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) 

  """ 
  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()