pysal.viz.splot.mapping.mapclassify_bin

pysal.viz.splot.mapping.mapclassify_bin(y, classifier, k=5, pct=[1, 10, 50, 90, 99, 100], hinge=1.5, multiples=[-2, -1, 1, 2], mindiff=0, initial=100, bins=None)[source]

Classify your data with pysal.mapclassify Note: Input parameters are dependent on classifier used.

Parameters:
y : array

(n,1), values to classify

classifier : str

pysal.mapclassify classification scheme

k : int, optional

The number of classes. Default=5.

pct : array, optional

Percentiles used for classification with percentiles. Default=[1,10,50,90,99,100]

hinge : float, optional

Multiplier for IQR when Box_Plot classifier used. Default=1.5.

multiples : array, optional

The multiples of the standard deviation to add/subtract from the sample mean to define the bins using std_mean. Default=[-2,-1,1,2].

mindiff : float, optional

The minimum difference between class breaks if using maximum_breaks classifier. Deafult =0.

initial : int

Number of initial solutions to generate or number of runs when using natural_breaks or max_p_classifier. Default =100. Note: setting initial to 0 will result in the quickest calculation of bins.

bins : array, optional

(k,1), upper bounds of classes (have to be monotically increasing) if using user_defined classifier. Default =None, Example =[20, max(y)].

Returns:
bins : pysal.mapclassify instance

Object containing bin ids for each observation (.yb), upper bounds of each class (.bins), number of classes (.k) and number of onservations falling in each class (.counts)

Note: Supported classifiers include: quantiles, box_plot, euqal_interval,

fisher_jenks, headtail_breaks, jenks_caspall, jenks_caspall_forced, max_p_classifier, maximum_breaks, natural_breaks, percentiles, std_mean, user_defined

Examples

Imports

>>> from pysal.lib import examples
>>> import geopandas as gpd
>>> from pysal.viz.splot.mapping import pysal.viz.mapclassify_bin

Load Example Data

>>> link_to_data = examples.get_path('columbus.shp')
>>> gdf = gpd.read_file(link_to_data)
>>> x = gdf['HOVAL'].values

Classify values by quantiles

>>> quantiles = mapclassify_bin(x, 'quantiles')

Classify values by box_plot and set hinge to 2

>>> box_plot = mapclassify_bin(x, 'box_plot', hinge=2)