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