Adjusting Gridlines and Ticks in Matplotlib Imshow

Code for solution as suggested by Serenity:

plt.figure()
im = plt.imshow(np.reshape(np.random.rand(100), newshape=(10,10)),
                interpolation='none', vmin=0, vmax=1, aspect='equal')

ax = plt.gca();

# Major ticks
ax.set_xticks(np.arange(0, 10, 1))
ax.set_yticks(np.arange(0, 10, 1))

# Labels for major ticks
ax.set_xticklabels(np.arange(1, 11, 1))
ax.set_yticklabels(np.arange(1, 11, 1))

# Minor ticks
ax.set_xticks(np.arange(-.5, 10, 1), minor=True)
ax.set_yticks(np.arange(-.5, 10, 1), minor=True)

# Gridlines based on minor ticks
ax.grid(which='minor', color='w', linestyle='-', linewidth=2)

Resulting image:

One can find it easier to use plt.pcolor or plt.pcolormesh:

data = np.random.rand(10, 10)
plt.pcolormesh(data, edgecolors='k', linewidth=2)
ax = plt.gca()
ax.set_aspect('equal')

Though, there are some differences among them and plt.imshow, the most obvious being that the image is swapped by the Y-axis (you can reversed it back easily by adding ax.invert_yaxis() though). For further discussion see here: When to use imshow over pcolormesh?

You can shift the pixels by passing the extent argument to imshow. extent is a 4-element list of scalars (left, right, bottom, top):

foo = np.random.rand(35).reshape(5, 7)
# This keeps the default orientation (origin at top left):
extent = (0, foo.shape[1], foo.shape[0], 0)
_, ax = plt.subplots()
ax.imshow(foo, extent=extent)
ax.grid(color='w', linewidth=2)
ax.set_frame_on(False)

This is kind of a hack but i like to use it since it does not require me to move the xticks or yticks.

import numpy as np
import matplotlib.pyplot as plt

plt.matshow(np.random.random(size=(10,10)))

plt.hlines(y=np.arange(0, 10)+0.5, xmin=np.full(10, 0)-0.5, xmax=np.full(10, 10)-0.5, color="black")
plt.vlines(x=np.arange(0, 10)+0.5, ymin=np.full(10, 0)-0.5, ymax=np.full(10, 10)-0.5, color="black")

plt.show()