matplotlib is the workhorse of data science visualization. The module pyplot gives us MATLAB like plots.
The most basic plot is done with the “plot”-function. It looks like this:
import matplotlib.pyplot as plt plt.plot([0, 1, 2, 3], [0, 1, 2, 3]) plt.show()
The plot function takes an x and y array and draws a blue line through all points.
You can of course draw each point independently without a line:
plt.plot([0, 1, 2, 3], [0, 1, 2, 3], "o")
or you can highlight the individual points while drawing the line
plt.plot([0, 1, 2, 3], [0, 1, 2, 3], marker='o')
You can find other markers here.
Color of the plot
The color of the line can be changed with the color parameter. The default color of a plot is blue. If you want e.g. red as the color, you can use ‘r’.
plt.plot([0, 1, 2, 3], [0, 1, 2, 3], color="r")
The following table shows the basic colors
| Color | shortcut |
|---|---|
| blue | b |
| green | g |
| red | r |
| cyan | c |
| magenta | m |
| yellow | y |
| black | k (not so obvious :-)) |
| white | w |
In /matplotlib/_color_data.py you find additional colors, even colors from the XKCD color survey results
plt.plot(x, y, color="xkcd:nasty green")
Stroke width and style
changing the width of the plotted line is done via linewidth
plt.plot([0, 1, 2, 3], [0, 1, 2, 3], linewidth=7.0, color="xkcd:nasty green")
and the stroke style can be altered with the linestyle parameter
plt.plot([0, 1, 2, 3], [0, 1, 2, 3], linestyle=":", color="xkcd:nasty green", linewidth=7.0)
You can find other line styles here
Axis Labels
In school I learned that all axis of a plot must have labels. So let’s add them:
plt.ylabel('some other numbers')
plt.xlabel('some numbers')
Saving the plot
If You want to save the plot as a png you can replace the show command with
plt.savefig('scatter_01.png')
You can find the code examples in one JuPyter notebook in my github repo
When you’ve become familiar with the basic plot function you can dive into part 2
