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

A `scatter` plot is used to plot data points on a figure based on the horizontal and vertical axes. Scatter plots can be used to show the relationship between two variables. They can also show how data clusters in a dataset. One of the most striking features of drawing scatter plots in Matplotlib is the ability to set different colors and sizes for individual data points by using additional variables.



In this lesson, we will learn two ways to draw a scatter plot.

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

How to Draw a Scatter Plot