mapclassify.UserDefined¶
-
class
mapclassify.
UserDefined
(y, bins)[source]¶ User Specified Binning
- Parameters
- yarray
(n,1), values to classify
- binsarray
(k,1), upper bounds of classes (have to be monotically increasing)
Notes
If upper bound of user bins does not exceed max(y) we append an additional bin.
Examples
>>> import mapclassify as mc >>> cal = mc.load_example() >>> bins = [20, max(cal)] >>> bins [20, 4111.45] >>> ud = mc.UserDefined(cal, bins) >>> ud.bins array([ 20. , 4111.45]) >>> ud.counts array([37, 21]) >>> bins = [20, 30] >>> ud = mc.UserDefined(cal, bins) >>> ud.bins array([ 20. , 30. , 4111.45]) >>> ud.counts array([37, 4, 17])
- Attributes
- ybarray
(n,1), bin ids for observations,
- binsarray
(k,1), the upper bounds of each class
- kint
the number of classes
- countsarray
(k,1), the number of observations falling in each class
Methods
__init__
(self, y, bins)Initialize self.
find_bin
(self, x)Sort input or inputs according to the current bin estimate
get_adcm
(self)Absolute deviation around class median (ADCM).
get_fmt
(self)get_gadf
(self)Goodness of absolute deviation of fit
get_legend_classes
(self[, fmt])Format the strings for the classes on the legend
get_tss
(self)Total sum of squares around 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
(self, gdf[, border_color, …])Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.
set_fmt
(self, fmt)table
(self)update
(self[, y, inplace])Add data or change classification parameters.
Attributes
fmt
-
plot
(self, gdf, border_color='lightgrey', border_width=0.1, title=None, legend=False, cmap='YlGnBu', axis_on=True, legend_kwds={'loc': 'lower right', 'fmt': '{:.2f}'}, file_name=None, dpi=600, ax=None)[source]¶ Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.
- gdfgeopandas geodataframe
Contains the geometry column for the choropleth map
- border_colorstring, optional
matplotlib color string to use for polygon border (Default: lightgrey)
- border_widthfloat, optional
width of polygon boarder (Default: 0.10)
- titlestring, optional
Title of map (Default: None)
- cmapstring, optional
matplotlib color string for color map to fill polygons (Default: YlGn)
- axis_onboolean, optional
Show coordinate axes (default True) (Default: True)
- legend_kwdsdict, optional
options for ax.legend() (Default: {“loc”: “lower right”, ‘fmt’:FMT})
- file_namestring, optional
Name of file to save figure to. (Default: None)
- dpiint, optional
Dots per inch for saved figure (Default: 600)
- axmatplotlib axis, optional
axis on which to plot the choropleth. (Default: None, so plots on the current figure)
- f,axtuple
matplotlib figure, axis on which the plot is made.
Examples
>>> import libpysal as lp >>> import geopandas >>> import mapclassify >>> gdf = geopandas.read_file(lp.examples.get_path("columbus.shp")) >>> q5 = mapclassify.Quantiles(gdf.CRIME) >>> q5.plot(gdf)
-
update
(self, y=None, inplace=False, \*\*kwargs)[source]¶ Add data or change classification parameters.
- Parameters
- yarray
(n,1) array of data to classify
- inplacebool
whether to conduct the update in place or to return a copy estimated from the additional specifications.
- Additional parameters provided in **kwargs are passed to the init
- function of the class. For documentation, check the class constructor.