Introduction to Python Binomial Distribution

In This Article, You Will Learn About Python Binomial Distribution.

Python Binomial Distribution – Before moving ahead, let’s know a bit of Python Normal Distribution

The binomial distribution is the process of counting how many times a particular event has occurred.

It describes the scenarios related to those events that bring some numbers as a result or outcomes.

It often involves statistical analyses of “counts” or “how many times” an event occurs.

E.g., Head-Tail, passed percentile in the exam, conditional statements, Boolean data types, etc.

It has three parameters –

n – Represents several trials.

p – Shows probability of occurrence of each test  (e.g. getting passed in exam 0.25 each in 4 exams).

size – Specifies the shape of the array in terms of Row and Column.

Apart from above all, there are a few more parameters –

hist – A Histogram (complete form) that puts a group of numbers together between the range.

kde – Shows curve on the graph.

Discrete Distribution – The distribution is defined at a separate set of events, e.g. an exam’s result is discrete as it can be only passed or fail In contrast, the age of people is continuous as it can be 19, 25, 62, and so on.

Example – Creating an array of random outcomes of tossing a coin five times.

from numpy import random

b = random.binomial(n=5, p=0.5, size=5)

print(b)
python-binomial-distribution

As shown above, it returned an array containing the possible number of outcomes of tossing a coin 5 times.  

Visualization of Binomial Distribution

Example – Visualizing the Binomial Distribution.   

from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)

plt.show()
python-binomial-distribution

As a result, it returned a graph that lies between the range of random numbers.  

Note: You will learn more about Binomial Distribution later in our tutorial.

Example – Visualizing the Binomial Distribution.   

from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.binomial(n=10, p=0.5, size=1000))

plt.show()
python-binomial-distribution

As a result, it returned a graph that lies between the range of random numbers.  

View to Normal Distribution and Binomial Distribution

The main difference between Normal Distribution and Binomial Distribution is that Normal Distribution leads to continuous numbers; on the other hand, Binomial Distribution leads to finite or countable events or outcomes.

But in case, there’s a bit of data, then Normal Distribution and Binomial Distribution can be defined as the same or similar.

Example – Visualizing the Binomial Distribution between the Normal and Binomial Distribution.

from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.normal(loc=20, scale=10, size=600), hist=False, label='normal')
kl.distplot(random.binomial(n=80, p=0.5, size=600), hist=False, label='binomial')

plt.show()
python-binomial-distribution

As a result, it returned a graph that lies between the range of random numbers.  

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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