API reference

Classifiers

mapclassify.BoxPlot(y[, hinge])	BoxPlot Map Classification
mapclassify.EqualInterval(y[, k])	Equal Interval Classification
mapclassify.FisherJenks(y[, k])	Fisher Jenks optimal classifier - mean based
mapclassify.FisherJenksSampled(y[, k, pct, …])	Fisher Jenks optimal classifier - mean based using random sample
mapclassify.greedy(gdf[, strategy, balance, …])	Color GeoDataFrame using various strategies of greedy (topological) colouring.
mapclassify.HeadTailBreaks(y)	Head/tail Breaks Map Classification for Heavy-tailed Distributions
mapclassify.JenksCaspall(y[, k])	Jenks Caspall Map Classification
mapclassify.JenksCaspallForced(y[, k])	Jenks Caspall Map Classification with forced movements
mapclassify.JenksCaspallSampled(y[, k, pct])	Jenks Caspall Map Classification using a random sample
mapclassify.MaxP(y[, k, initial])	MaxP Map Classification
mapclassify.MaximumBreaks(y[, k, mindiff])	Maximum Breaks Map Classification
mapclassify.NaturalBreaks(y[, k, initial])	Natural Breaks Map Classification
mapclassify.Quantiles(y[, k])	Quantile Map Classification
mapclassify.Percentiles(y[, pct])	Percentiles Map Classification
mapclassify.StdMean(y[, multiples])	Standard Deviation and Mean Map Classification
mapclassify.UserDefined(y, bins)	User Specified Binning

Utilities

mapclassify.KClassifiers(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
mapclassify.classify(y, scheme[, k, pct, …])	Classify your data with mapclassify.classify Note: Input parameters are dependent on classifier used.