Create Dual-Axis Matplotlib Plot

Introduction

This tutorial will guide you through the steps of creating a simple plot using Matplotlib, a Python library used for data visualization. We will be using the host_subplot module to create a plot with two y-axes.

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Skills Graph

Import necessary modules

The first step is to import the necessary modules for our plot. We will be using numpy to generate our x and y data, matplotlib.pyplot to create the plot, and mpl_toolkits.axes_grid1 to create the second y-axis.

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import host_subplot

Generate data

Next, we need to generate our x and y data. We will be generating a sine wave for this example.

xx = np.arange(0, 2*np.pi, 0.01)
yy = np.sin(xx)

Create the plot

Now we can create our plot using the host_subplot function. This function creates a subplot with two y-axes.

ax = host_subplot(111)
ax.plot(xx, yy)

Create the second y-axis

To create the second y-axis, we need to create a new axis object using the twin function.

ax2 = ax.twin()

Set tick labels for the second y-axis

We can set the tick labels for the second y-axis using the set_xticks function and passing in the tick locations and labels as arguments.

ax2.set_xticks([0., .5*np.pi, np.pi, 1.5*np.pi, 2*np.pi],
               labels=["$0$", r"$\frac{1}{2}\pi$",
                       r"$\pi$", r"$\frac{3}{2}\pi$", r"$2\pi$"])

Hide tick labels for the right y-axis

We can hide the tick labels for the right y-axis using the major_ticklabels.set_visible function.

ax2.axis["right"].major_ticklabels.set_visible(False)

Show tick labels for the top y-axis

We can show the tick labels for the top y-axis using the same major_ticklabels.set_visible function.

ax2.axis["top"].major_ticklabels.set_visible(True)

Display the plot

Finally, we can display our plot using the show function.

plt.show()

Summary

In this tutorial, we learned how to create a simple plot with two y-axes using Matplotlib. We used the host_subplot module to create the plot and the mpl_toolkits.axes_grid1 module to create the second y-axis. We generated our data using numpy and displayed the plot using matplotlib.pyplot.