This Python tutorial will learn about the “**Python Scipy Special**” where we will calculate the factorial, combinations, and softmax of the given numbers or array of integers. Additionally, cover the following topics:

- Python Scipy Special
- Python Scipy Special Factorial
- Python Scipy Special Softmax
- Python Scipy Special Legendre
- Python Scipy Special Comb
- Python Scipy Special Logit
- Python Scipy Special Gamma
- Python Scipy Special Binom
- Python Scipy Special Erf
- Python Scipy Special Beta

## Python Scipy Special

The Python Scipy module

that has the definition of several unique functions of mathematical physics is the major part of the *scipy.special*

. Elliptic, Airy, vessel, beta, hypergeometric, Mathieu, parabolic cylinder, kelvin, Struve, gamma, and spheroidal wave are some of the available functions.*scipy.special module*

There are also specific low-level stats functions that are not intended for public use because the stats module provides a more user-friendly interface to these functions.

The majority of these functions can accept array parameters and return array results using the same broadcasting principles as other Numerical Python math functions.

Read: Scipy Linalg – Helpful Guide

## Python Scipy Special Factorial

The Python SciPy has a method

within module *factorial*

that get the factorial of a number or a set of numbers.*scipy.special*

The syntax is given below.

`scipy.special.factorial(n, exact=True)`

Where parameters are:

Values for input, If n is less than zero, the result is 0.*n(array_data, integer):*If True, use long integer arithmetic to calculate the answer. If False, the gamma function is used to swiftly approximate the result in a floating point.*exact(boolean):*

The method

returns the *factorial*pe int, array, or float.

`nf`

( which is the factorial of integer of array of numbers) of tyLet’s understand with an example by following the below steps:

Import the required libraries using the below python code.

```
import numpy as np
from scipy import special
```

Create an array containing numbers and pass this array to a method

to compute the factorial of all the numbers with the array using the below code.*factorial()*

```
array_num = np.array([5, 3, 4])
special.factorial(array_num,exact= False)
```

Again compute the factorial of the same array with parameter

equal to *exact*

using the below code.*True*

`special.factorial(array_num,exact= True)`

Look at the output, how the factorial is different when we change the value

( for false it shows the result in float) to *False*

(for true, the result is only in integer) of parameter *True*

. *exact*

This is how to compute the factorial of a given number or set of numbers using the method

of Python Scipy.*factorial()*

Read: Scipy Stats Zscore + Examples

## Python Scipy Special Softmax

The Python Scipy module

contains a method *scipy.special*

that changes each item of an array by calculating each item’s exponential divided by the total of all the items’ exponentials.*softmax()*

The syntax is given below.

`scipy.special.softmax(x, axis=0)`

Where parameters are:

- x(array_data): It is the array of data as input.
- axis(None, int): Calculate values along the specified axis.

The method

returns *softmax()* of type ndarray.

`s`

(An array with the same dimensions as x. Along the selected axis, the outcome will equal one)Let’s understand with an example by following the below steps:

Import the required libraries using the below python code.

```
from scipy import special
import numpy as np
```

Create an array of numbers using the below code.

```
array_num = np.array([[3, 0.2, 0.5, 1],
[3, 7, 1, -1],
[13, 3, 2, 12]])
```

Apply the softmax function on the whole array to transform each value of the array using the below code.

`special.softmax(array_num)`

Look at the returned output that contains the values which are transformed by the method

.*softmax()*

This is how to compute the softmax of the given array.

Read: Scipy Convolve – Complete Guide

## Python Scipy Special Legendre

The module

of Python SciPy contains a method *scipy.special*

to calculate the Legendre polynomial.*legendre()*

The syntax is given below.

`scipy.special.legendre(n, monic=True`

Where parameters are:

To specify the polynomial degree.*n(int):*If this is the true case, the leading coefficient should be scaled to one.*monic(boolean):*

The method `legendre()`

returns

which is orthoploy1d.*P*

Let’s take an example by following the below steps:

Import the required libraries and create the Legendre polynomial of 4th order using the below code.

```
from scipy import special
special.legendre(4)
```

This is how to generate the Legendre polynomial using the method

of Python SciPy.*legendre()*

Read: Scipy Integrate + Examples

## Python Scipy Special Comb

The method

is used to compute the combination that is the number of different combinations of *comb()*** N** different things that can be done

**at a time. This method exists in the module**

*k**scipy.special*

of Python SciPy.The syntax is given below.

`scipy.special.comb(N, k, repetition=True, exact=True)`

Where parameters are:

It is used to define the number of things.*N(ndarray, int):*The number of elements that were taken.*k(ndarray, int):*If the value repetition is true, the number of combinations with repetition is calculated.*repetition(boolean):*If exact is false, floating-point precision is employed; otherwise, a large integer is computed with exact precision.*exact(boolean):*

The method returns of types ndarray, int, and float.

`val`

(which is the number of possible combinations in total)Let’s take an example by following the below steps:

Import the required libraries using the below python code.

```
from scipy import special
import numpy as np
```

Define several N things and elements chosen using the below code.

```
chosen_k = np.array([5, 6])
n_things = np.array([15, 15])
```

Calculate the combination using the below code.

`special.comb(n_things, chosen_k, exact=False)`

This is how to compute the combination using the method

of Python SciPy.*comb()*

Read: Scipy Stats – Complete Guide

## Python Scipy Special Logit

The logit function is the standard logistic distribution’s quantile function. The Python Scipy module

contains a method *scipy.special*

to compute the logit of a given ndarrays. Logit is the universal function.*logit()*

The syntax is given below.

`scipy.special.logit(x)`

Where a parameter

is the ndarray to which logit should be applied element-by-element.*x*

The method

returns *logit()* of type ndarray.

`out`

( A similar-shaped ndarray to x. Its elements are logit versions of the relevant x entry)Let’s compute the logit of ndarray using the below code.

```
from scipy import special
special.logit([0, 0.30, 0.6, 0.90, 0.99])
```

In the above code, we have created and passed the array containing the float values to a method

to find the logit of the values within an array.*logit()*

This is how to compute the logit of a given ndarray using the method `logit()`

of Python SciPy.

Read: Scipy Sparse – Helpful Tutorial

## Python Scipy Special Gamma

The Python Scipy has a method

within the module *gamma()*

that calculates the gamma of the given array. The generalized factorial function is what the gamma function is known as.*scipy.special*

The syntax is given below.

`scipy.special.gamma(z)`

Where a parameter

is an argument with a real or complex value of type array.*z*

The method

returns the gamma function’s values of type ndarray or scalar.*gamma()*

Let’s take an example by following the below steps:

Import the libraries using the below python code.

`from scipy import special`

Create an array of data and pass the array to a method

as shown below the code.*gamma()*

`special.gamma([5,1,0.5,1])`

Define a complex number and compute the gamma of that number using the below code.

`special.gamma(1.6 + 2j)`

As we can see in the above output, we have calculated the gamma values of the array and complex numbers.

This is how to compute the gamma value of the given array or complex number using the method

of Python SciPy.*gamma()*

Read: Scipy Constants – Multiple Examples

## Python Scipy Special Binom

Binom stands for the

, Python SciPy has a method *Binomial Coefficient*

that exists in a module *binom()*

. This method performs the same operation as the method *scipy.special*

that we have learned in the above sub-section. To know more about it, please refer to the above section *comb()*** “Python Scipy Comb”**.

The syntax is given below.

`scipy.special.binom(n, k)`

Where parameters are:

** N(ndarray, int):** It is used to define the number of things.

**The number of elements that were taken.**

*k(ndarray, int):*Let’s take an example by following the below steps:

Import the required libraries using the below python code.

```
from scipy import special
import numpy as np
```

Define several N things and elements chosen using the below code.

```
chosen_k = np.array([9, 11])
n_things = np.array([20, 20])
```

Calculate the combination using the below code.

`special.binom(n_things, chosen_k)`

This is how to compute the combination using the method

of Python SciPy.*binom()*

Read: Scipy Rotate Image + Examples

## Python Scipy Special Erf

The Erf stands for

which is the method *error function*

in module *erf()*

that returns the complicated argument’s error function.*scipy.special*

The syntax is given below.

`scipy.special.erf(z)`

Where the parameter

the array of data as input.*z*

The method

returns *erf()*of type ndarray.

`res`

(the error function’s values at the specified x locations) Let’s take an example by following the below steps:

Import the necessary libraries using the below python code.

```
import matplotlib.pyplot as plt
from scipy.special import erf
import numpy as np
%matplotlib inline
```

Generate an array of data using the below code.

`array_data = np.linspace(-5,5)`

Compute the error function of the array using the below code.

```
error_f = erf(array_data)
error_f[0::3]
```

Plot the graph of the error function using the below code.

```
plt.plot(array_data, error_f)
plt.xlabel('$arraydata$')
plt.ylabel('$eror_function(array_data)$')
plt.show()
```

This is how to compute the error function of the given data using the method

of Python SciPy.*erf()*

Read: Scipy Misc + Examples

## Python Scipy Special Beta

Beta functions, commonly known as the Euler integral of the first kind, are a special sort of function. The Python SciPy has a method

within the module *beta()*

that is applied to an array containing real values to get the values. The beta function follows a symmetrical pattern.*scipy.special*

The syntax is given below.

`scipy.special.beta(a, b, out=None)`

Where parameters are:

It is arguments with real value as input.*a,b(array_data):*The function result can have an optional output array.*out(ndarray):*

The method

returns the beta function’s value of type ndarray.*beta()*

Let’s take an example by following the below steps:

Import the necessary libraries using the below python code.

`from scipy import special`

Apply the beta function to the values using the below code.

`special.bet(4,8)`

Now perform the below steps to see the symmetrical pattern of the beta function.

```
print(special.beta(6,3))
print(special.beta(3,6))
```

In line 8, we have passed the two values to a method

by swapping the places of value in a function. The result is the same as shown in the above output. This is the symmetrical nature of the function *beta()*

.*beta()*

This is how to use the function

of Python SciPy.*beta()*

Also, take a look at some more Python Scipy tutorials.

- Scipy Signal – Helpful Tutorial
- Python Scipy FFT [11 Examples]
- Scipy Normal Distribution
- Python Scipy Matrix + Examples

So, in this tutorial, we have learned about the “** Python Scipy Special**” and covered the following topics.

- Python Scipy Special
- Python Scipy Special Factorial
- Python Scipy Special Softmax
- Python Scipy Special Legendre
- Python Scipy Special Comb
- Python Scipy Special Logit
- Python Scipy Special Gamma
- Python Scipy Special Binom
- Python Scipy Special Erf
- Python Scipy Special Beta

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.