code: def geom_density(
      mapping=None,
      *,
      data=None,
      stat=None,
      position=None,
      show_legend=None,
      sampling=None,
      tooltips=None,
      orientation=None,
      trim=None,
      kernel=None,
      adjust=None,
      bw=None,
      n=None,
      fs_max=None,
      quantiles=None,
      quantile_lines=None,
      color_by=None,
      fill_by=None,
      **other_args,
  ):
      """
      Display kernel density estimate, which is a smoothed version of the histogram.
  
      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='density'
          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).
      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.
      orientation : str, default='x'
          Specify the axis that the layer's stat and geom should run along.
          Possible values: 'x', 'y'.
      trim : bool, default=False
          If False, each density is computed on the full range of the data.
          If True, each density is computed over the range of that group.
      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 float
          The method (or exact value) of bandwidth.
          Either a string (choose among 'nrd0' and 'nrd'), or a float.
      adjust : float
          Adjust the value of bandwidth by multiplying it. Changes how smooth the frequency curve is.
      n : int, default=512
          The number of sampled points for plotting the function.
      fs_max : int, default=500
          Maximum size of data to use density computation with 'full scan'.
          For bigger data, less accurate but more efficient density computation is applied.
      quantiles : list of float, default=[0.25, 0.5, 0.75]
          Draw horizontal lines at the given quantiles of the density estimate.
      quantile_lines : bool, default=False
          Show the quantile lines.
      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
      -----
      Computed variables:
  
      - ..density.. : density estimate (mapped by default).
      - ..count.. : density * number of points.
      - ..scaled.. : density estimate, scaled to maximum of 1.
      - ..quantile.. : quantile estimate.
  
      `geom_density()` understands the following aesthetics mappings:
  
      - x : x-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").
      - 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").
      - size : lines width.
      - linetype : type of the line. Codes and names: 0 = 'blank', 1 = 'solid', 2 = 'dashed', 3 = 'dotted', 4 = 'dotdash', 5 = 'longdash', 6 = 'twodash'.
      - weight : used by 'density' stat to compute weighted density.
      - quantile : quantile values to draw quantile lines and fill quantiles of the geometry by color.
  
      Examples
      --------
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 6
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          x = np.random.normal(size=1000)
          ggplot({'x': x}, aes(x='x')) + geom_density()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 7-8
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 200
          np.random.seed(42)
          ggplot({'x': np.random.normal(size=n)}) + \\
              geom_density(aes(x='x', fill='..quantile..'), color='black', size=1, \\
                           quantiles=[0, .02, .1, .5, .9, .98, 1], quantile_lines=True)
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9-15
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 300
          np.random.seed(42)
          x = np.random.normal(size=n)
          c = np.random.choice(['a', 'b', 'c'], size=n)
          ggplot({'x': x, 'c': c}, aes(x='x')) + \\
              geom_density(aes(group='c', color='c', fill='c'), alpha=.2, \\
                           tooltips=layer_tooltips().format('..density..', '.3f')\\
                                                    .line('density|@..density..')\\
                                                    .format('..count..', '.1f')\\
                                                    .line('count|@..count..')\\
                                                    .format('..scaled..', '.2f')\\
                                                    .line('scaled|@..scaled..'))
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 10
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          x = np.random.normal(size=1000)
          p = ggplot({'x': x}, aes(x='x'))
          bunch = GGBunch()
          for i, bw in enumerate([.1, .2, .4]):
              for j, n in enumerate([16, 64, 256]):
                  bunch.add_plot(p + geom_density(kernel='epanechikov', bw=bw, n=n) + \\
                                     ggtitle('bw={0}, n={1}'.format(bw, n)),
                                 j * 300, i * 200, 300, 200)
          bunch.show()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 10-11
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          x = np.random.normal(size=1000)
          y = np.sign(x)
          p = ggplot({'x': x, 'y': y}, aes(x='x'))
          bunch = GGBunch()
          for i, adjust in [(i, .5 * (1 + i)) for i in range(3)]:
              bunch.add_plot(p + geom_density(aes(weight='y'), kernel='cosine', \\
                                              adjust=adjust) + \\
                                 ggtitle('adjust={0}'.format(adjust)),
                             i * 300, 0, 300, 200)
          bunch.show()
  
      """
      return _geom(
          "density",
          mapping=mapping,
          data=data,
          stat=stat,
          position=position,
          show_legend=show_legend,
          sampling=sampling,
          tooltips=tooltips,
          orientation=orientation,
          trim=trim,
          kernel=kernel,
          adjust=adjust,
          bw=bw,
          n=n,
          fs_max=fs_max,
          quantiles=quantiles,
          quantile_lines=quantile_lines,
          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 28 for calling a global function with example usage: geom_density(mapping) and returns: _geom