I have created an IQR function to add to the other dispersion metrics such as standard deviation. I have described the purpose and nature of the proposal in PR#7137, so I am pasting the text here as well: Motivation ---------- This function is used in one place in numpy already (to compute the Freedman-Diaconis histogram bin estimator) in addition to being requested on Stack Overflow a couple of times: - http://stackoverflow.com/questions/23228244/how-do-you-find-the-iqr-in-numpy - http://stackoverflow.com/questions/27472330/how-should-the-interquartile-ran... It is also used in matplotlib for box and violin plots: http://matplotlib.org/faq/howto_faq.html#interpreting-box-plots-and-violin-p.... It is a very simple, common and robust dispersion estimator. There does not appear to be an implementation for it anywhere in numpy or scipy. About --------- This function is a convenience combination of `np.percentile` and `np.subtract`. As such, it allows the the difference between any two percentiles to be computed, not necessarily (25, 75), which is the default. All of the recent enhancements to percentile are used. The documentation and testing is borrowed heavily from `np.percentile`. Wikipedia Reference: https://en.wikipedia.org/wiki/Interquartile_range Note ---------- The tests will not pass until the bug-fix for `np.percentile` kwarg `interpolation='midpoint'` (#7129) is incorporated and this PR is rebased.