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()