python - 如何在python中将误差线添加到直方图

嗨，我想在这段代码中添加误差条到直方图中。我很少看到关于它的文章，但我没有发现它们有帮助。这段代码生成高斯分布的随机数，并应用核估计。我需要有误差条来估计随着带宽的改变直方图有多不准确

from random import *
import numpy as np
from matplotlib.pyplot import*
from matplotlib import*
import scipy.stats as stats

def hist_with_kde(data, bandwidth = 0.3):
    #set number of bins using Freedman and Diaconis
    q1 = np.percentile(data,25)
    q3 = np.percentile(data,75)


    n = len(data)**(.1/.3)
    rng = max(data) - min(data)
    iqr = 2*(q3-q1)

    bins =int((n*rng)/iqr)
    print(bins)
    x = np.linspace(min(data),max(data),200)

    kde = stats.gaussian_kde(data,'scott')

    kde._compute_covariance()
    kde.set_bandwidth()


    plot(x,kde(x),'r') # distribution function
    hist(data,bins=bins,normed=True) # histogram

data = np.random.normal(0,1,1000)
hist_with_kde(data,30)

show()

最佳答案

将上面提到的answer与您的代码结合起来：

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats


def hist_with_kde(data, bandwidth = 0.3):
    #set number of bins using Freedman and Diaconis
    q1 = np.percentile(data, 25)
    q3 = np.percentile(data, 75)

    n = len(data)**(.1/.3)
    rng = max(data) - min(data)
    iqr = 2*(q3-q1)

    bins =int((n*rng)/iqr)
    print(bins)
    x = np.linspace(min(data), max(data), 200)

    kde = stats.gaussian_kde(data, 'scott')

    kde._compute_covariance()
    kde.set_bandwidth()

    plt.plot(x, kde(x), 'r')  # distribution function

    y, binEdges = np.histogram(data, bins=bins, normed=True)
    bincenters = 0.5*(binEdges[1:]+binEdges[:-1])
    menStd = np.sqrt(y)
    width = 0.2
    plt.bar(bincenters, y, width=width, color='r', yerr=menStd)


data = np.random.normal(0, 1, 1000)
hist_with_kde(data, 30)

plt.show()

看看进口产品，正如MaxNoe

关于python - 如何在python中将误差线添加到直方图，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/35390276/