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Lets-Plot for Python

v4.3.2

lets_plot.geom_point#

lets_plot.geom_point(mapping=None, *, data=None, stat=None, position=None, show_legend=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 an x and y coordinate, as for a scatter plot.

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=’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 same x-axis coordinate), ‘bin’ (counts number of points with 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 workaround overplotting).

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.

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.

size_unit{‘x’, ‘y’}

Relate the size of the point 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 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.

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. 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”).

  • fill : fill color. Is applied only to the points of shapes having inner area. 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”).

  • shape : shape of the point, an integer from 0 to 25.

  • size : size of the point.

  • stroke : width of the shape border. Applied only to the shapes having border.


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

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"))

The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).
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