import numpy as np

import statistics

N = int(eval(input()))

XY = np.array([tuple(map(int, input().split())) for i in range(N)])

ans = 0

for i in range(N):

    av = statistics.mean([np.linalg.norm(XY[i]-XY[j]) for j in range(N)])

    ans += av

print(ans)
N=int(eval(input()))

xy=[list(map(int,input().split())) for i in range(N)]

print((sum(((xi-xj)**2+(yi-yj)**2)**.5 for xi,yi in xy for xj,yj in xy)/N))
import numpy as np
import statistics

N = int(eval(input()))
XY = np.array([tuple(map(int, input().split())) for i in range(N)])
ans = 0
for i in range(N):
    av = statistics.mean([np.linalg.norm(XY[i] - XY[j]) for j in range(N)])
    ans += av
print(ans)

N = int(eval(input()))
xy = [list(map(int, input().split())) for i in range(N)]
print(
    (
        sum(((xi - xj) ** 2 + (yi - yj) ** 2) ** 0.5 for xi, yi in xy for xj, yj in xy)
        / N
    )
)

-import numpy as np -import statistics - -XY = np.array([tuple(map(int, input().split())) for i in range(N)]) -ans = 0 -for i in range(N): -    av = statistics.mean([np.linalg.norm(XY[i] - XY[j]) for j in range(N)]) -    ans += av -print(ans) +xy = [list(map(int, input().split())) for i in range(N)] +print( +    ( +        sum(((xi - xj) ** 2 + (yi - yj) ** 2) ** 0.5 for xi, yi in xy for xj, yj in xy) +        / N +    ) +)