lets_plot.geom_pie

lets_plot.geom_pie(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, labels=None, map=None, map_join=None, use_crs=None, hole=None, fill_by=None, stroke=None, stroke_color=None, **other_args)

Draw pie chart.

Parameters
mappingFeatureSpec

Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or DataFrame or polars.DataFrame or GeoDataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’count2d’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count2d’ (counts number of points with same x,y coordinate).

positionstr or FeatureSpec

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

show_legendbool, default=True

False - do not show legend for this layer.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).

tooltipslayer_tooltips

Result of the call to the layer_tooltips() function. Specify appearance, style and content.

labelslayer_labels

Result of the call to the layer_labels() function. Specify style and content of the annotations.

mapGeoDataFrame or Geocoder

Data containing coordinates of points.

map_joinstr or list

Keys used to join map coordinates with data. First value in pair - column/columns in data. Second value in pair - column/columns in map.

use_crsstr, optional, default=”EPSG:4326” (aka WGS84)

EPSG code of the coordinate reference system (CRS) or the keyword “provided”. If an EPSG code is given, then all the coordinates in GeoDataFrame (see the map parameter) will be projected to this CRS. Specify “provided” to disable any further re-projection and to keep the GeoDataFrame’s original CRS.

holefloat, default=0.0

A multiplicative factor applied to the pie diameter to draw donut-like chart. Accept values between 0 and 1.

fill_by{‘fill’, ‘color’}, default=’fill’

Define the source aesthetic for geometry filling.

strokefloat, default=0.0

Width of slice borders.

stroke_colorstr, default=’white’.

Color of slice borders.

other_args

Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns
LayerSpec

Geom object specification.

Notes

Computed variables:

  • ..count.. : number of points with same (x,y) coordinate.

  • ..sum.. : total number of points with same (x,y) coordinate.

  • ..prop.. : groupwise proportion.

  • ..proppct.. : groupwise proportion in percent.

geom_pie() understands the following aesthetics mappings:

  • x : x-axis value.

  • y : y-axis value.

  • slice : values associated to pie sectors.

  • explode : values to explode slices away from their center point, detaching it from the main pie.

  • size : pie diameter.

  • fill : color of geometry filling (by default).

  • color (colour) : color of geometry filling if fill_by=’color’.

  • alpha : transparency level of the pie. Accept values between 0 and 1.

  • weight : used by ‘count2d’ stat to compute weighted sum instead of simple count.


The data and map parameters of GeoDataFrame type support shapes Point and MultiPoint.

The map parameter of Geocoder type implicitly invokes centroids() function.


The conventions for the values of map_join parameter are as follows:

  • Joining data and GeoDataFrame object

    Data has a column named ‘State_name’ and GeoDataFrame has a matching column named ‘state’:

    • map_join=[‘State_Name’, ‘state’]

    • map_join=[[‘State_Name’], [‘state’]]

  • Joining data and Geocoder object

    Data has a column named ‘State_name’. The matching key in Geocoder is always ‘state’ (providing it is a state-level geocoder) and can be omitted:

    • map_join=’State_Name’

    • map_join=[‘State_Name’]

  • Joining data by composite key

    Joining by composite key works like in examples above, but instead of using a string for a simple key you need to use an array of strings for a composite key. The names in the composite key must be in the same order as in the US street addresses convention: ‘city’, ‘county’, ‘state’, ‘country’. For example, the data has columns ‘State_name’ and ‘County_name’. Joining with a 2-keys county level Geocoder object (the Geocoder keys ‘county’ and ‘state’ are omitted in this case):

    • map_join=[‘County_name’, ‘State_Name’]


Examples

1from lets_plot import *
2LetsPlot.setup_html()
3data = {'name': ['a', 'b', 'c', 'd', 'b'], 'value': [40, 90, 10, 50, 20]}
4ggplot(data) + geom_pie(aes(slice='value', fill='name'), stat='identity')

1from lets_plot import *
2from lets_plot.mapping import *
3LetsPlot.setup_html()
4data = {'name': ['a', 'b', 'c', 'd', 'b'], 'value': [40, 90, 10, 50, 20]}
5ggplot(data) + geom_pie(aes(fill=as_discrete('name', order_by='..count..'), weight='value'), \
6                        size=15, hole=0.2, stroke=1.0, \
7                        tooltips=layer_tooltips().format('@{..prop..}', '.0%') \
8                                                 .line('count|@{..count..} (@{..prop..})') \
9                                                 .line('total|@{..sum..}'))

1from lets_plot import *
2from lets_plot.mapping import *
3LetsPlot.setup_html()
4data = {'name': ['a', 'b', 'c', 'd', 'b'], 'value': [40, 90, 10, 50, 20]}
5ggplot(data) + geom_pie(aes(fill=as_discrete('name', order_by='..count..'), weight='value'), \
6                        size=15, hole=0.2, stroke=1.0, \
7                        labels=layer_labels(['..proppct..']).format('..proppct..', '{.1f}%'))