Skip to content
Related Articles

Related Articles

numpy.hypot() in Python
  • Last Updated : 18 Nov, 2020

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




# 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)


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




# 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)


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.




# 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)


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]]

References :
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.hypot.html#numpy.hypot
.

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up
Recommended Articles
Page :