To make it a bit more interesting we generate two functions: sine and cosine. We generate our x-values with numpy’s linspace function

import numpy as np 
import matplotlib.pyplot as plt 

x = np.linspace(0, 2*np.pi) 
sin = np.sin(x) 
cos = np.cos(x) 
plt.plot(x, sin, color='b') 
plt.plot(x, cos, color='r') 
plt.show()

You can plot two or more curves by repeatedly calling the plot method.

That’s fine as long as the individual plots share the same axis-description and values.

Subplots

fig = plt.figure() 
p1 = fig.add_subplot(2, 1, 1) 
p2 = fig.add_subplot(2, 1, 2) 
p1.plot(x, sin, c='b') 
p2.plot(x, cos, c='r'

The add_subplot method allows us to put many plots into one “parent” plot aka figure. The arguments are (number_of_rows, number_of_columns, place in the matrix) So in this example we have 2 rows in 1 column, sine is in first, cosine in second position:

when you have a 2 by 2 matrix it is counted from columns to row

fig = plt.figure() 
p1 = fig.add_subplot(221) 
p2 = fig.add_subplot(222) 
p3 = fig.add_subplot(223) 
p4 = fig.add_subplot(224) 
p1.plot(x, sin, c='b') 
p2.plot(x, cos, c='r') 
p3.plot(x, -sin, c='g') 
p4.plot(x, -cos, c='y')

The code is available as a Jupyter Notebook on my github

More about matplotlib in Part 3

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