Python Scipy stats.hypsecant.cdf() Function

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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.cdf() Function:

We can obtain the value of the cumulative distribution function by using the stats.hypsecant.cdf() method.

Syntax:

stats.hypsecant.cdf(x, beta)

Return Value:

The cumulative distribution value is returned by the cdf() function.

Scipy stats.hypsecant.cdf() Function in Python

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

Approach:

  • Import hypsecant from stats of scipy module using the import keyword
  • Give the beta value as static input and store it in a variable.
  • Pass some random value(x), beta value as arguments cdf() function of hypsecant to the get the value of the cumulative distribution function and store it in another variable.
  • Print the cumulative distribution function value for the given beta value.
  • The Exit of the Program.

Below is the implementation:

# Import hypsecant from stats of scipy module using the import keyword
from scipy.stats import hypsecant
# Give the beta value as static input and store it in a variable.
gvn_beta = 3

# Pass some random value(x), beta value as arguments cdf() function of hypsecant
# to the get the value of the cumulative distribution function and store it in another variable.
rslt = hypsecant.cdf(0.2, gvn_beta)
# Print the cumulative distribution function value for the given beta value
print("The cumulative distribution function value for the given beta value is:")
print(rslt)

Output:

The cumulative distribution function value for the given beta value is:
0.03866527549256035

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

Approach:

  • Import hypsecant from stats of scipy module using the import keyword
  • Give the beta value as static input and store it in a variable.
  • Pass some random value(x), beta value as arguments cdf() function of hypsecant to the get the value of the cumulative distribution function and store it in another variable.
  • Print the cumulative distribution function value for the given beta value.
  • The Exit of the Program.

Below is the implementation:

# Import hypsecant from stats of scipy module using the import keyword
from scipy.stats import hypsecant
# 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 = "))

# Pass some random value(x), beta value as arguments cdf() function of hypsecant
# to the get the value of the cumulative distribution function and store it in another variable.
rslt = hypsecant.cdf(0.6, gvn_beta)
# Print the cumulative distribution function value for the given beta value
print("The cumulative distribution function value for the given beta value{",gvn_beta,"} is:")
print(rslt)

Output:

Enter some random number = 2
The cumulative distribution function value for the given beta value{ 2 } is:
0.1539176313854603