Numpy Ufunc | Versatile functions

These functions include standard trigonometric functions, functions for arithmetic operations, complex number processing, statistical functions, etc. Generic functions have different characteristics, which are as follows:

• These functions work with ndarray (N-dimensional array), that is, with the Numpy class.
• It does fast element-wise operations on arrays.
• It supports various functions such like array translation, casting, etc.
• Numpy, generic functions — these are objects belonging to the numpy.ufunc class.
• Python functions can also be created as a generic function using the frompyfunc library function.
• Some ufuncs are called automatically when the corresponding arithmetic operator is used in arrays. For example, when adding two arrays is done element by element using the + operator, then np.add () is called internally.

Some of the main generic functions in Numpy are:

Trigonometric functions:

These functions work with radians, so it is necessary to convert angles to radians by multiplying by pi / 180. Only then can we call trigonometric functions. They take an array as input arguments. Includes features such as

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
deg2rad	convert degree into radians
rad2deg	convert radians into degree

# Python code to demonstrate trigonometric function import numpy as np   # create an array of corners angles = np.array ([ 0 , 30 , 45 , 60 , 90 , 180 ])    # convert powers to radians # using deg2rad function radians = np.deg2rad (angles)   # sine 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))     # hypothesis function demonstration base = 4 height = 3 print ( ’hypotenuse of right triangle is:’ ) print (np.hypot ( base, height))

Exit:

 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 hyperbolicten: [0. 0.52085606 0.76347124 0.9848655 right triangle is: 5.0 

Statistical functions:

These functions are used to calculate the mean , mean -median-mode-in-python-without-libraries/">median, variance, minimum of array elements. Includes features such as

 

Function	Description
amin, amax	returns minimum or maximum of an array or along an axis
ptp	returns range of values ​​(maximum-minimum) of an array or along an axis
percentile (a, p, axis)	calculate pth percentile of array or along specified axis
mean -median-mode-in-python-without-libraries/">median	compute mean -median-mode-in-python-without-libraries/">median of data along specified axis
mean	compute mean of data along specified axis
std	compute standard deviation of data along specified axis
var	compute variance of data along specified axis
average	compute average of data along specified axis

# Python code demonstrates an aggregate function import numpy as np   # build an array of weights 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))   # weight range, i.e. maximum weight, minimum 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 ))   # greedy print ( ’Mean weight of the students:’ ) print (np. mean (weight))   # mean -median-mode-in-python-without-libraries/">median print ( ’Median weight of the students: ’ ) print (np.mean -median-mode-in-python-without-libraries/">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))   # medium print ( ’ Average weight of the students: ’ ) print (np.average (weight))

Exit:

 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-tiddling functions:

These functions take integer values ​​as input arguments and you perform bitwise operations on the binary representations of these integers. Includes features such as

Function	Description
bitwise_and	performs bitwise and operation on two array elements
bitwies_or	performs bitwise or operation on two array elements
bitwise_xor	performs bitwise xor operation on two array elements
invert	performs bitwise inversion of an array elements
left_shift	shift the bits of elements to left
right_shift	shift the bits of elements to left

# Python code to demonstrate the bitwise function import numpy as np   # build 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))   # Shift left 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 ))

Exit:

 

 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]