如下所示：

 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 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() 

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