I found some code to generate a set of small multiples and it is working perfectly.
fig, axes = plt.subplots(6,3, figsize=(21,21)) fig.subplots_adjust(hspace=.3, wspace=.175) for ax, data in zip(axes.ravel(), clean_sets): ax.plot(data.ETo, "o")
The line for ax, data in zip(axes.ravel(), clean_sets):
contians .ravel()
but I do not understand what this is actually doing or why it is necessary.
If I take a look at the docs I find the following:
Return a contiguous flattened array.
A 1-D array, containing the elements of the input, is returned. A copy is made only if needed.
I guess the return that corresponds to axes from plt.subplot()
is a multidimensional array that can’t be iterated over, but really I’m not sure. A simple explanation would be greatly appreciated.
What is the purpose of using .ravel()
in this case?
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Answer
Your guess is correct. plt.subplots()
returns either an Axes
or a numpy
array of several axes, depending on the input. In case a 2D grid is defined by the arguments nrows
and ncols
, the returned numpy
array will be a 2D array as well.
This behaviour is explained in the pyplot.subplots
documentation inside the squeeze
argument,
squeeze
: bool, optional, default: True
If True, extra dimensions are squeezed out from the returned Axes object:
- if only one subplot is constructed (nrows=ncols=1), the resulting single Axes object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1D numpy object array of Axes objects are returned as numpy 1D arrays.
- for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.
If False, no squeezing at all is done: the returned Axes object is always a 2D array containing Axes instances, even if it ends up being 1×1.
Since here you have plt.subplots(6,3)
and hence N>1, M>1
, the resulting object is necessarily a 2D array, independent of what squeeze
is set to.
This makes it necessary to flatten this array in order to be able to zip
it. Options are
zip(axes.ravel())
zip(axes.flatten())
zip(axes.flat)