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

numpy euclidean distance

dist = numpy.linalg.norm(a-b)

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)