# matplotlib.

For quick plotting, you can't go wrong with matplotlib.

**Episode Notes**

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.

Feedback? See an issue? Feel free to mention it here.

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