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 Matplotlib?
Matplotlib elements
Matplotlib is a versatile tool that can be used to create many different plot elements. As such, it can be confusing to new users. Before trying to create a plot, let’s make sure to define some basic terms first.

The **first question** is what is a figure? You may see some code like below.

```
plot.set_title("This is a figure.")
axes.set_title("This is a figure.")
```

What is a `plot`? What is a `axes`? What are differences between them? How to draw a figure, by figure or axes? 

Below is an official image which tell us some important parts of a figure.

In this lesson, we will learn some basic features of Matplotlib, including key terms and their definitions.

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

Basics of Matplotlib