+1 vote

The **histogram() **function of the Numpy module computes the histogram of a dataset. It returns bin edges and the value of the histogram. The value of the histogram represents the number of elements in each bin.

Here is an example:

>>> import numpy as np

>>> import random

>>> a=[random.random() for _ in range(15)]

>>> a

[0.8371601421970039, 0.7511725216866857, 0.4424049452497366, 0.2877455161859661, 0.7632456353300519, 0.8196282040760022, 0.6400867612568486, 0.8548527042441426, 0.12257666317541871, 0.8296549025802453, 0.13648784817663995, 0.1838239540293889, 0.5173459297717008, 0.19341387676836408, 0.5716937417929663]

>>> bins = [0, 0.3, 0.6, 0.9, 1]

>>> np.histogram(a,bins)

(array([5, 3, 7, 0]), array([0. , 0.3, 0.6, 0.9, 1. ]))>>> v, e = np.histogram(a,bins)

>>> varray([5, 3, 7, 0])

>>> e

array([0. , 0.3, 0.6, 0.9, 1. ])

In this example, I have randomly generated 15 numbers between 0 and 1 and have created 4 bins: 0-0.3, 0.3-0.6, 0.6-0.9, 0.9-1.

The **histogram()** function returned two arrays: the bins I provided, and the other is the number of elements in those bins. E.g., there are 5 elements of the list in the range 0-0.3, and there are 5 elements in the range 0.6-0.9.

If you mention the number of bins you want to create, the **histogram()** function will automatically determine the range for those bins.

In the below example, I want to create two bins. The function automatically selected the bin edges and computed the number of elements in those bins.

>>> v, e = np.histogram(a,bins=2)

>>> varray([6, 9])

>>> e

array([0.12257666, 0.48871468, 0.8548527 ])

>>>