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

As we know that, the `pie` chart is an effective way to show the proportion of individual pieces of data as they relate to the data overall. One drawback of such charts is that they only show the data of a given moment. They do not show how parts of a population change over time. We can use `stackplot` to show how parts of the whole change over time.

`stackplot()` is the basic function that Matplotlib provides to create a stack plot. Below are some of the important parameters required by the function:

- `x`: 1d array of dimension N
- `y`: 2d array, dimension($M \times N$),or a sequence of arrays. Each dimension is $1 \times N$.
  ```
  stackplot(x, y)
  stackplot(x, y1, y2, y3)
  ```
- `baseline`: The method used to calculate the baseline. 
    - `zero` a constant zero baseline, which is the default setting.
    - `sym` sets the plot symmetrically around zero.
    - `wiggle` minimizes the sum of the squared slopes.
- `colors`: Provides a list of colors for each data set. 

In this lesson, we will learn how to show the changes in a population over time by using stackplot.

Introduction

How to Draw Various Plot Elements

How to Draw Different Types of Plots

How to Draw a Stack Plot