# Python Scipy stats.halfgennorm.entropy() Function

Scipy Library in Python:

• SciPy is a scientific computation package that uses the NumPy library underneath.
• SciPy is an abbreviation for Scientific Python.
• It includes additional utility functions for optimization, statistics, and signal processing.
• SciPy, like NumPy, is open source, so we can freely use it.
• Travis Olliphant, the developer of NumPy, created SciPy.
• SciPy has optimized and added/enhanced functions that are often used in NumPy and Data Science.

Scipy stats.halfgennorm.entropy() Function:

We can obtain the value of entropy of a random variate by using the stats.halfgennorm.entropy() function.

Syntax:

stats.halfgennorm.entropy(beta)

Return Value:

The entropy value of a random variate is returned by the stats.halfgennorm.entropy() Function.

### What is Entropy?

When entropy is discussed in information theory, it refers to the randomness in data. Another way to think about entropy is as the data’s unpredictability. So a high entropy indicates that the data is scattered, whereas a low entropy indicates that nearly all of the data is the same.

## Scipy stats.halfgennorm.entropy() Function in Python

### Method #1: Using entropy Function (Static Input)

Approach:

• Import halfgennorm() method from stats of scipy module using the import keyword
• Give the beta value as static input and store it in a variable.
• Calculate the entropy value using the entropy() function of halfgennorm by passing the given beta value as an argument to it.
• Store it in another variable.
• Print the entropy value for the given beta value.
• The Exit of the Program.

Below is the implementation:

# Import halfgennorm() method from stats of scipy module using the import keyword
from scipy.stats import halfgennorm

# Give the beta value as static input and store it in a variable.
gvn_beta = 3

# Calculate the entropy value using the entropy() function of halfgennorm by passing
# the given beta value as an argument to it.
# Store it in another variable.
rslt = halfgennorm.entropy(gvn_beta)

# Print the entropy value for the given beta value
print("The entropy value for the given beta {", gvn_beta,"} value = ", rslt)

Output:

The entropy value for the given beta { 3 } value = 0.22014169159299057

### Method #2: Using entropy Function (User Input)

Approach:

• Import halfgennorm() method from stats of scipy module using the import keyword
• Give the beta value as user input using the int(input()) function and store it in a variable.
• Calculate the entropy value using the entropy() function of halfgennorm by passing the given beta value as an argument to it.
• Store it in another variable.
• Print the entropy value for the given beta value.
• The Exit of the Program.

Below is the implementation:

# Import halfgennorm() method from stats of scipy module using the import keyword
from scipy.stats import halfgennorm

# Give the beta value as user input using the int(input()) function and store it in a variable.
gvn_beta = int(input("Enter some random number = "))

# Calculate the entropy value using the entropy() function of halfgennorm by passing
# the given beta value as an arguments to it.
# Store it in another variable.
rslt = halfgennorm.entropy(gvn_beta)

# Print the entropy value for the given beta value
print("The entropy value for the given beta {", gvn_beta,"} value = ", rslt)

Output:

Enter some random number = 5
The entropy value for the given beta { 5 } value = 0.1146259099966842