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 `boxplot`?

A `boxplot` is a standardized way of displaying a dataset based on a five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles. In statistics, the `boxplot` is a method for graphically depicting groups of numerical data through their quartiles.

In this lesson, we will learn how to display data quartiles by using a box plot.

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

How to Draw a Box Plot

What is `boxplot`?

How to Draw a Box Plot

What is boxplot?

What is `boxplot`?