1. Where to go next
Congratulations! You have completed this introduction to Matplotlib.
And yet, we have only scratched the surface in what Matplotlib can do.
2. The Matplotlib gallery
One way to learn about other kinds of visualizations that you can create with Matplotlib is to visit the online gallery of examples on the Matplotlib website, at this URL.
3. Gallery of examples
The gallery contains several dozen examples of figures that you can create with Matplotlib. If you click on one of the figures, you will land in a page
4. Example page with code
that contains not only a larger version of the example, but also the full Python code that would generate this example from scratch.
If you are interested in creating a visualization that is a variation on this example, you can start by copying over this example code and editing it to fit your particular use-case.
This is much better than starting from scratch!
5. Plotting data in 3D
Here are a few of the things that you might want to do next. In this course we always visualized data using the two dimensions of the page, but you can also extend your capability to visualize data, by adding perspective to your visualizations to make them appear three-dimensional.
For example, here is a parametric curve through a three-dimensional space.
In this web page, you can learn more about creating three-dimensional visualizations.
6. Visualizing images with pseudo-color
Another capability of Matplotlib is visualizing data from images.
For example, here is an image visualized using pseudo-color, where each value in the image is translated into a color.
You can learn more about working with images in this URL.
7. Animations
You might remember this visualization that I showed you in the very first lesson of this course. It used one more dimension, time, by varying the display through animation.
You can create animations by creating multiple frames of the movie, each as its own visualization, and then stitching them together into a movie using tools such as Quicktime, but Matplotlib also has its own interface for creating animations. You can learn about this interface at this URL.
8. Using Matplotlib for geospatial data
There are multiple software packages that extend Matplotlib's capability to a variety of different kinds of data. For example, Cartopy extends Matplotlib to be used with geospatial data, such as maps.
9. pandas + Matplotlib = Seaborn
Another library that extends Matplotlib is Seaborn. This library creates very sophisticated statistical visualizations from pandas data structures, such as DataFrames. The nice thing about Seaborn is that you can create elegant and sophisticated visualizations of your data with very little code.
For example, this code would create this visualization that encodes the fuel efficiency of cars as a function of their horsepower, but also encodes the country in which the car was manufactured, using the color of the bubbles, as well as their weight, using the size of each bubble.
10. Seaborn example gallery
Seaborn also has an extensive example gallery that you can visit in this URL.
11. Good luck visualizing your data!
So as you can see, there is a lot more to learn about data visualization. But thanks to this course, you have already taken your first step along the path to visualizing your data in Python, using Matplotlib. Good luck visualizing your data!