mapclassify.MaximumBreaks¶

class mapclassify.MaximumBreaks(y, k=5, mindiff=0)[source]

Maximum Breaks Map Classification.

Parameters:
ynumpy.array

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

kint (default 5)

The number of classes required.

mindifffloat (default 0)

The minimum difference between class breaks.

Examples

>>> import mapclassify
>>> mb = mapclassify.MaximumBreaks(cal, k=5)
>>> mb.k
5

>>> mb.bins
array([ 146.005,  228.49 ,  546.675, 2417.15 , 4111.45 ])

>>> mb.counts.tolist()
[50, 2, 4, 1, 1]

Attributes:
ybnumpy.array

$$(n,1)$$, bin IDs for observations.

binsnumpy.array

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

kint

The number of classes.

countsnumpy.array

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

__init__(y, k=5, mindiff=0)[source]

Methods

 __init__(y[, k, mindiff]) find_bin(x) Sort input or inputs according to the current bin estimate. get_adcm() Absolute deviation around class median (ADCM). get_fmt() get_gadf() Goodness of absolute deviation of fit. get_legend_classes([fmt]) Format the strings for the classes on the legend. get_tss() Returns sum of squares over all 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(gdf[, border_color, border_width, ...]) Plot a mapclassifier object. plot_histogram([color, linecolor, ...]) Plot histogram of y with bin values superimposed set_fmt(fmt) table() update([y, inplace]) Add data or change classification parameters.

Attributes

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

Add data or change classification parameters.

Parameters:
ynumpy.array (default None)

$$(n,1)$$, array of data to classify.

inplacebool (default False)

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

**kwargsdict

Additional parameters that are passed to the __init__ function of the class. For documentation, check the class constructor.