Python Plotting With Matplotlib (Overview)
A picture is worth a thousand words, and with Python’s
matplotlib library, it fortunately takes far less than a thousand words of code to create a production-quality graphic.
matplotlib is also a massive library, and getting a plot to look just right is often achieved through trial and error. Using one-liners to generate basic plots in
matplotlib is relatively simple, but skillfully commanding the remaining 98% of the library can be daunting.
In this beginner-friendly course, you’ll learn about plotting in Python with
matplotlib by looking at the theory and following along with practical examples. While learning by example can be tremendously insightful, it helps to have even just a surface-level understanding of the library’s inner workings and layout as well.
By the end of this course, you’ll:
- Know the differences between PyLab and Pyplot
- Grasp the key concepts in the design of
- Visualize arrays with
- Plot by combining
This course assumes you know a tiny bit of NumPy. You’ll mainly use the
numpy.random module to generate “toy” data, drawing samples from different statistical distributions. If you don’t already have
matplotlib installed, see the documentation for a walkthrough before proceeding.
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