title: Standard Deviation vs Z-Score vs IQR for Analysis of 3D Numpy Array
body: <p>I am trying to analyze a large set of data in a 3D numpy array and am not sure on how to approach analyzing this data for outliers. I do not want to break my array’s size by completely throwing out data, since it would likely necessitate throwing out an entire slice of data.</p>
  <p>That being said, references on the different methods for examining outliers don’t show any specific best method to recalculate the outlier and replace it. What are the benefits and drawbacks for a randomly distributed set of data largely centered around a median and recalculating each outlier based on the different methods (st. dev., z-score, IQR, others?)?</p>
  <p>Further, what would be the most optimized method in Python be to handle very large 3D arrays on the order of several hundred thousands.</p>