Python Data Visualization Cookbook 2.9.2

 import numpy as np
 import matplotlib.pyplot as plt

 def is_outlier(points, threshold=3.5):
     if len(points.shape) == 1:
         points = points[:, None]

     # Find the median number of points
     median = np.median(points, axis=0)

     diff = np.sum((points - median)**2, axis=-1)
     diff = np.sqrt(diff)
     MAD = np.median(diff)

     MZS = 0.6745 * diff / MAD

     return MZS > threshold

 # Create 100 random numbers
 x = np.random.random(100)

 # The number of the histogram buckets
 buckets = 50

 # Add in a few outliers
 x = np.r_[x, -49, 95, 100, -100]

 # The function 'is_outlier()' return a array of boolean
 # If True, get the element; else pass the element
 # For example:
 # x = [1,2,3,4]
 # y = x[array([False,True,True,False])]
 # y is [2,3]
 filtered = x[~is_outlier(x)]

 # Create a new figure
 plt.figure()

 # Define the width of the figure
 plt.subplot(211)
 # Drawing histogram
 # histogram(arr,bins,normed,facecolor,edgecolor,alpha,histtype)
 plt.hist(x, buckets)
 plt.xlabel('Raw')

 plt.subplot(212)
 plt.hist(filtered, buckets)
 plt.xlabel('Cleaned')

 # Show the figure
 plt.show()