code: def geom_density2d(
      mapping=None,
      *,
      data=None,
      stat=None,
      position=None,
      show_legend=None,
      sampling=None,
      tooltips=None,
      kernel=None,
      adjust=None,
      bw=None,
      n=None,
      bins=None,
      binwidth=None,
      color_by=None,
      **other_args,
  ):
      """
      Display density function contour.
  
      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`
          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='density2d'
          The statistical transformation to use on the data for this layer, as a string.
      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.
      kernel : str, default='gaussian'
          The kernel we use to calculate the density function.
          Choose among 'gaussian', 'cosine', 'optcosine', 'rectangular' (or 'uniform'),
          'triangular', 'biweight' (or 'quartic'), 'epanechikov' (or 'parabolic').
      bw : str or list of float
          The method (or exact value) of bandwidth.
          Either a string (choose among 'nrd0' and 'nrd'), or a float array of length 2.
      adjust : float
          Adjust the value of bandwidth by multiplying it. Change how smooth the frequency curve is.
      n : list of int
          The number of sampled points for plotting the function
          (on x and y direction correspondingly).
      bins : int
          Number of levels.
      binwidth : float
          Distance between levels.
      color_by : {'fill', 'color', 'paint_a', 'paint_b', 'paint_c'}, default='color'
          Define the color 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
      -----
      `geom_density2d()` draws density function.
  
      Computed variables:
  
      - ..group.. : number of density estimate contour line.
  
      `geom_density2d()` understands the following aesthetics mappings:
  
      - x : x-axis coordinates.
      - y : y-axis coordinates.
      - alpha : transparency level of a layer. Accept values between 0 and 1.
      - color (colour) : color of the geometry lines. 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").
      - size : lines width.
      - linetype : type of the line of border. Codes and names: 0 = 'blank', 1 = 'solid', 2 = 'dashed', 3 = 'dotted', 4 = 'dotdash', 5 = 'longdash', 6 = 'twodash'.
  
      |
  
      'density2d' statistical transformation combined with parameter value `contour=False`
      could be used to draw heatmaps (see the example below).
  
      Examples
      --------
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 1000
          np.random.seed(42)
          x = np.random.normal(size=n)
          y = np.random.normal(size=n)
          ggplot({'x': x, 'y': y}, aes('x', 'y')) + \\
              geom_density2d()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 1000
          np.random.seed(42)
          x = np.random.normal(size=n)
          y = np.random.normal(size=n)
          ggplot({'x': x, 'y': y}, aes('x', 'y')) + \\
              geom_density2d(aes(color='..group..'), size=1, show_legend=False) + \\
              scale_color_brewer(type='seq', palette='GnBu', direction=-1)
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 12
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 1000
          np.random.seed(42)
          x = np.random.normal(size=n)
          y = np.random.normal(size=n)
          p = ggplot({'x': x, 'y': y}, aes('x', 'y'))
          bunch = GGBunch()
          for i, bw in enumerate([.2, .4]):
              for j, n in enumerate([16, 256]):
                  bunch.add_plot(p + geom_density2d(kernel='epanechikov', bw=bw, n=n) + \\
                                     ggtitle('bw={0}, n={1}'.format(bw, n)),
                                 j * 400, i * 400, 400, 400)
          bunch.show()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 12-13
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 1000
          np.random.seed(42)
          x = np.random.normal(size=n)
          y = np.random.normal(size=n)
          p = ggplot({'x': x, 'y': y}, aes('x', 'y'))
          bunch = GGBunch()
          for i, adjust in enumerate([1.5, 2.5]):
              for j, bins in enumerate([5, 15]):
                  bunch.add_plot(p + geom_density2d(kernel='cosine', \\
                                                    adjust=adjust, bins=bins) + \\
                                     ggtitle('adjust={0}, bins={1}'.format(adjust, bins)),
                                 j * 400, i * 400, 400, 400)
          bunch.show()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9-10
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 1000
          np.random.seed(42)
          x = np.random.normal(size=n)
          y = np.random.normal(size=n)
          ggplot({'x': x, 'y': y}, aes('x', 'y')) + \\
              geom_raster(aes(fill='..density..'), \\
                         stat='density2d', contour=False, n=50) + \\
              scale_fill_gradient(low='#49006a', high='#fff7f3')
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 1000
          np.random.seed(42)
          data = {'x': 10 * np.random.normal(size=n) - 100, \\
                  'y': 3 * np.random.normal(size=n) + 40}
          ggplot(data, aes('x', 'y')) + geom_livemap() + \\
              geom_density2d()
  
      """
      return _geom(
          "density2d",
          mapping=mapping,
          data=data,
          stat=stat,
          position=position,
          show_legend=show_legend,
          sampling=sampling,
          tooltips=tooltips,
          kernel=kernel,
          adjust=adjust,
          bw=bw,
          n=n,
          bins=bins,
          binwidth=binwidth,
          color_by=color_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 29 for calling a global function with example usage: geom_density2d(mapping) and returns: _geom