geom_point#
- geom_point(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, map=None, map_join=None, use_crs=None, size_unit=None, color_by=None, fill_by=None, **other_args)#
Draw points defined by x and y coordinates, as for a scatter plot.
- 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=’identity’
The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with the same x-axis coordinate), ‘bin’ (counts number of points with the x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate), ‘sum’ (counts the number of points at each location - might help to work around overplotting).
- 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.
- 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.- size_unit{‘x’, ‘y’, ‘min’, ‘max’}
Relate the size of the point 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 fill aesthetic for the geometry.
- 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
The point geometry is used to create scatterplots. The scatterplot is useful for displaying the relationship between two continuous variables, although it can also be used with one continuous and one categorical variable, or two categorical variables.
geom_point()
understands the following aesthetics mappings:x : x-axis value.
y : y-axis value.
alpha : transparency level of the point. Accept values between 0 and 1.
color (colour) : color of the geometry. For more info see Color and Fill.
fill : fill color. Is applied only to the points of shapes having inner area. For more info see Color and Fill.
shape : shape of the point, an integer from 0 to 25. For more info see Point Shapes.
angle : rotation angle of the point shape, in degrees.
size : size of the point.
stroke : width of the shape border. Applied only to the shapes having border.
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
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4x = np.linspace(-2 * np.pi, 2 * np.pi, 100) 5y = np.sin(x) 6ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + geom_point()
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4np.random.seed(42) 5n = 100 6x = np.random.uniform(-1, 1, size=n) 7y = 25 * x ** 2 + np.random.normal(size=n) 8ggplot({'x': x, 'y': y}) + \ 9 geom_point(aes(x='x', y='y', fill='y'), \ 10 shape=21, size=5, color='white')
1from lets_plot import * 2from lets_plot.geo_data import * 3LetsPlot.setup_html() 4data = {"city": ["New York", "Los Angeles", "Chicago"], \ 5 "est_pop_2019": [8_336_817, 3_979_576, 2_693_976]} 6centroids = geocode_cities(data["city"]).get_centroids() 7ggplot() + geom_livemap() + \ 8 geom_point(aes(size="est_pop_2019"), color="red", show_legend=False, \ 9 data=data, map=centroids, map_join="city", \ 10 tooltips=layer_tooltips().title("@city") 11 .line("population|@est_pop_2019") 12 .format("@est_pop_2019", ".2~s"))
The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).