直方核密度分布圖

代碼示例

import numpy as np
from scipy.stats import norm
from scipy import stats
from matplotlib import pyplot as plt
import seaborn as sns

def distribution(data):
    
    sns.distplot(data, fit=norm,
                kde_kws={"label": "KDE"})
    (mu, sigma) = norm.fit(data)
#     print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
    skew = stats.skew(data)
    kurtosis = stats.kurtosis(data)
#     print( '\n skew = {:.2f} and kurt = {:.2f}\n'.format(skew,kurtosis))
    print('長度：'，len(data),'\n','均值：',mu,'\n','标準差：',sigma,'\n','偏度：',skew,'\n','峰度：',kurtosis,'\n')
    plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma),'KED','Histogram'],loc='best')
    plt.ylabel('Frequency')
    plt.title('Distribution')


distribution(ts01)
           

長度：2398

均值： 4113.451905754795

标準差： 437.0188164032393

偏度：-0.08015885063004205

峰度： -1.2589846224956012