Gain insights into visually representing data with Matplotlib in Python. Explore plotting techniques, control axes, create complex layouts, and draw various plots, enhancing your data science skills.

For data science, Matplotlib is one of the most popular tools for representing data in a visual manner. There are many other tools, but for the Python user, Matplotlib is a must-know.

In this course, you will learn how to visually represent data in several different ways. You will learn how to use figures and axes to plot a chart, as well as how to plot from multiple types of objects and modules.

You will also discover ways to control the spine of an axes and how to create complex layouts for a figure using GridSpec so you can create visually stunning charts.

In the latter half of the course, you will focus on how to draw various types of plots, whether it be a line plot, a stem plot, or a heatmap plot. Overall, this is your no-fuss introduction to creating impactful data charts.

By the end, you will have an important new skill to add to your resume. As any data scientist knows, it is necessary that you be able to show insights found from analyzing data.

Matplotlib for Python: Visually Represent Data with Plots

# What is a `legend`?

A `legend` provides a description for an element in the figure. A legend helps the user understand a figure when the figure contains many elements. For example, if there are two curves on a graph, then the legend can help the user distinguish between the curves.

There is more than one way to add a `legend` to a figure. In this lesson, we will learn two methods. Both use the `plot()` function. The difference, however, is in how the two methods set their labels.

# Adding a `legend` without labels

In this method, we don't pass the labels to the `legend()`, because the labels are passed in `plot()`. When `legend()` is called, the Axes would detect the labels automatically.

import numpy as np
import matplotlib.pyplot as plt

points = np.linspace(-5, 5, 256)
y1 = np.tanh(points) + 0.5
y2 = np.sin(points) - 0.2

fig, axe = plt.subplots(dpi=800)
axe.plot(points, y1, label="tanh")
axe.plot(points, y2, label="sin")
axe.legend()
fig.savefig("output/output.png")
plt.close(fig)

As we can see in the example above, `line 11` shows how to add a `legend` by using `legend()`. In this example, we don’t pass the labels explicitly because it will detect the labels automatically, which we’ve set at `line 9` and `line 10`.

In this lesson, we will learn how to add a legend to the plot.

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

Adding a Legend to the Plot

What is a `legend`?

Adding a `legend` without labels

Adding a Legend to the Plot

What is a legend?

Adding a legend without labels

What is a `legend`?

Adding a `legend` without labels