numpy.hypot() in Python

numpy.exp2(arr1, arr2[, out]) = ufunc 'hypot') : 

arr1, arr2  : [array_like] Legs(side and perpendicular) of triangle
out         : [ndarray, optional] Output array with result.

An array having hypotenuse of the right triangle.

# Python3 program explaining
# hypot() function

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)

leg1 array :  [12, 3, 4, 6]
leg2 array :  [5, 4, 3, 8]

Hypotenuse is as follows :
[ 13.   5.   5.  10.]

# Python3 program explaining
# hypot() function

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)

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]]

# Python3 program explaining
# hypot() function

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)

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]]

mohit gupta_omg :)

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Article Tags :

• Python
• Python-numpy
• Python numpy-Mathematical Function

Practice Tags :

• python