matplotlib iterate subplot axis array through single list

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.

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'

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).

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.