在python中做正态性检验示例

 s.sort_values(by = 'value',inplace = True)
 s_r = s.reset_index(drop=False)

s_r['p'] = (s_r.index - 0.5)/len(s_r)
s_r['q'] = (s_r['value'] - mean) / std
print(s_r.head())
# 计算百分位数
# 计算q值

ax3 = fig.add_subplot(3,1,3)
ax3.plot(s_r['p'],s_r['value'],'k',alpha=0.5,linewidth = 3)

st = s['value'].describe()
x1 ,y1 = 0.25, st['25%']
x2 ,y2 = 0.75, st['75%']
ax3.plot([x1,x2],[y1,y2],'-r',linewidth = 3)

 

# 直接用算法做KS检验

from scipy import stats
stats.kstest(df['value'], 'norm', (u, std))
# 结果返回两个值：statistic → D值，pvalue → P值
# p值大于0.05，很可能为正态分布
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