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

%pylab inline
import scipy.stats.stats as st
from scipy.stats import norm
x_norm = norm.rvs(size=500)
h = hist(x_norm)
print 'counts, ', h[0]
print 'bin centers', h[1]

这是一个很简单的例子，还有另一个例子。

 """
 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 np
 import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt


 # example data
 mu = 100 # mean of distribution
 sigma = 15 # standard deviation of distribution
 x = mu + sigma * np.random.randn(10000)

 num_bins = 50
 # the histogram of the data
 n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5)
 # add a 'best fit' line
 y = 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 ylabel
 plt.subplots_adjust(left=0.15)
 plt.show()

参考： http://blog.csdn.net/u013571243/article/details/48998619