mapclassify.Percentiles

class mapclassify.Percentiles(y, pct=[1, 10, 50, 90, 99, 100])[source]

Percentiles Map Classification

Parameters
yarray

attribute to classify

pctarray

percentiles default=[1,10,50,90,99,100]

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> p = mc.Percentiles(cal)
>>> p.bins
array([1.357000e-01, 5.530000e-01, 9.365000e+00, 2.139140e+02,
       2.179948e+03, 4.111450e+03])
>>> p.counts
array([ 1,  5, 23, 23,  5,  1])
>>> p2 = mc.Percentiles(cal, pct = [50, 100])
>>> p2.bins
array([   9.365, 4111.45 ])
>>> p2.counts
array([29, 29])
>>> p2.k
2
Attributes
ybarray

bin ids for observations (numpy array n x 1)

binsarray

the upper bounds of each class (numpy array k x 1)

kint

the number of classes

countsint

the number of observations falling in each class (numpy array k x 1)

__init__(self, y, pct=[1, 10, 50, 90, 99, 100])[source]

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

Methods

__init__(self, y[, pct])

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

update(self, y=None, inplace=False, \*\*kwargs)[source]

Add data or change classification parameters.

Parameters
yarray

(n,1) array of data to classify

inplacebool

whether to conduct the update in place or to return a copy estimated from the additional specifications.

Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.