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 the `colorbar`?

While `legend` helps us identify the different elements of a chart, the feature only works for discrete data. If the data is continuous, how could we display it on the `legend`? `colorbar` can help us. The `colorbar` maps the values of an interval to the colors of a range. The user can then distinguish the approximate range of the data by color. `colorbar` is a very useful and beautiful feature.

The simplest way to draw a color bar is by calling `colorbar()`.

>**Notice:** In this lesson, the `colorbar()` is called from the figure object. Let’s see some of the parameters required by `colorbar`.
>
>- `mappable`: The `matplotlib.cm.ScalarMappable` described by the colorbar. 
>- `cax`: The axes object onto which the color bar will be drawn.

What is `matplotlib.cm.ScalarMappable`?

In short, `ScalarMappable` is a class to map scaler data to RGBA. This class requires two parameters:

- `norm`: A normalize class, which typically maps a range number to the interval `[0, 1]`.
- `cmap`: The colormap is used to map normalized data values to RGBA colors. Matplotlib has already defined many color maps, which we can use by calling `plt.get_cmap()` with a name as the parameter.

# What is the `colorbar`?

While `legend` helps us identify the different elements of a chart, the feature only works for discrete data. If the data is continuous, how could we display it on the `legend`? `colorbar` can help us. The `colorbar` maps the values of an interval to the colors of a range. The user can then distinguish the approximate range of the data by color. `colorbar` is a very useful and beautiful feature.

The simplest way to draw a color bar is by calling `colorbar()`.

>**Notice:** In this lesson, the `colorbar()` is called from the figure object. Let’s see some of the parameters required by `colorbar`.
>
>- `mappable`: The `matplotlib.cm.ScalarMappable` described by the colorbar. 
>- `cax`: The axes object onto which the color bar will be drawn.

What is `matplotlib.cm.ScalarMappable`?

In short, `ScalarMappable` is a class to map scaler data to RGBA. This class requires two parameters:

- `norm`: A normalize class, which typically maps a range number to the interval `[0, 1]`.
- `cmap`: The colormap is used to map normalized data values to RGBA colors. Matplotlib has already defined many color maps, which we can use by calling `plt.get_cmap()` with a name as the parameter.

In this lesson, we will learn how to use a color bar to improve the readability of our data.

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

Using Color Bars to Improve Data Readability

What is the `colorbar`?

Using Color Bars to Improve Data Readability

What is the colorbar?

What is the `colorbar`?