visualization figsize - Why do many examples use “fig, ax=plt.subplots()” in Matplotlib/pyplot/python

figure grid (3)

plt.subplots() is a function that returns a tuple containing a figure and axes object(s). Thus when using fig, ax = plt.subplots() you unpack this tuple into the variables fig and ax. Having fig is useful if you want to change figure-level attributes or save the figure as an image file later (e.g. with fig.savefig('yourfilename.png'). You certainly don't have to use the returned figure object but many people do use it later so it's common to see. Also, all axes objects (the objects that have plotting methods), have a parent figure object anyway, thus:

fig, ax = plt.subplots()

is more concise than this:

fig = plt.figure()
ax = fig.add_subplot(111)

I'm learning to use matplotlib by studying examples, and a lot of examples seem to include a line like the following before creating a single plot...

fig, ax = plt.subplots()

Here are some examples...

I see this function used a lot, even though the example is only attempting to create a single chart. Is there some other advantage? The official demo for subplots() also uses f, ax = subplots when creating a single chart, and it only ever references ax after that. This is the code they use.

# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')

Just a supplement here.

The following question is that what if I want more subplots in the figure?

As mentioned in the Doc, we can use fig = plt.subplots(nrows=2, ncols=2) to set a group of subplots with grid(2,2) in one figure object.

Then as we know, the fig, ax = plt.subplots() returns a tuple, let's try fig, ax1, ax2, ax3, ax4 = plt.subplots(nrows=2, ncols=2) firstly.

ValueError: not enough values to unpack (expected 4, got 2)

It raises a error, but no worry, because we now see that plt.subplots() actually returns a tuple with two elements. The 1st one must be a figure object, and the other one should be a group of subplots objects.

So let's try this again:

fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(nrows=2, ncols=2)

and check the type:

type(fig) #<class 'matplotlib.figure.Figure'>
type(ax1) #<class 'matplotlib.axes._subplots.AxesSubplot'>

Of course, if you use parameters as (nrows=1, ncols=4), then the format should be:

fig, [ax1, ax2, ax3, ax4] = plt.subplots(nrows=1, ncols=4)

So just remember to keep the construction of the list as the same as the subplots grid we set in the figure.

Hope this would be helpful for you.

In your second example (with scanf()) reason why this is still slower might be because scanf("%s") parses string and looks for any space char (space, tab, newline).

Also, yes, CPython does some caching to avoid harddisk reads.

python matplotlib plot visualization