API reference


mapclassify.Box_Plot(y[, hinge]) Box_Plot Map Classification
mapclassify.Equal_Interval(y[, k]) Equal Interval Classification
mapclassify.Fisher_Jenks(y[, k]) Fisher Jenks optimal classifier - mean based
mapclassify.Fisher_Jenks_Sampled(y[, k, …]) Fisher Jenks optimal classifier - mean based using random sample
mapclassify.HeadTail_Breaks(y) Head/tail Breaks Map Classification for Heavy-tailed Distributions
mapclassify.Jenks_Caspall(y[, k]) Jenks Caspall Map Classification
mapclassify.Jenks_Caspall_Forced(y[, k]) Jenks Caspall Map Classification with forced movements
mapclassify.Jenks_Caspall_Sampled(y[, k, pct]) Jenks Caspall Map Classification using a random sample
mapclassify.Max_P_Classifier(y[, k, initial]) Max_P Map Classification
mapclassify.Maximum_Breaks(y[, k, mindiff]) Maximum Breaks Map Classification
mapclassify.Natural_Breaks(y[, k, initial]) Natural Breaks Map Classification
mapclassify.Quantiles(y[, k]) Quantile Map Classification
mapclassify.Percentiles(y[, pct]) Percentiles Map Classification
mapclassify.Std_Mean(y[, multiples]) Standard Deviation and Mean Map Classification
mapclassify.User_Defined(y, bins) User Specified Binning


mapclassify.K_classifiers(y[, pct]) Evaluate all k-classifers and pick optimal based on k and GADF
mapclassify.gadf(y[, method, maxk, pct]) Evaluate the Goodness of Absolute Deviation Fit of a Classifier Finds the minimum value of k for which gadf>pct