One of the most popular data visualization packages in the R programming language is ggplot2.
To apply ggplot2 styling to a plot created in Matplotlib, you can use the following syntax:
import matplotlib.pyplot as plt
plt.style.use('ggplot')
The following example shows how to use this syntax in practice.
Example: Using ggplot Styles in Matplotlib Plots
Suppose we have a NumPy array with 1,000 values:
import numpy as np
#make this example reproducible.
np.random.seed(1)
#create numpy array with 1000 values that follow normal dist with mean=10 and sd=2
data = np.random.normal(size=1000, loc=10, scale=2)
#view first five values
data[:5]
array([13.24869073, 8.77648717, 8.9436565 , 7.85406276, 11.73081526])
We can use the following code to create a histogram in Matplotlib to visualize the distribution of values in the NumPy array:
import matplotlib.pyplot as plt
#create histogram
plt.hist(data, color='lightgreen', ec='black', bins=15)
To apply ggplot2 styling to this histogram, we can use plt.syle.use(‘ggplot’) as follows:
import matplotlib.pyplot as plt
#specify ggplot2 style
plt.style.use('ggplot')
#create histogram with ggplot2 style
plt.hist(data, color='lightgreen', ec='black', bins=15)
The histogram now has the style of a plot created in ggplot2.
Namely, this style adds a light grey background with white gridlines and uses slightly larger axis tick labels.
Note that we applied ggplot2 styling to a histogram, but the statement plt.style.use(‘ggplot’) can be used to apply ggplot2 styling to any plot in Matplotlib.
Note: You can find more style sheets available to use in Matplotlib plots here.
Additional Resources
The following tutorials explain how to create other common charts in Python:
How to Create Stacked Bar Charts in Matplotlib
How to Create a Relative Frequency Histogram in Matplotlib
How to Create a Horizontal Barplot in Seaborn