Sofar we've only really shown you the "simple" usage of matplotlib.
This includes a "use one-liners where possible" attitude but it is
worth demonstrating that there's another side of the matplotlib api
that is more specialized. To demonstrate this we'll now use the
plt.subplots
function (not plt.subplot
) which gives us a more
aligned way of plotting.
import numpy as np
import matplotlib.pylab as plt
def grid_plots(n, m):
fig, axes = plt.subplots(n, m, figsize=(7, 7), sharex=True, sharey=True)
for i, ax in enumerate(axes.flat):
x = np.random.normal(i, 1, (1000,))
ax.hist(x, bins=30)
ax.set_title(f"$\mu$={i}, $\sigma$={1}")
grid_plots(3, 3)
If you're interested in all the code from these videos you can find the notebook on github.