jupyter_analysis_tools.analysis.getModZScore

jupyter_analysis_tools.analysis.getModZScore(points)[source]

Returns a boolean array with True if points are outliers and False otherwise. Note: Similar to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.zscore.html but uses the median instead of the mean.

Parameters:

  • points – An numobservations by numdimensions array of observations

  • thresh – The modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers.

Returns:

mask (numpy array) – A numobservations-length boolean array.

References

Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.