splot.mapping.mapclassify_bin¶
-
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
- yarray
(n,1), values to classify
- classifierstr
pysal.mapclassify classification scheme
- kint, optional
The number of classes. Default=5.
- pctarray, optional
Percentiles used for classification with percentiles. Default=[1,10,50,90,99,100]
- hingefloat, optional
Multiplier for IQR when BoxPlot classifier used. Default=1.5.
- multiplesarray, 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].
- mindifffloat, optional
The minimum difference between class breaks if using maximum_breaks classifier. Deafult =0.
- initialint
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.
- binsarray, optional
(k,1), upper bounds of classes (have to be monotically increasing) if using user_defined classifier. Default =None, Example =[20, max(y)].
- Returns
- binspysal.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 libpysal import examples >>> import geopandas as gpd >>> from splot.mapping import 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)