"""Demo of the histogram (hist) function with a few features.In addition to the basic histogram, this demo shows a few optional features:    * Setting the number of data bins    * The ``normed`` flag, which normalizes bin heights so that the integral of      the histogram is 1. The resulting histogram is a probability density.    * Setting the face color of the bars    * Setting the opacity (alpha value).x : (n,) array or sequence of (n,) arrays这个参数是指定每个bin(箱子)分布的数据,对应x轴bins : integer or array_like, optional这个参数指定bin(箱子)的个数,也就是总共有几条条状图normed : boolean, optionalIf True, the first element of the return tuple will be the counts normalized to form a probability density, i.e.,n/(len(x)`dbin)这个参数指定密度,也就是每个条状图的占比例比,默认为1color : color or array_like of colors or None, optional这个指定条状图的颜色我们绘制一个10000个数据的分布条状图,共50份,以统计10000分的分布情况"""import numpy as npimport matplotlib.mlab as mlabimport matplotlib.pyplot as plt# example datamu = 100 # mean of distributionsigma = 15 # standard deviation of distributionx = mu + sigma * np.random.randn(10000)num_bins = 7# the histogram of the datan, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.9)# add a 'best fit' liney = mlab.normpdf(bins, mu, sigma)plt.plot(bins, y, 'r--')plt.xlabel('Smarts')plt.ylabel('Probability')plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')# Tweak spacing to prevent clipping of ylabelplt.subplots_adjust(left=0.15)plt.show()plt.plot(n)plt.title('n values')plt.show()plt.plot(bins)plt.title('bins values')plt.show()