matplotlib iterate subplot axis array through single list

Is there a simple/clean way to iterate an array of axis returned by subplots like

nrow = ncol = 2
a = []
fig, axs = plt.subplots(nrows=nrow, ncols=ncol)
for i, row in enumerate(axs):
    for j, ax in enumerate(row):
        a.append(ax)
         
for i, ax in enumerate(a):
    ax.set_ylabel(str(i))

which even works for nrow or ncol == 1.

I tried list comprehension like:

[element for tupl in tupleOfTuples for element in tupl]

but that fails if nrows or ncols == 1

The ax return value is a numpy array, which can be reshaped, I believe, without any copying of the data. If you use the following, you'll get a linear array that you can iterate over cleanly.

nrow = 1; ncol = 2;
fig, axs = plt.subplots(nrows=nrow, ncols=ncol)

for ax in axs.reshape(-1): 
  ax.set_ylabel(str(i))

This doesn't hold when ncols and nrows are both 1, since the return value is not an array; you could turn the return value into an array with one element for consistency, though it feels a bit like a cludge:

nrow = 1; ncol = 1;
fig, axs = plt.subplots(nrows=nrow, ncols=nrow)
axs = np.array(axs)

for ax in axs.reshape(-1):
  ax.set_ylabel(str(i))

reshape docs. The argument -1 causes reshape to infer dimensions of the output.

The fig return value of plt.subplots has a list of all the axes. To iterate over all the subplots in a figure you can use:

nrow = 2
ncol = 2
fig, axs = plt.subplots(nrow, ncol)
for i, ax in enumerate(fig.axes):
    ax.set_ylabel(str(i))

This also works for nrow == ncol == 1.

I am not sure when it was added, but there is now a squeeze keyword argument. This makes sure the result is always a 2D numpy array. Turning that into a 1D array is easy:

fig, ax2d = subplots(2, 2, squeeze=False)
axli = ax2d.flatten()

Works for any number of subplots, no trick for single ax, so a little easier than the accepted answer (perhaps squeeze didn't exist yet back then).

Matplotlib has its own flatten function on axes.

Why don't you try following code?

fig, axes = plt.subplots(2, 3)
for ax in axes.flat:
    ## do something with instance of 'ax'

TLDR; axes.flat is the most pythonic way of iterating through axes

As others have pointed out, the return value of plt.subplots() is a numpy array of Axes objects, thus there are a ton of built-in numpy methods for flattening the array. Of those options axes.flat is the least verbose access method. Furthermore, axes.flatten() returns a copy of the array whereas axes.flat returns an iterator to the array. This means axes.flat will be more efficient in the long run.

Stealing @Sukjun-Kim's example:

fig, axes = plt.subplots(2, 3)
for ax in axes.flat:
    ## do something with instance of 'ax'

sources: axes.flat docs Matplotlib tutorial

Here is a good practice:
For example, we need a set of 4 by 4 subplots so we can have them like below:

rows = 4; cols = 4;
fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(20, 16), squeeze=0, sharex=True, sharey=True)
axes = np.array(axes)

for i, ax in enumerate(axes.reshape(-1)):
  ax.set_ylabel(f'Subplot: {i}')

The output is beautiful and clear.