API reference


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.


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.Pooled(Y[, classifier])

Applying global binning across columns

mapclassify.StdMean(y[, multiples])

Standard Deviation and Mean Map Classification

mapclassify.UserDefined(y, bins[, lowest])

User Specified Binning


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.