The ggplot style is a popular choice because it tries to emulate ggplot, a popualr plotting package for R.

Creating a custom style

It's easy in Python to create a custom matplotlib style. The hard thing (which we'll consider below) is working out what the style should contain!

Here's a typical (if very simple) style sheet you could create:

This simple file (I've called it wise_owl.style, but you could use any file name) will customise the appearance of any chart to which it's applied.

Here's how you could apply this:

import numpy as np

import matplotlib.pyplot as plt

# Wikipedia list of most populous countries in the world

countries = ["India","China","United States","Indonesia",

"Pakistan","Nigeria","Brazil"]

populations = [1413,1408,340,282,241,224,213]

# apply custom Wise Owl style

plt.style.use('c:/wiseowl/wise_owl.style')

# create a chart based on these countries amd figures

fig, ax = plt.subplots(figsize = (8,3))

# create and format the bar chart on the left

ax.bar(countries,populations)

ax.set_title("Most populous countries")

ax.set_xlabel("Countries")

ax.set_ylabel("Populations (m)")

# make things fit in better

fig.tight_layout()

plt.show()

And here's what this code would produce:

The figure background is pink, and the chart title is in bold, dark red and slightly bigger.

So creating and applying matplotlib styles is easy - but what should they contain?

Getting help for writing styles

The easiest way to create styles is to copy code from the matplotlib template containing all of the possible formatting instructions that you can use. To see where this is, run this code:

import matplotlib

# show the location of the matplotlib

# runtime configuration file

print(matplotlib.matplotlib_fname())

This will give you something like this (I've edited this a bit to preserve confidentiality):

C:\Users\olly.owl\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbzdf2kfra8p0\LocalCache\local-packages\Python313\site-packages\matplotlib\mpl-data\matplotlibrc

You can then paste this into your Explorer address bar:

Paste in the full file path and name.

This will let you edit this in any application, such as Visual Studio Code:

Your list of applications will be different to this (and probably shorter!).

You can then copy any of the 814 lines (for my version) from this file to create your own customised style sheet.

Don't forget to remove the # symbol from the start of each line that you want to include. You can also add your own comments, as I've done in the example earlier on this page.

Parts of this blog
Getting started with matplotlib Subplots, figures and axes (explicit references) An introduction to formatting matplotlib charts Working with different chart types Saving charts Using style sheets to format charts consistently (this blog)

Some other pages relevant to the above blogs include:

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