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[PL 0303] Python for Data Visualization - Matplotlib Master Class

This is a lecture on data visualization using the Matplotlib library.

40 students are taking this course

Matplotlib
Python
AI

What you will learn!

  • Okay, I will translate the following Korean text into English: 안녕하세요. 저는 파이썬을 좋아합니다. Translation: Hello. I like Python.

  • Matplotlib

  • Data Visualization

NOTICE

This lecture is part of the pre-semester (preparatory semester before this semester) of the artificial intelligence specialized curriculum All about AI.

Data processing libraries: NumPy, Matplotlib, Pandas
This lecture covers Matplotlib, the core of data visualization .

Miro Link: https://miro.com/app/board/uXjVNJ8PZSs=/?share_link_id=801072444784

For an introduction to All About AI, please refer to the orientation lecture.

Matplotlib, the core of data visualization using Python!

In the Python world, Matplotlib is the de facto standard library for data visualization.

Like NumPy, once you learn it properly, it is a library that will be of great help in any field you learn in the future.

In this course, you will learn Matplotlib more completely than any other training material .

Matplotlib from a systematic and object-oriented perspective!

This course consists of 4 parts and 18 chapters.

Part.I Matplotlib Preview

Chap1 Visualization Preview with PyPlot Interfaces

Chap2 Visualization Preview with OOP Interfaces

Part.II Matplotlib Anatomy

Chap3 Matplotlib Anatomy Prerequisites

Chap4 Figure Objects

Chap5 Axes Objects

Chap6 Text Objects

Chap7 Spines, Ticks and Grids

Chap8 Axis Objects and Legends

Chap9 Colormaps and Colorbars

Chap10 rcParams and Styles

Chap11 Transformations

Part.III Plotting APIs

Chap12 Pairwise Data Visualizations

Chap13 Statistical Distributions Visualizations

Chap.14 Gridded Data Visualizations

Chap.15 3D Data Visualizations

Part IV Visualization Hands-on Practices

Chap.16 EDA on the Iris Species

Chap.17 MNIST Classification

Chap.18 EDA on the London Bike Sharing


The following parts are introduced in reverse chronological order to explain what you need to learn to achieve your goals.

Part IV Visualization Hands-on Practices

Ultimately, through this course we will learn the ability to visualize data the way we want.

To this end, in the final Part VI, we will practice processing and visualizing the most commonly used data in machine learning.

Here is an example of a visualization that we will be creating together in Part VI.

Part.III Plotting APIs

To draw graphs like Part.VI, you need to learn how to use various plotting APIs provided by Matplotlib.

Matplotlib provides the following types of plotting:

In this lecture, we will learn how to use all the plotting APIs except the less commonly used graphs in the figures above.

Part.II Matplotlib Anatomy

Before we dive right into Part III, in Part II we'll learn how to use elements that are common to all graphs .

As you can see in this image, elements like Figure, Axes, Text, Spine, XAXis, YAxis, Tick, Ticklabel, and Grid apply to all graphs.

So in Part II, before we start drawing graphs in earnest, we'll learn how to handle these common elements as we wish.

This will allow you to learn common elements that apply to the world of Matplotlib all at once, and systematically learn how Matplotlib works.

Part.I Matplotlib Preview

In Part II, we will learn the components of Matplotlib graphs one by one.

However, if you learn this content right away, it may sound like something out of the blue.

So in the very first part, Part.I , we will get a taste of the overall process of drawing graphs using Matplotlib.

Learn briefly about the two interfaces used in Matplotlib, and why the OOP Interface is more powerful than the PyPlot Interface.

This will help you understand where and how the things you learn next apply.

Lecture as a Reference Note!

When using Python's Matplotlib in practice, there is a lot of content, so it is difficult to memorize and use everything.

The lecture notes for this course will serve as reference notes to help you work quickly by looking at the lecture notes instead of having to search for materials on the Internet inefficiently.

Recommended for
these people!

Who is this course right for?

  • Anyone who works with data using Python

  • For those learning machine learning and deep learning

Need to know before starting?

  • Numpy Basics

  • Basic Python Syntax

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Curriculum

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103 lectures ∙ (19hr 55min)

Course Materials:

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