mapclassify.Quantiles

class mapclassify.Quantiles(y, k=5)[source]

Quantile Map Classification

Parameters
yarray

(n,1), values to classify

kint

number of classes required

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> q = mc.Quantiles(cal, k = 5)
>>> q.bins
array([1.46400e+00, 5.79800e+00, 1.32780e+01, 5.46160e+01, 4.11145e+03])
>>> q.counts
array([12, 11, 12, 11, 12])
Attributes
ybarray

(n,1), bin ids for observations, each value is the id of the class the observation belongs to yb[i] = j for j>=1 if bins[j-1] < y[i] <= bins[j], yb[i] = 0 otherwise

binsarray

(k,1), the upper bounds of each class

kint

the number of classes

countsarray

(k,1), the number of observations falling in each class

__init__(self, y, k=5)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, y[, k])

Initialize self.

find_bin(self, x)

Sort input or inputs according to the current bin estimate

get_adcm(self)

Absolute deviation around class median (ADCM).

get_fmt(self)

get_gadf(self)

Goodness of absolute deviation of fit

get_legend_classes(self[, fmt])

Format the strings for the classes on the legend

get_tss(self)

Total sum of squares around class means

make(\*args, \*\*kwargs)

Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function.

plot(self, gdf[, border_color, …])

Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.

set_fmt(self, fmt)

table(self)

update(self[, y, inplace])

Add data or change classification parameters.

Attributes

fmt