A simple way to achieve this is to make sure that for every x-value, the y-values sum to 100.
I assume that you have the y-values organized in an array as in the example below, i.e.
y = np.array([[17, 19, 5, 16, 22, 20, 9, 31, 39, 8],
[46, 18, 37, 27, 29, 6, 5, 23, 22, 5],
[15, 46, 33, 36, 11, 13, 39, 17, 49, 17]])
To make sure the column totals are 100, you have to divide the y array by its column sums, and then multiply by 100. This makes the y-values span from 0 to 100, making the "unit" of the y-axis percent. If you instead want the values of the y-axis to span the interval from 0 to 1, don't multiply by 100.
Even if you don't have the y-values organized in one array as above, the principle is the same; the corresponding elements in each array consisting of y-values (e.g. y1, y2 etc.) should sum to 100 (or 1).
The below code is a modified version of the example @LogicalKnight linked to in his comment.
import numpy as np
from matplotlib import pyplot as plt
fnx = lambda : np.random.randint(5, 50, 10)
y = np.row_stack((fnx(), fnx(), fnx()))
x = np.arange(10)
# Make new array consisting of fractions of column-totals,
# using .astype(float) to avoid integer division
percent = y / y.sum(axis=0).astype(float) * 100
fig = plt.figure()
ax = fig.add_subplot(111)
ax.stackplot(x, percent)
ax.set_title('100 % stacked area chart')
ax.set_ylabel('Percent (%)')
ax.margins(0, 0) # Set margins to avoid "whitespace"
plt.show()
This gives the output shown below.
