mapclassify.classify

mapclassify.classify(y, scheme, k=5, pct=[1, 10, 50, 90, 99, 100], pct_sampled=0.1, truncate=True, hinge=1.5, multiples=[-2, -1, 1, 2], mindiff=0, initial=100, bins=None, lowest=None, anchor=False)

Classify your data with mapclassify.classify. Input parameters are dependent on classifier used.

Parameters:

ynumpy.array

\((n,1)\), values to classify.

schemestr

pysal.mapclassify classification scheme.

kint (default 5)

The number of classes.

pctnumpy.array (default [1, 10, 50, 90, 99, 100])

Percentiles used for classification with percentiles.

pct_sampledfloat default (0.10)

The percentage of n that should form the sample (JenksCaspallSampled, FisherJenksSampled) If pct is specified such that n*pct > 1000, then pct=1000.

truncatebool (default True)

Truncate pct_sampled in cases where pct * n > 1000.

hingefloat (default 1.5)

Multiplier for IQR when BoxPlot classifier used.

multiplesnumpy.array (default [-2,-1,1,2])

The multiples of the standard deviation to add/subtract from the sample mean to define the bins using std_mean.

mindifffloat (default is 0)

The minimum difference between class breaks if using maximum_breaks classifier.

initialint (default 100)

Number of initial solutions to generate or number of runs when using natural_breaks or max_p_classifier. Setting initial to 0 will result in the quickest calculation of bins.

binsnumpy.array (default None)

\((k,1)\), upper bounds of classes (have to be monotically increasing) if using user_defined classifier. Default is None. For example: [20, max(y)].

lowestfloat (default None)

Scalar minimum value of lowest class. Default is to set the minimum to -inf if y.min() > first upper bound (which will override the default), otherwise minimum is set to y.min().

anchorbool (default False)

Anchor upper bound of one class to the sample mean.

Returns:

classifiermapclassify.classifiers.MapClassifier

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

Notes

Supported classifiers include:

• quantiles

• boxplot

• equalinterval

• fisherjenks

• fisherjenkssampled

• headtailbreaks

• jenkscaspall

• jenkscaspallsampled

• jenks_caspallforced

• maxp

• maximumbreaks

• naturalbreaks

• percentiles

• prettybreaks

• stdmean

• userdefined

Examples

>>> import libpysal
>>> import geopandas
>>> from mapclassify import classify

Load example data.

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

Classify values by quantiles.

>>> quantiles = classify(x, "quantiles")

Classify values by box_plot and set hinge to 2.

>>> box_plot = classify(x, 'box_plot', hinge=2)
>>> box_plot
BoxPlot

   Interval      Count
----------------------
( -inf, -9.50] |     0
(-9.50, 25.70] |    13
(25.70, 33.50] |    12
(33.50, 43.30] |    12
(43.30, 78.50] |     9
(78.50, 96.40] |     3