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# Numpy ufunc | Universal functions

• Last Updated : 21 Jul, 2021

Universal functions in Numpy are simple mathematical functions. It is just a term that we gave to mathematical functions in the Numpy library. Numpy provides various universal functions that cover a wide variety of operations.
These functions include standard trigonometric functions, functions for arithmetic operations, handling complex numbers, statistical functions, etc. Universal functions have various characteristics which are as follows-

• These functions operates on ndarray (N-dimensional array) i.e Numpy’s array class.
• It performs fast element-wise array operations.
• It supports various features like array broadcasting, type casting etc.
• Numpy, universal functions are objects those belongs to numpy.ufunc class.
• Python functions can also be created as a universal function using frompyfunc library function.
• Some ufuncs are called automatically when the corresponding arithmetic operator is used on arrays. For example when addition of two array is performed element-wise using ‘+’ operator then np.add() is called internally.

Some of the basic universal functions in Numpy are-

### Trigonometric functions:

These functions work on radians, so angles need to be converted to radians by multiplying by pi/180. Only then we can call trigonometric functions. They take an array as input arguments. It includes functions like-

Function Description
sin, cos, tan compute sine, cosine and tangent of angles
arcsin, arccos, arctan calculate inverse sine, cosine and tangent
hypot calculate hypotenuse of given right triangle
sinh, cosh, tanh compute hyperbolic sine, cosine and tangent
arcsinh, arccosh, arctanh compute inverse hyperbolic sine, cosine and tangent

## Python3

 # Python code to demonstrate trigonometric function import numpy as np    # create an array of angles angles = np.array([0, 30, 45, 60, 90, 180])     # conversion of degree into radians # using deg2rad function radians = np.deg2rad(angles)    # sine of angles print('Sine of angles in the array:') sine_value = np.sin(radians) print(np.sin(radians))    # inverse sine of sine values print('Inverse Sine of sine values:') print(np.rad2deg(np.arcsin(sine_value)))    # hyperbolic sine of angles print('Sine hyperbolic of angles in the array:') sineh_value = np.sinh(radians) print(np.sinh(radians))    # inverse sine hyperbolic  print('Inverse Sine hyperbolic:') print(np.sin(sineh_value))     # hypot function demonstration base = 4 height = 3 print('hypotenuse of right triangle is:') print(np.hypot(base, height))

Output:

Sine of angles in the array:
[  0.00000000e+00   5.00000000e-01   7.07106781e-01   8.66025404e-01
1.00000000e+00   1.22464680e-16]

Inverse Sine of sine values:
[  0.00000000e+00   3.00000000e+01   4.50000000e+01   6.00000000e+01
9.00000000e+01   7.01670930e-15]

Sine hyperbolic of angles in the array:
[  0.           0.54785347   0.86867096   1.24936705   2.3012989
11.54873936]

Inverse Sine hyperbolic:
[ 0.          0.52085606  0.76347126  0.94878485  0.74483916 -0.85086591]

hypotenuse of right triangle is:
5.0

### Statistical functions:

These functions are used to calculate mean, median, variance, minimum of array elements. It includes functions like-

## Python3

 # Python code demonstrate statistical function import numpy as np    # construct a weight array weight = np.array([50.7, 52.5, 50, 58, 55.63, 73.25, 49.5, 45])    # minimum and maximum  print('Minimum and maximum weight of the students: ') print(np.amin(weight), np.amax(weight))    # range of weight i.e. max weight-min weight print('Range of the weight of the students: ') print(np.ptp(weight))    # percentile print('Weight below which 70 % student fall: ') print(np.percentile(weight, 70))     # mean  print('Mean weight of the students: ') print(np.mean(weight))    # median  print('Median weight of the students: ') print(np.median(weight))    # standard deviation  print('Standard deviation of weight of the students: ') print(np.std(weight))    # variance  print('Variance of weight of the students: ') print(np.var(weight))    # average  print('Average weight of the students: ') print(np.average(weight))

Output:

Minimum and maximum weight of the students:
45.0 73.25

Range of the weight of the students:
28.25

Weight below which 70 % student fall:
55.317

Mean weight of the students:
54.3225

Median weight of the students:
51.6

Standard deviation of weight of the students:
8.05277397857

Variance of weight of the students:
64.84716875

Average weight of the students:
54.3225

### Bit-twiddling functions:

These functions accept integer values as input arguments and perform bitwise operations on binary representations of those integers. It include functions like-

## Python3

 # Python code to demonstrate bitwise-function import numpy as np    # construct an array of even and odd numbers even = np.array([0, 2, 4, 6, 8, 16, 32]) odd = np.array([1, 3, 5, 7, 9, 17, 33])    # bitwise_and print('bitwise_and of two arrays: ') print(np.bitwise_and(even, odd))    # bitwise_or print('bitwise_or of two arrays: ') print(np.bitwise_or(even, odd))    # bitwise_xor print('bitwise_xor of two arrays: ') print(np.bitwise_xor(even, odd))     # invert or not print('inversion of even no. array: ') print(np.invert(even))    # left_shift  print('left_shift of even no. array: ') print(np.left_shift(even, 1))    # right_shift  print('right_shift of even no. array: ') print(np.right_shift(even, 1))

Output:

bitwise_and of two arrays:
[ 0  2  4  6  8 16 32]

bitwise_or of two arrays:
[ 1  3  5  7  9 17 33]

bitwise_xor of two arrays:
[1 1 1 1 1 1 1]

inversion of even no. array:
[ -1  -3  -5  -7  -9 -17 -33]

left_shift of even no. array:
[ 0  4  8 12 16 32 64]

right_shift of even no. array:
[ 0  1  2  3  4  8 16]

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