Understand Seaborn drawing heat maps in one article

Introduction

In this article, we will use the Seaborn library to represent data in the form of a heatmap heat map. We will focus on how to create it, as well as how to change its color, adjust the corresponding font size, and so on.

Without further ado, let’s get started!

What is a heat map?

Heatmap, also known as heat map in Chinese, is a data lake visualization technique that represents the size or density of data through color changes. In heat map, changes in data size are usually represented as gradual color changes, forming a “heat” distribution map in two-dimensional space.

The depth of color is usually used to represent the magnitude of data. The darker the color, the higher the value, and vice versa. This type of chart is very suitable for displaying the distribution of data such as geographic information, user behavior, and scientific measurements.

Install the Seaborn library

To create a heatmap in Python, you can use the Seaborn library, which is built on top of the Matplotlib library. The Seaborn library provides an advanced abstract interface for data lake visualization, making it easy for us to draw various matrix data.