Creating Figures with Matplotlib in Python

Introduction

This lab will guide you through the process of creating figures with titles, subtitles, and global labels using the Matplotlib library in Python. You will learn how to create different types of oscillation plots and how to add a global x- or y-label to a figure.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Create a damped and undamped oscillation plot

First, we will create a figure with two subplots, one for a damped oscillation and another for an undamped oscillation. We will use the np.linspace() function to create an array of time values and then plot the corresponding amplitude values for each type of oscillation using the np.cos() and np.exp() functions.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0.0, 5.0, 501)

fig, (ax1, ax2) = plt.subplots(1, 2, layout='constrained', sharey=True)
ax1.plot(x, np.cos(6*x) * np.exp(-x))
ax1.set_title('damped')
ax1.set_xlabel('time (s)')
ax1.set_ylabel('amplitude')

ax2.plot(x, np.cos(6*x))
ax2.set_xlabel('time (s)')
ax2.set_title('undamped')

fig.suptitle('Different types of oscillations', fontsize=16)

plt.show()

Add global x- and y-labels to a figure

Next, we will add global x- and y-labels to a figure showing the relative stock prices of different companies over time. We will use the np.genfromtxt() function to read in a CSV file containing stock price data and then plot the data for each company using subplots. We will use the fig.supxlabel() and fig.supylabel() methods to add global x- and y-labels to the figure.

from matplotlib.cbook import get_sample_data

with get_sample_data('Stocks.csv') as file:
    stocks = np.genfromtxt(
        file, delimiter=',', names=True, dtype=None,
        converters={0: lambda x: np.datetime64(x, 'D')}, skip_header=1)

fig, axs = plt.subplots(4, 2, figsize=(9, 5), layout='constrained',
                        sharex=True, sharey=True)
for nn, ax in enumerate(axs.flat):
    column_name = stocks.dtype.names[1+nn]
    y = stocks[column_name]
    line, = ax.plot(stocks['Date'], y / np.nanmax(y), lw=2.5)
    ax.set_title(column_name, fontsize='small', loc='left')
fig.supxlabel('Year')
fig.supylabel('Stock price relative to max')

plt.show()

Summary

In this lab, you learned how to create figures with titles and subtitles using the Matplotlib library in Python. You also learned how to add global x- and y-labels to a figure. These skills are useful for creating clear and informative visualizations of data.