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.
nrow=ncol=1 you can use squeeze=0. plt.subplots(nrows=nrow, ncols=nrow, squeeze=0) always returns a 2 dimensional array for the axes, even if both are one.
squeeze=, but note that in 2024 it's a boolean, so squeeze=False would be the "correct" way to use the argument.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.
axs then?
fig.axes have/do that axs doesn't have/do?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).
You can use numpy's .flat attribute on the axes object.
Here is an example
fig, axes = plt.subplots(2, 3)
for ax in axes.flat:
## do something with instance of 'ax'
.flat is an attribute of the numpy array and has nothing to do with matplotlib.
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 up four by four 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.
