numpy - understanding matplotlib.subplots python

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Answer 1

The different return types are due to the squeeze keyword argument to plt.subplots() which is set to True by default. Let's enhance the documentation with the respective unpackings:

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.
    fig, ax = plt.subplots()
  • for Nx1 or 1xN subplots, the returned object is a 1D numpy object array of Axes objects are returned as numpy 1D arrays.
    fig, (ax1, ..., axN) = plt.subplots(nrows=N, ncols=1) (for Nx1)
    fig, (ax1, ..., axN) = plt.subplots(nrows=1, ncols=N) (for 1xN)
  • for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.
    fig, ((ax11, .., ax1M),..,(axN1, .., axNM)) = plt.subplots(nrows=N, ncols=M)
• 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 1x1.
  fig, ((ax,),) = plt.subplots(nrows=1, ncols=1, squeeze=False)
fig, ((ax,), .. ,(axN,)) = plt.subplots(nrows=N, ncols=1, squeeze=False) for Nx1
  fig, ((ax, .. ,axN),) = plt.subplots(nrows=1, ncols=N, squeeze=False) for 1xN
  fig, ((ax11, .., ax1M),..,(axN1, .., axNM)) = plt.subplots(nrows=N, ncols=M)

Alternatively you may always use the unpacked version

fig, ax_arr = plt.subplots(nrows=N, ncols=M, squeeze=False)

and index the array to obtain the axes, ax_arr[1,2].plot(..).

So for a 2 x 3 grid it wouldn't actually matter if you set squeeze to False. The result will always be a 2D array. You may unpack it as

fig, ((ax1, ax2, ax3),(ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3)

to have ax{i} as the matplotlib axes objects, or you may use the packed version

fig, ax_arr = plt.subplots(nrows=2, ncols=3)
ax_arr[0,0].plot(..) # plot to first top left axes
ax_arr[1,2].plot(..) # plot to last bottom right axes