code: def 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,
      color_by=None,
      fill_by=None,
      **other_args,
  ):
      """
      Draw points defined by an x and y coordinate, 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".
      data : dict 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.
      stat : str, 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).
      position : str or `FeatureSpec`, default='identity'
          Position adjustment, either as a string ('identity', 'stack', 'dodge', ...),
          or the result of a call to a position adjustment function.
      show_legend : bool, default=True
          False - do not show legend for this layer.
      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.
      map : `GeoDataFrame` or `Geocoder`
          Data containing coordinates of points.
      map_join : str 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_crs : str, 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.
      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
      --------
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 6
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
          y = np.sin(x)
          ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + geom_point()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9-10
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          n = 100
          x = np.random.uniform(-1, 1, size=n)
          y = 25 * x ** 2 + np.random.normal(size=n)
          ggplot({'x': x, 'y': y}) + \\
              geom_point(aes(x='x', y='y', fill='y'), \\
                         shape=21, size=5, color='white')
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 8-11
  
          from lets_plot import *
          from lets_plot.geo_data import *
          LetsPlot.setup_html()
          data = {"city": ["New York", "Los Angeles", "Chicago"], \\
                  "est_pop_2019": [8_336_817, 3_979_576, 2_693_976]}
          centroids = geocode_cities(data["city"]).get_centroids()
          ggplot() + geom_livemap() + \\
              geom_point(aes(size="est_pop_2019"), color="red", show_legend=False, \\
                         data=data, map=centroids, map_join="city", \\
                         tooltips=layer_tooltips().title("@city")
                                                  .line("population|@est_pop_2019"))
  
      """
      return _geom(
          "point",
          mapping=mapping,
          data=data,
          stat=stat,
          position=position,
          show_legend=show_legend,
          sampling=sampling,
          tooltips=tooltips,
          map=map,
          map_join=map_join,
          use_crs=use_crs,
          color_by=color_by,
          fill_by=fill_by,
          **other_args,
      )
  
imports: from lets_plot.geo_data_internals.utils import is_geocoder
  from .core import FeatureSpec, LayerSpec
  from .util import as_annotated_data, is_geo_data_frame, geo_data_frame_to_crs, get_geo_data_frame_meta
label_desc: Use image node_id 1 for calling a global function with example usage: geom_point(mapping) and returns: _geom