PYTHON

np euclidean distance python

import numpy as np
a = np.array((1,1,1))
b = np.array((2,2,2))
dist = np.linalg.norm(a-b)

euclidean distance python

# I hope to be of help and to have understood the request
from math import sqrt # import square root from the math module
# the x and y coordinates are the points on the Cartesian plane
pointA = (x, y) # first point
pointB = (x, y) # second point
distance = calc_distance(pointA, pointB) # here your beautiful result
def calc_distance(p1, p2): # simple function, I hope you are more comfortable
  return sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2) # Pythagorean theorem

Calculate Euclidean Distance in Python using distance.euclidean()

# Python code to find Euclidean distance
# using distance.euclidean() method

# Import SciPi Library
from scipy.spatial import distance

# initializing points in
# numpy arrays
point1 = (4, 4, 2)
point2 = (1, 2, 1)
 
# print Euclidean distance 
print(distance.euclidean(point1,point2))

Calculate Euclidean Distance in Python

# Python code to find Euclidean distance
# using dot() and sqrt() methods

# Import NumPy Library
import numpy as np

# initializing points in
# numpy arrays
point1 = np.array((4, 4, 2))
point2 = np.array((1, 2, 1))

# subtracting both the vectors
temp = point1 - point2
 
# Perform dot product
# and do the square root
dist = np.sqrt(np.dot(temp.T, temp))
 
# printing Euclidean distance
print(dist)

euclidean distance python 3 variables

# Python code to find Euclidean distance 
# using sum() and square() 
  
import numpy as np 
  
# intializing points in 
# numpy arrays 
point1 = np.array((1, 2, 3)) 
point2 = np.array((1, 1, 1)) 
  
# finding sum of squares 
sum_sq = np.sum(np.square(point1 - point2)) 
  
# Doing squareroot and 
# printing Euclidean distance 
print(np.sqrt(sum_sq)) 

python euclidean distance

def euclidean_distance(row1, row2):
    distance = 0.0

    for i in range(len(row1)-1):
        distance += (row1[i] - row2[i])**2
    return m.sqrt(distance)