hist的使用

hist的api参数很多,如果要知道每个含义得一个一个去试,看了doc,这个hist的参数挺多的,api中有个简明的例子,我们使用几个重要的参数即可

函数签名

 

matplotlib.pyplot.hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype=u'bar', align=u'mid', orientation=u'vertical', rwidth=None, log=False, color=None, label=None, stacked=False, hold=None, **kwargs)

 

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, optional

If 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1

color : 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

 

"""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)."""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 = 50# the histogram of the datan, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5)# 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()