5  Graphing with Style

5.1 Style

There is a tremendous amount of customization that can be done with PyPlot graphs. Two important style customizations involve the marker and line. The marker is the point that is plotted, and the line is the connection between points. See the PyPlot marker style reference and line style reference.

5.1.1 Marker

The most important customizations is the marker, but we can additionally set the colors and size:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [2, 4, 3, 5]

plt.plot( x, y, 
          marker='o',
          markeredgecolor='red', 
          markerfacecolor='orange',
          markersize=10
         )
plt.show()

Some additional marker types include:

Marker Type	Marker Code	Appearance
Point	'.'	·
Plus	'+'	+
X	'x'	×
Circle	'o'	○
Square	's'	□
Pentagon	'p'	⬠
Hexagon	'h'	⬡
Diamond	'D'	◇
None	''	(no mark)

5.1.2 Line

The main line customizations include which type of line, and what color to draw it with.

x = [1, 2, 3, 4]
y = [2, 4, 3, 5]

plt.plot( x, y, 
          linestyle='-.', 
          color='purple'
        )
plt.show()

Some additional line styles include:

Line Type	Line Code	Appearance
Solid	'-'	─────
Dashed	'--'	─ ─ ─ ─
Dashdot	'-.'	─ · ─ ·
Dotted	':'	· · · ·
None	''	(no line)

5.1.3 Marker and Line Shortcuts

It is best to write out the explicit attributes you are customizing, but there are some nice shortcuts for convenience. Plot red point markers with dotted line using:

x = [1, 2, 3, 4]
y = [2, 4, 3, 5]

plt.plot(x, y, 'r.:')    # Red, point marker, dotted line
plt.show()

plt.plot(x, y, 'ks--')   # Black, square marker, dashed line 
plt.show()

The matplotlib documentation offers several nice cheat-sheets that can be printed.

5.2 Summary

Description	Python
Label \(x\) axis	plt.xlabel('X')
Label \(y\) axis	plt.ylabel('Y')
Title a graph	plt.title('My Plot')
Plot and label	plt.plot(x,y,label='series1')
Turn on legend	plt.xlabel('X')
Turn on grid	plt.grid()

Exercises

1. Graph the following data set as a scatter plot. On the same plot, draw an approximate best fit line to the data (this is an estimate).

   x	0	1	2	3	4	5	6	7	8	9
   y	12.5	13.1	13.2	13.2	13.8	14.1	15	14.8	14.9	15.1

2. Recreate the following graphs as closely as you can:

3. Perform a reflex test (or use previous data). Have one person place their hand at the edge of a table with their fingers out. A second person holds a ruler just above the first person’s finger tips and lets go at a random time. Without moving their hand, the first person closes their fingers to stop the ruler. For each person,
   • Collect 12 samples of how far the ruler falls before it is caught.
   • Put the data into a NumPy array.
   • Create a histogram for each person depicting the distribution of their reflex times.
   • Create a box plot for each person depicting the distribution of their reflex times.
4. Consider a pendulum with mass \(m\), length \(L\), and release angle \(\theta\). The period \(T\) of a pendulum is the amount of time it takes to complete one cycle of motion, swinging from one side to the other and then back again. To model the period of a pendulum, we want to determine the effect that each of the variables \(m\), \(L\), and \(\theta\) have on \(T\). We can explore this experimentally by keeping two of the variables constant while changing the third and looking for changes in \(T\).