There are two obtions using plt.bar
.
Single, adjacent bars
You can either plot the bars next to each other, in a grouped fashion, where you need to determine the bars' positions from the number of columns in the array.

import numpy as np
import matplotlib.pyplot as plt
data = np.array([[19, 14, 6, 36, 3],
[12, 12, 1, 32, 1],
[18, 25, 0, 33, 0],
[13, 19, 0, 32, 5],
[12, 14, 0, 33, 0],
[16, 14, 7, 30, 0],
[11, 18, 5, 31, 2],
[17, 11, 3, 46, 7]])
x = np.arange(data.shape[0])
dx = (np.arange(data.shape[1])-data.shape[1]/2.)/(data.shape[1]+2.)
d = 1./(data.shape[1]+2.)
fig, ax=plt.subplots()
for i in range(data.shape[1]):
ax.bar(x+dx[i],data[:,i], width=d, label="label {}".format(i))
plt.legend(framealpha=1).draggable()
plt.show()
Stacked bars
Or you can stack the bars on top of each other, such that the bottom of the bar starts at the top of the previous one.

import numpy as np
import matplotlib.pyplot as plt
data = np.array([[19, 14, 6, 36, 3],
[12, 12, 1, 32, 1],
[18, 25, 0, 33, 0],
[13, 19, 0, 32, 5],
[12, 14, 0, 33, 0],
[16, 14, 7, 30, 0],
[11, 18, 5, 31, 2],
[17, 11, 3, 46, 7]])
x = np.arange(data.shape[0])
fig, ax=plt.subplots()
for i in range(data.shape[1]):
bottom=np.sum(data[:,0:i], axis=1)
ax.bar(x,data[:,i], bottom=bottom, label="label {}".format(i))
plt.legend(framealpha=1).draggable()
plt.show()