如何在python中向直方图添加误差线 [英] how to add error bars to histogram diagram in python

问题描述

我想在此代码中向直方图添加误差线.我很少看到有关它的文章，但我发现它们没有帮助.此代码产生具有高斯分布的随机数，并且对其应用了内核估计.需要有误差条来估计直方图在改变带宽时不准确的程度

Hi I want to add error bars to the histogram within this code.I have seen few post about it but I didn't find them helpful.this code produce random numbers with Gaussian distribution and a kernel estimation apply to it.I need to have errorbars to estimate how much the histogram is inaccurate with changing the bandwidth

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()

推荐答案

将上面提到的 answer 与您的代码相结合:

Combining the answer mentioned above with your code:

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()

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