How to Change Plot Background in Matplotlib - Stack Abuse

How to Change Plot Background in Matplotlib

Introduction

Matplotlib is one of the most widely used data visualization libraries in Python. From simple to complex visualizations, it's the go-to library for most.

In this tutorial, we'll take a look at how to change the background of a plot in Matplotlib.

Importing Data and Libraries

Let's import the required libraries first. We'll obviously need Matplotlib, and we'll use Pandas to read the data:

import matplotlib.pyplot as plt
import pandas as pd

Specifically, we'll be using the Seattle Weather Dataset:

weather_data = pd.read_csv("seattleWeather.csv")
print(weather_data.head())
         DATE  PRCP  TMAX  TMIN  RAIN
0  1948-01-01  0.47    51    42  True
1  1948-01-02  0.59    45    36  True
2  1948-01-03  0.42    45    35  True
3  1948-01-04  0.31    45    34  True
4  1948-01-05  0.17    45    32  True

Creating a Plot

Now, let's create a simple Matplotlib Scatterplot, with a few different variables we want to visualize:

PRCP = weather_data['PRCP']
TMAX = weather_data['TMAX']
TMIN = weather_data['TMIN']

Now, we'll construct a scatter plot between the minimum temperature and precipitation and show() it using Matplotlib's PyPlot:

plt.scatter(TMIN, PRCP)
plt.show()

matplotlib scatter plot

The graph that we have produced is interpretable, but it is looking a little plain. Let's try customizing it. We want to customize the background of our plot using a couple of different methods.

Change Plot Background in Matplotlib

Now, let's go ahead and change the background of this plot. We can do this with two different approaches. We can change the color of the face, which is currently set to white. Or, we can input a picture using imshow().

Change Axes Background in Matplotlib

Let's first change the color of the face. This can either be done with the set() function, passing in the face argument and its new value, or via the dedicated set_facecolor() function:

ax = plt.axes()
ax.set_facecolor("orange")
# OR
ax.set(facecolor = "orange")

plt.scatter(TMIN, PRCP)
plt.show()

change axes background color matplotlib

Either of these approaches produces the same result, as they both call on the same function under the hood.

Change Figure Background in Matplotlib

If you would like to set the background for the figure and need an axes to be transparent, this can be done with the set_alpha() argument when you create the figure. Let's create a figure and an axes object. Of course, you can also use the set() function, and pass the alpha attribute instead.

The color of the entire figure will be blue and we will initially set the alpha of the axes object to 1.0, meaning fully opaque. We color the axes object orange, giving us an orange background within the blue figure:

fig = plt.figure()
fig.patch.set_facecolor('blue')
fig.patch.set_alpha(0.6)

ax = fig.add_subplot(111)
ax.patch.set_facecolor('orange')
ax.patch.set_alpha(1.0)

plt.scatter(TMIN, PRCP)
plt.show()

change figure background matplotlib

Now let's see what happens when we adjust the alpha of the axes subplot down to 0.0:

fig = plt.figure()
fig.patch.set_facecolor('blue')
fig.patch.set_alpha(0.6)

ax = fig.add_subplot(111)
ax.patch.set_facecolor('orange')
ax.patch.set_alpha(0.0)

plt.scatter(TMIN, PRCP)
plt.show()

change axes background matplotlib within figure

Notice that the background of the plot itself is transparent now.

Add Image to Plot Background in Matplotlib

Better understand your data with visualizations

  •  30-day no-questions refunds
  •  Beginner to Advanced
  •  Updated regularly (update June 2021)
  •  New bonus resources and guides

If you would like to use an image as the background for a plot, this can be done by using PyPlot's imread() function. This function loads an image into Matplotlib, which can be displayed with the function imshow().

In order to plot on top of the image, the extent of the image has to be specified. By default, Matplotlib uses the upper left corner of the image as the image's origin. We can give a list of points to the imshow() function, specifying what region of the image should be displayed. When combined with subplots, another plot can be inserted on top of the image.

Let's use an image of rain as the background for our plot:

img = plt.imread("rain.jpg")
fig, ax = plt.subplots()
ax.imshow(img, extent=[-5, 80, -5, 30])
ax.scatter(TMIN, PRCP, color="#ebb734")
plt.show()

adding image to background matplotlib

The extent argument takes in additional arguments in this order: horizontal_min, horizontal_max, vertical_min, vertical_max).

Here, we've read the image, cropped it and showed it on the axes using imshow(). Then, we've plotted the scatter plot with a different color and shown the plot.

Conclusion

In this tutorial, we've gone over several ways to change a background of a plot using Python and Matplotlib.

If you're interested in Data Visualization and don't know where to start, make sure to check out our bundle of books on Data Visualization in Python:

Data Visualization in Python with Matplotlib and Pandas is a book designed to take absolute beginners to Pandas and Matplotlib, with basic Python knowledge, and allow them to build a strong foundation for advanced work with theses libraries - from simple plots to animated 3D plots with interactive buttons.

It serves as an in-depth, guide that'll teach you everything you need to know about Pandas and Matplotlib, including how to construct plot types that aren't built into the library itself.

Data Visualization in Python, a book for beginner to intermediate Python developers, guides you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair. More specifically, over the span of 11 chapters this book covers 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy.

It serves as a unique, practical guide to Data Visualization, in a plethora of tools you might use in your career.

Last Updated: April 12th, 2021

Improve your dev skills!

Get tutorials, guides, and dev jobs in your inbox.

No spam ever. Unsubscribe at any time. Read our Privacy Policy.

Dan NelsonAuthor

Aspiring data scientist and writer. BS in Communications. I hope to use my multiple talents and skillsets to teach others about the transformative power of computer programming and data science.

Want a remote job?

    Prepping for an interview?

    • Improve your skills by solving one coding problem every day
    • Get the solutions the next morning via email
    • Practice on actual problems asked by top companies, like:
     
     
     

    Better understand your data with visualizations

    •  30-day no-questions refunds
    •  Beginner to Advanced
    •  Updated regularly (update June 2021)
    •  New bonus resources and guides

    © 2013-2021 Stack Abuse. All rights reserved.