对数据的归一化

import numpy as np
import matplotlib.pyplot as plt
def Normalization1(x):
    '''归一化（0~1）'''
    '''x_=(x−x_min)/(x_max−x_min)'''
    return [(float(i)-min(x))/float(max(x)-min(x)) for i in x]
def Normalization2(x):
    '''归一化（-1~1）'''
    '''x_=(x−x_mean)/(x_max−x_min)'''
    return [(float(i)-np.mean(x))/(max(x)-min(x)) for i in x]
def z_score(x):
    '''标准化（μ=0，σ=1）'''
    '''x∗=(x−μ)/σ'''
    x_mean=np.mean(x)
    s2=sum([(i-np.mean(x))*(i-np.mean(x)) for i in x])/len(x)
    return [(i-x_mean)/s2 for i in x]

l=[-10, 5, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 15, 15, 30]
l1=[]
# for i in l:
#     i+=2
#     l1.append(i)
# print(l1)
cs=[]
for i in l:
    c=l.count(i)
    cs.append(c)
print(cs)
n=Normalization2(l)
z=z_score(l)
print(n)
print(z)
plt.plot(l,cs)
plt.plot(z,cs)
plt.show()