numpy.hypot() in Python
Last Updated :
08 Mar, 2024
This mathematical function helps user to calculate hypotenuse for the right angled triangle, given its side and perpendicular. Result is equivalent to Equivalent to sqrt(x1**2 + x2**2), element-wise.
Syntax :
numpy.exp2(arr1, arr2[, out]) = ufunc 'hypot') :
Parameters :
arr1, arr2 : [array_like] Legs(side and perpendicular) of triangle
out : [ndarray, optional] Output array with result.
Return :
An array having hypotenuse of the right triangle.
Code #1 : Working
import numpy as np
leg1 = [ 12 , 3 , 4 , 6 ]
print ( "leg1 array : " , leg1)
leg2 = [ 5 , 4 , 3 , 8 ]
print ( "leg2 array : " , leg2)
result = np.hypot(leg1, leg2)
print ( "\nHypotenuse is as follows :" )
print (result)
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Output :
leg1 array : [12, 3, 4, 6]
leg2 array : [5, 4, 3, 8]
Hypotenuse is as follows :
[ 13. 5. 5. 10.]
Code #2 : Working with 2D array
import numpy as np
leg1 = np.random.rand( 3 , 4 )
print ( "leg1 array : \n" , leg1)
leg2 = np.ones(( 3 , 4 ))
print ( "leg2 array : \n" , leg2)
result = np.hypot(leg1, leg2)
print ( "\nHypotenuse is as follows :" )
print (result)
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Output :
leg1 array :
[[ 0.57520509 0.12043366 0.50011671 0.13800957]
[ 0.0528084 0.17827692 0.44236813 0.87758732]
[ 0.94926413 0.47816742 0.46111934 0.63728903]]
leg2 array :
[[ 1. 1. 1. 1.]
[ 1. 1. 1. 1.]
[ 1. 1. 1. 1.]]
Hypotenuse is as follows :
[[ 1.15362944 1.00722603 1.11808619 1.0094784 ]
[ 1.00139339 1.01576703 1.09347591 1.33047342]
[ 1.37880469 1.10844219 1.10119528 1.18580661]]
Code 3 : Equivalent to sqrt(x1**2 + x2**2), element-wise.
import numpy as np
leg1 = np.random.rand( 3 , 4 )
print ( "leg1 array : \n" , leg1)
leg2 = np.ones(( 3 , 4 ))
print ( "leg2 array : \n" , leg2)
result = np.sqrt((leg1 * leg1) + (leg2 * leg2))
print ( "\nHypotenuse is as follows :" )
print (result)
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Output :
leg1 array :
[[ 0.7015073 0.89047987 0.1595603 0.27557254]
[ 0.67249153 0.16430312 0.70137114 0.48763522]
[ 0.68067777 0.52154819 0.04339669 0.2239366 ]]
leg2 array :
[[ 1. 1. 1. 1.]
[ 1. 1. 1. 1.]
[ 1. 1. 1. 1.]]
Hypotenuse is as follows :
[[ 1.15362944 1.00722603 1.11808619 1.0094784 ]
[ 1.00139339 1.01576703 1.09347591 1.33047342]
[ 1.37880469 1.10844219 1.10119528 1.18580661]]
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