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()
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
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
ax = plt.axes() ax.set_facecolor("orange") # OR ax.set(facecolor = "orange") plt.scatter(TMIN, PRCP) plt.show()
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()
Now let's see what happens when we adjust the alpha of the axes subplot down to
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()
Notice that the background of the plot itself is transparent now.
Add Image to Plot Background in Matplotlib
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
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()
extent argument takes in additional arguments in this order:
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.
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 book on Data Visualization in Python.
Data Visualization in Python, a book for beginner to intermediate Python developers, will guide 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.