Using numpy with Matplotlib
Martin McBride, 2019-03-03
Tags numeric python linspace function plot
Categories matplotlib numpy
Here we will look at how to use numpy to create the x values and y values more easily.
Creating the x values
We want to create a set of 100 x values, equally spaced over the range 0 to 12. We can do this using Python lists by scaling the loop variable.
However, numpy has a function
linspace that is designed to do this job. It has two advantages:
- it is more readable
- it covers the exact range 0 to 12, with the intermediate values equally spaced
If you recall, our previous code went from 0 to almost 12, which wasn't really much of a problem, but it wasn't quite correct.
Here is how we use
linspace to create our range:
xa = np.linspace(0, 12, 100)
Creating the y values
numpy supports universal functions. These allow you to operate on entire arrays in one go:
ya = np.sin(xa)*np.exp(-xa/4)
This performs the same calculation on each of the 100 elements of
xa, creating a new 100 element array
ya containing the results. This avoids a loop, which makes the code more readable, but also more efficient.
Here is the complete code:
from matplotlib import pyplot as plt import numpy as np xa = np.linspace(0, 12, 100) ya = np.sin(xa)*np.exp(-xa/4) plt.plot(xa, ya) plt.show()
This code is shorter and more readable, as well as being more efficient. This becomes more important with large data sets, and especially 2 dimensional data.
Here is the output, which look exactly the same as before:
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