Awesome
mapclassify: Classification Schemes for Choropleth Maps
mapclassify implements a family of classification schemes for choropleth maps.
Its focus is on the determination of the number of classes, and the assignment
of observations to those classes. It is intended for use with upstream mapping
and geovisualization packages (see
geopandas)
that handle the rendering of the maps.
For further theoretical background see Rey, S.J., D. Arribas-Bel, and L.J. Wolf (2020) "Geographic Data Science with PySAL and the PyData Stack”.
Using mapclassify
Load built-in example data reporting employment density in 58 California counties:
>>> import mapclassify
>>> y = mapclassify.load_example()
>>> y.mean()
125.92810344827588
>>> y.min(), y.max()
(0.13, 4111.4499999999998)
Map Classifiers Supported
BoxPlot
>>> mapclassify.BoxPlot(y)
BoxPlot
Interval Count
--------------------------
( -inf, -52.88] | 0
( -52.88, 2.57] | 15
( 2.57, 9.36] | 14
( 9.36, 39.53] | 14
( 39.53, 94.97] | 6
( 94.97, 4111.45] | 9
EqualInterval
>>> mapclassify.EqualInterval(y)
EqualInterval
Interval Count
--------------------------
[ 0.13, 822.39] | 57
( 822.39, 1644.66] | 0
(1644.66, 2466.92] | 0
(2466.92, 3289.19] | 0
(3289.19, 4111.45] | 1
FisherJenks
>>> import numpy as np
>>> np.random.seed(123456)
>>> mapclassify.FisherJenks(y, k=5)
FisherJenks
Interval Count
--------------------------
[ 0.13, 75.29] | 49
( 75.29, 192.05] | 3
( 192.05, 370.50] | 4
( 370.50, 722.85] | 1
( 722.85, 4111.45] | 1
FisherJenksSampled
>>> np.random.seed(123456)
>>> x = np.random.exponential(size=(10000,))
>>> mapclassify.FisherJenks(x, k=5)
FisherJenks
Interval Count
----------------------
[ 0.00, 0.64] | 4694
( 0.64, 1.45] | 2922
( 1.45, 2.53] | 1584
( 2.53, 4.14] | 636
( 4.14, 10.61] | 164
>>> mapclassify.FisherJenksSampled(x, k=5)
FisherJenksSampled
Interval Count
----------------------
[ 0.00, 0.70] | 5020
( 0.70, 1.63] | 2952
( 1.63, 2.88] | 1454
( 2.88, 5.32] | 522
( 5.32, 10.61] | 52
HeadTailBreaks
>>> mapclassify.HeadTailBreaks(y)
HeadTailBreaks
Interval Count
--------------------------
[ 0.13, 125.93] | 50
( 125.93, 811.26] | 7
( 811.26, 4111.45] | 1
JenksCaspall
>>> mapclassify.JenksCaspall(y, k=5)
JenksCaspall
Interval Count
--------------------------
[ 0.13, 1.81] | 14
( 1.81, 7.60] | 13
( 7.60, 29.82] | 14
( 29.82, 181.27] | 10
( 181.27, 4111.45] | 7
JenksCaspallForced
>>> mapclassify.JenksCaspallForced(y, k=5)
JenksCaspallForced
Interval Count
--------------------------
[ 0.13, 1.34] | 12
( 1.34, 5.90] | 12
( 5.90, 16.70] | 13
( 16.70, 50.65] | 9
( 50.65, 4111.45] | 12
JenksCaspallSampled
>>> mapclassify.JenksCaspallSampled(y, k=5)
JenksCaspallSampled
Interval Count
--------------------------
[ 0.13, 12.02] | 33
( 12.02, 29.82] | 8
( 29.82, 75.29] | 8
( 75.29, 192.05] | 3
( 192.05, 4111.45] | 6
MaxP
>>> mapclassify.MaxP(y)
MaxP
Interval Count
--------------------------
[ 0.13, 8.70] | 29
( 8.70, 16.70] | 8
( 16.70, 20.47] | 1
( 20.47, 66.26] | 10
( 66.26, 4111.45] | 10
MaximumBreaks
>>> mapclassify.MaximumBreaks(y, k=5)
MaximumBreaks
Interval Count
--------------------------
[ 0.13, 146.00] | 50
( 146.00, 228.49] | 2
( 228.49, 546.67] | 4
( 546.67, 2417.15] | 1
(2417.15, 4111.45] | 1
NaturalBreaks
>>> mapclassify.NaturalBreaks(y, k=5)
NaturalBreaks
Interval Count
--------------------------
[ 0.13, 75.29] | 49
( 75.29, 192.05] | 3
( 192.05, 370.50] | 4
( 370.50, 722.85] | 1
( 722.85, 4111.45] | 1
Quantiles
>>> mapclassify.Quantiles(y, k=5)
Quantiles
Interval Count
--------------------------
[ 0.13, 1.46] | 12
( 1.46, 5.80] | 11
( 5.80, 13.28] | 12
( 13.28, 54.62] | 11
( 54.62, 4111.45] | 12
Percentiles
>>> mapclassify.Percentiles(y, pct=[33, 66, 100])
Percentiles
Interval Count
--------------------------
[ 0.13, 3.36] | 19
( 3.36, 22.86] | 19
( 22.86, 4111.45] | 20
PrettyBreaks
>>> np.random.seed(123456)
>>> x = np.random.randint(0, 10000, (100,1))
>>> mapclassify.PrettyBreaks(x)
Pretty
Interval Count
----------------------------
[ 300.00, 2000.00] | 23
( 2000.00, 4000.00] | 15
( 4000.00, 6000.00] | 18
( 6000.00, 8000.00] | 24
( 8000.00, 10000.00] | 20
StdMean
>>> mapclassify.StdMean(y)
StdMean
Interval Count
--------------------------
( -inf, -967.36] | 0
(-967.36, -420.72] | 0
(-420.72, 672.57] | 56
( 672.57, 1219.22] | 1
(1219.22, 4111.45] | 1
UserDefined
>>> mapclassify.UserDefined(y, bins=[22, 674, 4112])
UserDefined
Interval Count
--------------------------
[ 0.13, 22.00] | 38
( 22.00, 674.00] | 18
( 674.00, 4112.00] | 2
Alternative API
As of version 2.4.0 the API has been extended. A classify function is now
available for a streamlined interface:
>>> classify(y, 'boxplot')
BoxPlot
Interval Count
--------------------------
( -inf, -52.88] | 0
( -52.88, 2.57] | 15
( 2.57, 9.36] | 14
( 9.36, 39.53] | 14
( 39.53, 94.97] | 6
( 94.97, 4111.45] | 9
Use Cases
Creating and using a classification instance
>>> bp = mapclassify.BoxPlot(y)
>>> bp
BoxPlot
Interval Count
--------------------------
( -inf, -52.88] | 0
( -52.88, 2.57] | 15
( 2.57, 9.36] | 14
( 9.36, 39.53] | 14
( 39.53, 94.97] | 6
( 94.97, 4111.45] | 9
>>> bp.bins
array([ -5.28762500e+01, 2.56750000e+00, 9.36500000e+00,
3.95300000e+01, 9.49737500e+01, 4.11145000e+03])
>>> bp.counts
array([ 0, 15, 14, 14, 6, 9])
>>> bp.yb
array([5, 1, 2, 3, 2, 1, 5, 1, 3, 3, 1, 2, 2, 1, 2, 2, 2, 1, 5, 2, 4, 1, 2,
2, 1, 1, 3, 3, 3, 5, 3, 1, 3, 5, 2, 3, 5, 5, 4, 3, 5, 3, 5, 4, 2, 1,
1, 4, 4, 3, 3, 1, 1, 2, 1, 4, 3, 2])
Binning new data
>>> bp = mapclassify.BoxPlot(y)
>>> bp
BoxPlot
Interval Count
--------------------------
( -inf, -52.88] | 0
( -52.88, 2.57] | 15
( 2.57, 9.36] | 14
( 9.36, 39.53] | 14
( 39.53, 94.97] | 6
( 94.97, 4111.45] | 9
>>> bp.find_bin([0, 7, 3000, 48])
array([1, 2, 5, 4])
Note that find_bin does not recalibrate the classifier:
>>> bp
BoxPlot
Interval Count
--------------------------
( -inf, -52.88] | 0
( -52.88, 2.57] | 15
( 2.57, 9.36] | 14
( 9.36, 39.53] | 14
( 39.53, 94.97] | 6
( 94.97, 4111.45] | 9
Apply
>>> import mapclassify
>>> import pandas
>>> from numpy import linspace as lsp
>>> data = [lsp(3,8,num=10), lsp(10, 0, num=10), lsp(-5, 15, num=10)]
>>> data = pandas.DataFrame(data).T
>>> data
0 1 2
0 3.000000 10.000000 -5.000000
1 3.555556 8.888889 -2.777778
2 4.111111 7.777778 -0.555556
3 4.666667 6.666667 1.666667
4 5.222222 5.555556 3.888889
5 5.777778 4.444444 6.111111
6 6.333333 3.333333 8.333333
7 6.888889 2.222222 10.555556
8 7.444444 1.111111 12.777778
9 8.000000 0.000000 15.000000
>>> data.apply(mapclassify.Quantiles.make(rolling=True))
0 1 2
0 0 4 0
1 0 4 0
2 1 4 0
3 1 3 0
4 2 2 1
5 2 1 2
6 3 0 4
7 3 0 4
8 4 0 4
9 4 0 4
Development Notes
Because we use geopandas in development, and geopandas has stable mapclassify as a dependency, setting up a local development installation involves creating a conda environment, then replacing the stable mapclassify with the development version of mapclassify in the development environment. This can be accomplished with the following steps:
conda-env create -f environment.yml
conda activate mapclassify
conda remove -n mapclassify mapclassify
pip install -e .