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, stroke_side=None, spacer_width=None, spacer_color=None, size_unit=None, color_by=None, fill_by=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 Pandas 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, default=’identity’
Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘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.
- 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.
- 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. 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.
- size_unit{‘x’, ‘y’}
Relate the size of the pie chart to the length of the unit step along one of the 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.
- 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.
..sumprop.. : proportion of points with same (x,y) coordinate among all points in the dataset.
..sumpct.. : proportion of points with 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.
size : pie diameter.
fill : fill color. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).
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 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 * 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 * 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, \ 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, \ 7 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).