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

We can use `plot()` to draw a scatter plot.

We can use <code>plot()</code> to draw a scatter plot.

Colorbar only supports continuous color.

The maximum (or minimum) value of boxplot is the real maximum (or minimum).

The maximum (or minimum) value of boxplot is the real maximum (or minimum).

What coordinate system does a radar plot use?

What coordinate system does a radar plot use?

A heatmap can be used to show the relationship between two variables

A stem plot can be used to show the shape of a data’s distribution

A box plot can be used to show the distribution of the data.

Error bars are used to indicate the error or uncertainty in a reported measurement.

Which of the following statements is incorrect?

Which of the following statements is incorrect?

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

Quiz: Different Types of Plots