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Overview of the mapclassify API¶
There are a number of ways to access the functionality in mapclassify
We first load the example dataset that we have seen earlier.
[1]:
import geopandas
import libpysal
import mapclassify
Current mapclassify version.
[2]:
mapclassify.__version__
[2]:
'2.4.2+107.gb97c316a.dirty'
[3]:
pth = libpysal.examples.get_path("columbus.shp")
gdf = geopandas.read_file(pth)
y = gdf.HOVAL
gdf.head()
[3]:
| AREA | PERIMETER | COLUMBUS_ | COLUMBUS_I | POLYID | NEIG | HOVAL | INC | CRIME | OPEN | ... | DISCBD | X | Y | NSA | NSB | EW | CP | THOUS | NEIGNO | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.309441 | 2.440629 | 2 | 5 | 1 | 5 | 80.467003 | 19.531 | 15.725980 | 2.850747 | ... | 5.03 | 38.799999 | 44.070000 | 1.0 | 1.0 | 1.0 | 0.0 | 1000.0 | 1005.0 | POLYGON ((8.62413 14.23698, 8.55970 14.74245, ... |
| 1 | 0.259329 | 2.236939 | 3 | 1 | 2 | 1 | 44.567001 | 21.232 | 18.801754 | 5.296720 | ... | 4.27 | 35.619999 | 42.380001 | 1.0 | 1.0 | 0.0 | 0.0 | 1000.0 | 1001.0 | POLYGON ((8.25279 14.23694, 8.28276 14.22994, ... |
| 2 | 0.192468 | 2.187547 | 4 | 6 | 3 | 6 | 26.350000 | 15.956 | 30.626781 | 4.534649 | ... | 3.89 | 39.820000 | 41.180000 | 1.0 | 1.0 | 1.0 | 0.0 | 1000.0 | 1006.0 | POLYGON ((8.65331 14.00809, 8.81814 14.00205, ... |
| 3 | 0.083841 | 1.427635 | 5 | 2 | 4 | 2 | 33.200001 | 4.477 | 32.387760 | 0.394427 | ... | 3.70 | 36.500000 | 40.520000 | 1.0 | 1.0 | 0.0 | 0.0 | 1000.0 | 1002.0 | POLYGON ((8.45950 13.82035, 8.47341 13.83227, ... |
| 4 | 0.488888 | 2.997133 | 6 | 7 | 5 | 7 | 23.225000 | 11.252 | 50.731510 | 0.405664 | ... | 2.83 | 40.009998 | 38.000000 | 1.0 | 1.0 | 1.0 | 0.0 | 1000.0 | 1007.0 | POLYGON ((8.68527 13.63952, 8.67758 13.72221, ... |
5 rows × 21 columns
Original API (< 2.4.0)¶
[4]:
bp = mapclassify.BoxPlot(y)
bp
[4]:
BoxPlot
Interval Count
----------------------
( -inf, -0.70] | 0
(-0.70, 25.70] | 13
(25.70, 33.50] | 12
(33.50, 43.30] | 12
(43.30, 69.70] | 7
(69.70, 96.40] | 5
Extended API (>= 2.40)¶
Note the original API is still available so this extension keeps backwards compatibility.
[5]:
bp = mapclassify.classify(y, "box_plot")
bp
[5]:
BoxPlot
Interval Count
----------------------
( -inf, -0.70] | 0
(-0.70, 25.70] | 13
(25.70, 33.50] | 12
(33.50, 43.30] | 12
(43.30, 69.70] | 7
(69.70, 96.40] | 5
[6]:
type(bp)
[6]:
mapclassify.classifiers.BoxPlot
[7]:
q5 = mapclassify.classify(y, "quantiles", k=5)
q5
[7]:
Quantiles
Interval Count
----------------------
[17.90, 23.08] | 10
(23.08, 30.48] | 10
(30.48, 39.10] | 9
(39.10, 45.83] | 10
(45.83, 96.40] | 10
Robustness of the scheme argument¶
[8]:
mapclassify.classify(y, "boxPlot")
[8]:
BoxPlot
Interval Count
----------------------
( -inf, -0.70] | 0
(-0.70, 25.70] | 13
(25.70, 33.50] | 12
(33.50, 43.30] | 12
(43.30, 69.70] | 7
(69.70, 96.40] | 5
[9]:
mapclassify.classify(y, "Boxplot")
[9]:
BoxPlot
Interval Count
----------------------
( -inf, -0.70] | 0
(-0.70, 25.70] | 13
(25.70, 33.50] | 12
(33.50, 43.30] | 12
(43.30, 69.70] | 7
(69.70, 96.40] | 5
[10]:
mapclassify.classify(y, "Box_plot")
[10]:
BoxPlot
Interval Count
----------------------
( -inf, -0.70] | 0
(-0.70, 25.70] | 13
(25.70, 33.50] | 12
(33.50, 43.30] | 12
(43.30, 69.70] | 7
(69.70, 96.40] | 5
[13]:
mapclassify.classify(y, 'Std_Mean')
[13]:
StdMean
Interval Count
----------------------
( -inf, 1.50] | 0
( 1.50, 19.97] | 5
(19.97, 56.90] | 37
(56.90, 75.37] | 3
(75.37, 96.40] | 4
[15]:
mapclassify.classify(y, 'Std_Mean', anchor=True)
[15]:
StdMean
Interval Count
----------------------
[17.90, 19.97] | 5
(19.97, 38.44] | 24
(38.44, 56.90] | 13
(56.90, 75.37] | 3
(75.37, 93.83] | 3
(93.83, 96.40] | 1
[16]:
y.mean(), y.std(), y.min(), y.max()
[16]:
(38.43622446938775, 18.466069465206047, 17.9, 96.400002)
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