Enter your code here. Read input from STDIN. Print output to STDOUT

import numpy as np import pandas as pd from functools import reduce from scipy.stats import norm

i = int(input()) l = input() ls = list(map(lambda x: int(x), l.split()))

def mean(ls, n): return reduce(lambda acc, cur : acc + cur, ls, 0) / n

def median(ls, n): ls_sort = ls ls_sort.sort() if n % 2 != 0: return ls_sort[int(n / 2)]

return (ls_sort[int(n / 2)] + ls_sort[int(n / 2) - 1]) / 2

def mode(ls, n): keys = list(set(ls)) keys.sort() freq_el = {} for key in keys: freq_el[key] = 0

for el in ls:
    freq_el[el] += 1

sorted_freq = dict(sorted(freq_el.items(), key = lambda item : item[1], reverse = True))

return list(sorted_freq.keys())[0]

def std(ls, n): m = mean(ls, n) st = ((reduce(lambda acc, cur: acc + (cur - m)**2 , ls, 0)) / n)**0.5 return round(st, 1)

def Confidence_Interval(ls, n): m = mean(ls, n) st = std(ls, n) # z_score = norm.ppf(0.975) z_score = 1.96

upperBound = m + z_score * (st / n**0.5)

lowerBound = m - z_score * (st / n**0.5)

return (round(lowerBound, 1), round(upperBound, 1))

print(mean(ls, i))
print(median(ls, i))
print(mode(ls, i))
print(std(ls, i)) print(*Confidence_Interval(ls, i))