The plt.bar creates the bar chart for us. If you do not explicitly choose a color, then, despite doing multiple plots, all bars will look the same. This gives us a change to cover a new Matplotlib customization option, however. You can use color to color just about any kind of plot, using colors like g for green, b for blue, r for red, and so on. You can also use hex color codes, like #191970

Next, we can cover histograms. Very much like a bar chart, histograms tend to show distribution by grouping segments together. Examples of this might be age groups, or scores on a test. Rather than showing every single age a group might be, maybe you just show people from 20-25, 25-30... and so on.

sample code: pythonprogramming.net
hkinsley.com
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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.