Two-Dimensional Plotting
To begin with, we have to import the respective libraries. The main plotting functions are
found in the sublibrary matplotlib.pyplot:
In [ 1 ]: import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
One-Dimensional Data Set
In all that follows, we will plot data stored in NumPy ndarray objects. However,
matplotlib is of course able to plot data stored in different Python formats, like list
objects, as well. First, we need data that we can plot. To this end, we generate 20 standard
normally distributed (pseudo)random numbers as a NumPy ndarray:
In [ 2 ]: np.random.seed( 1000 )
y = np.random.standard_normal( 20 )
The most fundamental, but nevertheless quite powerful, plotting function is plot from the
pyplot sublibrary. In principle, it needs two sets of numbers:
x values: a list or an array containing the x coordinates (values of the abscissa)
y values: a list or an array containing the y coordinates (values of the ordinate)
The number of x and y values provided must match, of course. Consider the following two
lines of code, whose output is presented in Figure 5-1:
In [ 3 ]: x = range(len(y))
plt.plot(x, y)
Figure 5-1. Plot given x and y values
plot notices when you pass an ndarray object. In this case, there is no need to provide the
“extra” information of the x values. If you only provide the y values, plot takes the index
values as the respective x values. Therefore, the following single line of code generates
exactly the same output (cf. Figure 5-2):
In [ 4 ]: plt.plot(y)
