Ok, here's one way to attack it, using features from the matplotlib
hist
function itself:
fig, ax = plt.subplots(1, 1, figsize=(9, 5))
ax.hist([data.ix[low:high, 'values'] for low, high in [(0, 70), (70, 85), (85, 90)]],
bins=15,
stacked=True,
rwidth=1.0,
label=['first70', 'next15', 'last5'])
ax.legend()
Which gives: