2017.04.10：python数据可视化01

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本文介绍了一种基于修改后的Z得分的异常值检测方法。该方法首先计算数据集的中位数，接着计算每个数据点与中位数之间的差异，并通过调整得到修改后的Z得分。当得分超过设定阈值时，则认为该数据点为异常值。

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def is_outlier(points, threshold=3.5):
    """
    Returns a boolean array with True if points are outliers and False 
    otherwise.
    
    Data points with a modified z-score greater than this 
    # value will be classified as outliers.
    """
    # transform into vector
    if len(points.shape) == 1:
        points = points[:,None]

    # compute median value
    # axis=0表述列; axis=1,表述行
    median = np.median(points, axis=0)
    
    # compute diff sums along the axis
    diff = np.sum((points - median)**2, axis=-1)
    diff = np.sqrt(diff)
    # compute MAD
    med_abs_deviation = np.median(diff)
    
    # compute modified Z-score
    # http://www.itl.nist.gov/div898/handbook/eda/section4/eda43.htm#Iglewicz
    modified_z_score = 0.6745 * diff / med_abs_deviation

    # return a mask for each outlier
    return modified_z_score > threshold