geom_pie#
- geom_pie(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, labels=None, map=None, map_join=None, use_crs=None, hole=None, stroke_side=None, spacer_width=None, spacer_color=None, start=None, direction=None, size_unit=None, color_by=None, fill_by=None, **other_args)#
Draw pie chart.
- Parameters:
- mapping
FeatureSpec
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 Pandas or Polars
DataFrame
orGeoDataFrame
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 the same x,y coordinate).
- positionstr or
FeatureSpec
, default=’identity’ Position adjustment. Either a position adjustment name: ‘dodge’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).
- show_legendbool, default=True
False - do not show legend for this layer.
- inherit_aesbool, default=True
False - do not combine the layer aesthetic mappings with the plot shared mappings.
- manual_keystr or
layer_key
The key to show in the manual legend. Specify text for the legend label or advanced settings using the layer_key() function.
- sampling
FeatureSpec
Result of the call to the
sampling_xxx()
function. To prevent any sampling for this layer pass value “none” (string “none”).- tooltips
layer_tooltips
Result of the call to the layer_tooltips() function. Specify appearance, style and content. Set tooltips=’none’ to hide tooltips from the layer.
- labels
layer_labels
Result of the call to the layer_labels() function. Specify style and content of the annotations.
- map
GeoDataFrame
orGeocoder
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 inmap
.- 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 themap
parameter) will be projected to this CRS. Specify “provided” to disable any further re-projection and to keep theGeoDataFrame
’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.
- stroke_side{‘outer’, ‘inner’, ‘both’}, default=’both’
Define which arcs of pie sector should have a stroke.
- spacer_widthfloat, default=0.75
Line width between sectors in pixels. Spacers are not applied to exploded sectors and to sides of adjacent sectors.
- spacer_colorstr
Color for spacers between sectors. By default, the “paper” color is used.
- startfloat, default=None
Specify the angle at which the first sector starts. Accept values between 0 and 360. Default is a negative angle of the first sector.
- direction{1, -1}, default=1
Specify angle direction, 1=clockwise, -1=counter-clockwise.
- size_unit{‘x’, ‘y’, ‘min’, ‘max’}
Relate the size of the pie chart to the length of the unit step along one of the axes. ‘x’ uses the unit step along the x-axis, ‘y’ uses the unit step along the y-axis. ‘min’ uses the smaller of the unit steps along the x- and y-axes. ‘max’ uses the larger of the unit steps along the x- and y-axes. If None, no fitting is performed.
- color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’
Define the color aesthetic for the geometry.
- fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’
Define the source aesthetic for geometry filling.
- 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.
- mapping
- Returns:
LayerSpec
Geom object specification.
Notes
Computed variables:
..count.. : number of points with the same (x,y) coordinate.
..sum.. : total number of points with the same (x,y) coordinate.
..prop.. : groupwise proportion.
..proppct.. : groupwise proportion in percent.
..sumprop.. : proportion of points with the same (x,y) coordinate among all points in the dataset.
..sumpct.. : proportion of points with the same (x,y) coordinate among all points in the dataset 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. Accept values between 0 and 1.
size : pie diameter.
fill : fill color. For more info see Color and Fill.
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.
stroke : width of inner and outer arcs of pie sector.
color : color of inner and outer arcs of pie sector.
The
data
andmap
parameters ofGeoDataFrame
type support shapesPoint
andMultiPoint
.The
map
parameter ofGeocoder
type implicitly invokes get_centroids() function.
The conventions for the values of
map_join
parameter are as follows:Joining data and
GeoDataFrame
objectData 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
objectData 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 (theGeocoder
keys ‘county’ and ‘state’ are omitted in this case):map_join=[‘County_name’, ‘State_Name’]
To hide axis tooltips, set ‘blank’ or the result of element_blank() to the
axis_tooltip
,axis_tooltip_x
oraxis_tooltip_y
parameter of the theme().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 * 2LetsPlot.setup_html() 3data = {'name': ['a', 'b', 'c', 'd', 'b'], 'value': [40, 90, 10, 50, 20]} 4ggplot(data) + geom_pie(aes(fill='name', weight='value'), size=.5, size_unit='x')
1from lets_plot import * 2LetsPlot.setup_html() 3data = {'name': ['a', 'b', 'c', 'd', 'b'], 'value': [40, 90, 10, 50, 20], 'explode': [0, 0, 0.2, 0, 0]} 4ggplot(data) + geom_pie(aes(fill='name', weight='value', explode='explode'), \ 5 size=15, hole=0.2, color='black', stroke=2, stroke_side='both', \ 6 spacer_color='black', spacer_width=2)
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(fill=as_discrete('name', order_by='..count..'), weight='value'), \ 5 size=15, hole=0.2, \ 6 tooltips=layer_tooltips().format('@{..prop..}', '.0%') \ 7 .line('count|@{..count..} (@{..prop..})') \ 8 .line('total|@{..sum..}'))
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(fill=as_discrete('name', order_by='..count..'), weight='value'), \ 5 size=15, hole=0.2, \ 6 labels=layer_labels(['..proppct..']).format('..proppct..', '{.1f}%'))
1from lets_plot import * 2from lets_plot.geo_data import * 3LetsPlot.setup_html() 4data = {"city": ["New York", "New York", "Philadelphia", "Philadelphia"], \ 5 "est_pop_2020": [4_381_593, 3_997_959, 832_685, 748_846], \ 6 "sex": ["female", "male", "female", "male"]} 7centroids = geocode_cities(data["city"]).get_centroids() 8ggplot() + geom_livemap() + \ 9 geom_pie(aes(slice="est_pop_2020", fill="sex", size="est_pop_2020"), \ 10 stat='identity', data=data, map=centroids, map_join="city") + \ 11 scale_size(guide='none')
The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).