In this Matplotlib tutorial, we're going to be talking about styles. With Matplotlib, we have styles which serve a very similar purpose to Matplotlib graphs as CSS (cascading style sheet) pages serve for HTML. As you can see up to this point, all of these changes we're making to our graphs start to add up, and we only have one axis so far! We could use for loops to at least keep the amount of code down, but we can also make use of these styles with Matplotlib.

The idea of a style page is to write your customization to a style file, and then, to use those changes and apply them to your graph, all you do is import style and then use that specific style. This way, let's say you are finding yourself always changing various elements of your graphs. Instead of having to write 25-200 lines of customization code per chart, you can just write it once to a style, and then load in that style and apply all of those changes in two lines! Let's get started.

sample code: pythonprogramming.net
hkinsley.com
twitter.com/sentdex
sentdex.com
seaofbtc.com

1. Introduction and Line
2. Legends titles and labels
3. bar charts and histograms
4. Scatter Plots
5. stack plots
6. Pie Charts
7. loading data from files
8. getting data from the internet
9. converting data from the internet
10. basic customizations, rotating labels
11. handling unix time
12. more customization of colors and fills
13. spines and horizontal lines
14. candlestick OHLC graphs
15. styles
16. Live graphs
17. annotations and placing text
18. annotating last price to edge of matplotlib graph example
19. subplots
20. implementing subplots to our stock chart
21. adding more indicator data to our charts
22. cleaning chart, custom fills, pruning
23. sharex axis
24. multi y axis plotting volume on stock chart
25. customizing Matplotlib Legends
26. Basemap intro
27. Basemap customization options
28. plotting coordinates on a map with Basemap
29. matplotlib 3d intro
30. 3d scatter plot
31. 3d bar charts
32. conclusion

Learn how to visualize data in the form of line graphs, bar charts, pie charts, 3D graphs, and more with Python 3 and Matplotlib.