Python NumPy numpy.histogram() Function

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Minahil Noor Jan 30, 2023
NumPy
  1. Syntax of numpy.histogram()
  2. Example Codes: numpy.histogram()
  3. Example Codes: numpy.histogram() to Specify the Number and Size of Bins
  4. Example Codes: numpy.histogram() to Use the density Parameter
  5. Example Codes: numpy.histogram() to Plot the Histogram
Python NumPy numpy.histogram() Function

Python NumPy numpy.histogram() function generates the values of a histogram. It doesn’t plot a histogram but it computes its values. We pass an array as a parameter. This function computes its histogram and returns an array that has stored histogram values. We can say that it returns the numeric representation of a histogram.

Syntax of numpy.histogram()

numpy.histogram(a, bins=10, range=None, normed=None, weights=None, density=None)

Parameters
a It is an array_like structure. It represents the input data to compute the histogram.
bins It is an integer, string, or sequence of scalars. It represents the number of bins. A bin is like a range, for example, 0-5, 6-10, etc. If bins is an integer then it represents the number of equally spaced bins. If it is a string then it represents the method to calculate the bin space. If it is a sequence then it represents the bins with different widths.
range It is the range given in a floating point number. It represents the upper and the lower range of the bins. If the range is not given then [a.min(), a.max()] is the range.
normed It is a boolean parameter. Its function is like the density parameter but if the bins are not equally spaced it generates incorrect results.
weights It is an array_like structure. Its size is the same as a. If the density is True then the weights are normalized.
density It is a boolean parameter. If its value is True then it computes the probability rather than computing the frequency.

Return

It returns two arrays: hist and bin_edges. The array hist shows the values of the histogram and bin_edges shows the bin edges. The size of bin_edges is always 1 + (size of hist) i.e length(hist)+1.

Example Codes: numpy.histogram()

The parameter a is a mandatory parameter. If we execute this function without passing the number of bins then it will compute ten bins with unequal spaces.

import numpy as np

a = np.array(
    [89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78]
)

histogram = np.histogram(a)
print(histogram)

Output:

(
    array([2, 3, 2, 2, 1, 0, 1, 2, 3, 4], dtype=int64),
    array([2.0, 10.8, 19.6, 28.4, 37.2, 46.0, 54.8, 63.6, 72.4, 81.2, 90.0]),
)

Example Codes: numpy.histogram() to Specify the Number and Size of Bins

We will specify the number of bins first.

import numpy as np

a = np.array(
    [89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78]
)

histogram = np.histogram(a, bins=2)
print(histogram)

Output:

(array([10, 10], dtype=int64), array([ 2., 46., 90.]))

The above code has computed a histogram that has 2 bins. The bins are [2., 46.) and [46., 90.).

Now, we will specify the edge of the bins.

import numpy as np

a = np.array(
    [89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78]
)

histogram = np.histogram(a, bins=[0, 30, 60, 90])
print(histogram)

Output:

(array([7, 4, 9]), array([0, 30, 60, 90]))

Example Codes: numpy.histogram() to Use the density Parameter

import numpy as np

a = np.array(
    [89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78]
)

histogram = np.histogram(a, bins=5, density=True)
print(histogram)

Output:

(array([ 0.01420455,  0.01136364,  0.00284091,  0.00852273,  0.01988636]), 
 array([  2. ,  19.6,  37.2,  54.8,  72.4,  90. ]))

Note that instead of computing histogram values, the function generates the probability.

Example Codes: numpy.histogram() to Plot the Histogram

We can plot the histogram using pyplot.

from matplotlib import pyplot as plt
import numpy as np

a = np.array(
    [89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78]
)

plt.hist(a, bins=[0, 20, 40, 60, 80, 100])
plt.title("histogram")
plt.show()

Output:

numpy histogram