NumPy Normal Distribution (Python Tutorial)

NumPy Normal Distribution (Python Tutorial)

This is a detailed tutorial of the NumPy Normal Distribution. Learn to implement Normal Distribution in Numpy and visualize using Seaborn.

This is a detailed tutorial of NumPy Poisson Distribution. Learn to implement Poisson Distribution in NumPy and visualize using Seaborn.

Poisson Distribution This distribution is a discrete distribution in which we have a discrete set of data. This data is not in the continuous form of data. In this type of data, we have certain types of the specified value, and the outcomes can not be beyond these values.

This distribution helps us in calculating the probability of occurrence of an event. These events will take place at a certain specified time. As a result, we get to know about the occurrence of an event and the number of times the event will occur. Also, the event takes place in a particular time period, not before or after that time.

This distribution takes in these two parameters as input:

lam – this is the rate of occurrence of an event size – it is the shape of the returned array. Let u stake an example to understand it better:

Output:

[4 6 2 3 7] Here in this example, we have given the rate of occurrence as four and the shape of the array as five. As a result, we get the following outcome.

Difference between Normal, Binomial and Poisson In a normal distribution, we have continuous data, whereas the other two distributions have binomial and Poisson have a discrete set of data. They can become similar when certain standard deviation and mean could match and also large ver n, and near-zero p is very much identical to the Poisson distribution because n*p is equal to lam.

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