matplotlib colorbar in each subplot

Question

This can be easily solved with the the utility make_axes_locatable. I provide a minimal example that shows how this works and should be readily adaptable:

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

import numpy as np

m1 = np.random.rand(3, 3)
m2 = np.arange(0, 3*3, 1).reshape((3, 3))

fig = plt.figure(figsize=(16, 12))
ax1 = fig.add_subplot(121)
im1 = ax1.imshow(m1, interpolation='None')

divider = make_axes_locatable(ax1)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(im1, cax=cax, orientation='vertical')

ax2 = fig.add_subplot(122)
im2 = ax2.imshow(m2, interpolation='None')

divider = make_axes_locatable(ax2)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(im2, cax=cax, orientation='vertical');

In plt.colorbar(z1_plot,cax=ax1), use ax= instead of cax=, i.e. plt.colorbar(z1_plot,ax=ax1)

Please have a look at this matplotlib example page. There it is shown how to get the following plot with four individual colorbars for each subplot:

I hope this helps.
You can further have a look here, where you can find a lot of what you can do with matplotlib.

Specify the ax argument to matplotlib.pyplot.colorbar(), e.g.

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(2, 2)
for i in range(2):
    for j in range(2):
         data = np.array([[i, j], [i+0.5, j+0.5]])
         im = ax[i, j].imshow(data)
         plt.colorbar(im, ax=ax[i, j])

plt.show()