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

How many spines does a plot have?

If we want to customize the tick, we use tick_params().

Which of the following statements is incorrect?

Which of the following statements is incorrect?

The axes will detect the labels automatically from `plot()`.

What will happen if we call `legend()` without any parameters?

What will happen if we call <code>legend()</code> without any parameters?

Which function should we use if we want to add a title for a figure that contains two subplots?

Which function should we use if we want to add a title for a figure that contains two subplots?

Which of the following representations of colors is supported?

Which of the following representations of colors is supported?

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

Quiz: Figure Elements