= Using a single axis label to annotate multiple subplot axes =
When using multiple subplots with the same axis units, it is redundant to label each axis individually, and makes the graph overly complex.   You can use a single axis label, centered in the plot frame, to label multiple subplot axes.  Here is how to do it:

{{{
#!python
# note that this a code fragment...you will have to define your own data to plot
# Set up a whole-figure axes, with invisible axis, ticks, and ticklabels,
# which we use to get the xlabel and ylabel in the right place
bigAxes = pylab.axes(frameon=False)     # hide frame
pylab.xticks([])                        # don't want to see any ticks on this axis
pylab.yticks([])
# I'm using TeX for typesetting the labels--not necessary
pylab.ylabel(r'\textbf{Surface Concentration $(nmol/m^2)$}', size='medium')
pylab.xlabel(r'\textbf{Time (hours)}', size='medium')
# Create subplots and shift them up and to the right to keep tick labels
# from overlapping the axis labels defined above
topSubplot = pylab.subplot(2,1,1)
position = topSubplot.get_position()
position[0] = 0.15
position[1] = position[1] + 0.01
topSubplot.set_position(position)
pylab.errorbar(times150, average150)
bottomSubplot = pylab.subplot(2,1,2)
position = bottomSubplot.get_position()
position[0] = 0.15
position[1] = position[1] + 0.03
bottomSubplot.set_position(position)
pylab.errorbar(times300, average300)
}}}

###
Alternatively, you can use the following snippet to have shared ylabels on your subplots. Also see the attached [attachment:Same_ylabel_subplots.png figure output].

{{{
#!python
import pylab

figprops = dict(figsize=(8., 8. / 1.618), dpi=128)                                          # Figure properties
adjustprops = dict(left=0.1, bottom=0.1, right=0.97, top=0.93, wspace=0.2 hspace=0.2)       # Subplot properties

fig = pylab.figure(**figprops)                                                              # New figure
fig.subplots_adjust(**adjustprops)                                                          # Tunes the subplot layout

ax = fig.add_subplot(3, 1, 1)
bx = fig.add_subplot(3, 1, 2, sharex=ax, sharey=ax)
cx = fig.add_subplot(3, 1, 3, sharex=ax, sharey=ax)

ax.plot([0,1,2], [2,3,4], 'k-')
bx.plot([0,1,2], [2,3,4], 'k-')
cx.plot([0,1,2], [2,3,4], 'k-')

pylab.setp(ax.get_xticklabels(), visible=False)
pylab.setp(bx.get_xticklabels(), visible=False)

bx.set_ylabel('This is a long label shared among more axes', fontsize=14)
cx.set_xlabel('And a shared x label', fontsize=14)
}}}

Thanks to Sebastian Krieger from matplotlib-users list for this trick.
###

###
Simple function to get rid of superfluous xticks but retain the ones on the bottom  (works in pylab). Combine it with the above snippets to get a nice plot without too much redundance:
{{{
#!python
def rem_x():
    '''Removes superfluous x ticks when multiple subplots  share
    their axis works only in pylab mode but can easily be rewritten
    for api use'''
    nr_ax=len(gcf().get_axes())
    count=0
    for z in gcf().get_axes():
        if count == nr_ax-1: break
            setp(z.get_xticklabels(),visible=False)
            count+=1
}}}

###
The first one above doesn't work for me. The subplot command overwrites the bigaxes.
However, I found a much simpler solution to do a decent job for two axes and one ylabel:

yyl=plt.ylabel(r'My longish label that I want vertically centred')

yyl.set_position((yyl.get_position()[0],1)) # This says use the top of the bottom axis as the reference point.

yyl.set_verticalalignment('center')
     

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CategoryCookbookMatplotlib