How to normalize a histogram in python?

When you plot a normalized histogram, it is not the height that should sum up to one, but the area underneath the curve should sum up to one:

In [44]:

import matplotlib.pyplot as plt
k=(3,3,3,3)
x, bins, p=plt.hist(k, density=True)  # used to be normed=True in older versions
from numpy import *
plt.xticks( arange(10) ) # 10 ticks on x axis
plt.show()  
In [45]:

print bins
[ 2.5  2.6  2.7  2.8  2.9  3.   3.1  3.2  3.3  3.4  3.5]

Here, this example, the bin width is 0.1, the area underneath the curve sums up to one (0.1*10).

To have the sum of height to be 1, add the following before plt.show():

for item in p:
    item.set_height(item.get_height()/sum(x))

One way is to get the probabilities on your own, and then plot with plt.bar:

In [91]: from collections import Counter
    ...: c=Counter(k)
    ...: print c
Counter({1: 2, 3: 1, 4: 1})

In [92]: plt.bar(prob.keys(), prob.values())
    ...: plt.show()

result:

You could use the solution outlined here:

weights = np.ones_like(myarray)/float(len(myarray))
plt.hist(myarray, weights=weights)