If Jupyter Notebook is the new Excel, the horsepower of data science (visualizations, presentations and demos), Matplotlib is the engine or the burning core, the source of the power. Matplotlib, often used together with pyplot, can be used to visualize data, graph plots, visualize deep learning and machine learning training loops and loss.
This article, Matplotlib Explained, was originally written by Hennadii Madan for Kite Blog for Python. Read the original article on Kite Blog! It is republished on www.siliconvanity.com with permission from Kite. I recommend this article because it gets down to the details of what Matplotlib is all about. When doing machine learning tutorials, Jupyter Notebook, or Python, sklearn tutorials, matplotlib is usually glossed over, and discussed only as an afterthought. This article talks about the design of Matplotlib. Kite allows developers to code faster in python with line-of-code completion. It is now available for Atom, PyCharm, Sublime, VS Code and Vim. I tried it and like it. You can try it in the browser. What I like the most is to easily auto-complete API syntax without checking the documentation every other second. Recommend!
This article offers an overview of matplotlib. Once you understand the basics, you may find it very useful to find code snippets for very cool plots and use it in your projects. I usually use a code snippets that I found and customize it for my project rather than compose something from scratch.
This post features a basic tutorial on matplotlib plotting package for python. In it, we’ll discuss the purpose of data visualization and construct several simple plots to showcase the basic matplotlib functionality. After reading this post you’ll understand what matplotlib is, when and how to use it, when not to use it, and where to find help!
Table of Contents1. Introduction
- What is matplotlib?
- When to use matplotlib
- When not to use matplotlib
- Purpose of data visualization
- Backends and interaction setup
- Jupyter notebook
3. Visualization techniques
- We see in 2D
- 1D data
- 2D data
- Multidimensional data
What is matplotlib?
When to use matplotlib
- Exploratory data analysis
- Scientific plotting for publication
When not to use matplotlib
- Graphical user interfaces – instead, use pyforms.
- Interactive visualization for web – instead, use bokeh.
- Large datasets – instead, use vispy.
Purpose of data visualization
Backends and interaction setup
3. Visualization techniques
We see in 2D
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