This course is designed for those interested to learn how to make different data visualization graphs and charts by programming them in Python.
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Who this course is for:
- Anyone who wants to learn data visualization
- Anyone who knows some data visualization but wants to improve their capabilities
What you’ll learn:
- Understand how to make many types of visualizations
- Understand why the code works, and how it can be manipulated/adapted to your own cases
- How matplotlib’s structure works, and how you can use that to enhance customization
Requirements:
- Basic Python knowledge
Being able to visualize data is becoming a very in-demand skill! Not only does it allow you to present your results easier, but it also enables you to do more analysis by being able to have everything in a visual format in front of you.
Data Visualization is a key skill to have when wanting to work as a Data Scientist or Analyst, but being able to create great visualizations is an important skill for everyone to have.
In this course we’ll go through all the steps to learn how we can create many sorts of visualizations using Python. Along the way we’ll also understand how our library works behind the scenes, which will give us a lot of customization power. An important takeaway with this skill is the ability to create your own visualizations and customize them to fit the situation you need.
Our Promise to You
By the end of this course, you will have learned about advanced data visualizations using Python.
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Course Curriculum
| Section 1 - Prerequisites | |||
| Introduction To Matplotlib | 00:00:00 | ||
| Importing Libraries In Python | 00:00:00 | ||
| Dealing With Files In Python | 00:00:00 | ||
| Section 2 - Developing An Understanding Of The Basics | |||
| Making A Scatter Plot | 00:00:00 | ||
| Understanding Figures | 00:00:00 | ||
| Creating Axes | 00:00:00 | ||
| Making A Line Plot | 00:00:00 | ||
| Section 3 - Customization | |||
| Customization Foreword | 00:00:00 | ||
| Changing The X And Y Limits | 00:00:00 | ||
| Adding A Title And Axis Labels | 00:00:00 | ||
| Adding In Equations Into Text | 00:00:00 | ||
| Adding And Formatting Axis Ticks | 00:00:00 | ||
| Customizing Tick Labels | 00:00:00 | ||
| Adding A Legend | 00:00:00 | ||
| Adding Text Annotations | 00:00:00 | ||
| Customizing Our Graph Edges | 00:00:00 | ||
| Using Plot Styles | 00:00:00 | ||
| Saving Our Plots | 00:00:00 | ||
| Section 4 - Types Of Visualizations | |||
| Histograms | 00:00:00 | ||
| Bar Graphs | 00:00:00 | ||
| Box And Whisker Plots | 00:00:00 | ||
| Pie Charts | 00:00:00 | ||
| 2-Dimensional Histograms | 00:00:00 | ||
| Section 5 - Images | |||
| Loading And Showing Images | 00:00:00 | ||
| Colormaps | 00:00:00 | ||
| Adding A Colorbar To Our Axis | 00:00:00 | ||
| Section 6 - 3-Dimensional Plotting | |||
| 3D Line And Scatter Plots | 00:00:00 | ||
| Changing View Angles And Animating Our Graphs | 00:00:00 | ||
Course Review
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Overall, the course was a great introduction to Python’s Matplotlib. I was able to follow along with the course using an Online Matplotlib Compiler for most of the course. I was able to read in the data and run the exercises. However, I had issues with Section 5 for uploading the image. I researched but still had issues and questions:
Why was file path not used to upload image? Was image placed in special folder?
When I researched, I found https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imread.html that stated imread deprecated in favor of PIL and function exists for historical reasons. It is recommended to use PIL.Image.open instead for loading images. Unfortunately, I couldn’t figure out with the online matplotlib compiler how to work with objects.
For Section 6, I didn’t need to import library as 3d library was already built in. Therefore, course may need some updating for the updated libraries but still a good introduction to the subject matter.