code: def geom_path(
      mapping=None,
      *,
      data=None,
      stat=None,
      position=None,
      show_legend=None,
      sampling=None,
      tooltips=None,
      map=None,
      map_join=None,
      use_crs=None,
      flat=None,
      geodesic=None,
      color_by=None,
      **other_args,
  ):
      """
      Connect observations in the order, how they appear in the data.
  
      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).
      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`
          Data containing coordinates of lines.
      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.
      flat : bool, default=False.
          True - keep a line straight (corresponding to a loxodrome in case of Mercator projection).
          False - allow a line to be reprojected, so it can become a curve.
      geodesic : bool, default=False
          Draw geodesic. Coordinates expected to be in WGS84. Works only with `geom_livemap()`.
      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_path()` connects the observations in the order in which they appear in the data.
      `geom_path()` lets you explore how two variables are related over time.
  
      `geom_path()` understands the following aesthetics mappings:
  
      - x : x-axis value.
      - y : y-axis value.
      - alpha : transparency level of a layer. 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").
      - linetype : type of the line. Codes and names: 0 = 'blank', 1 = 'solid', 2 = 'dashed', 3 = 'dotted', 4 = 'dotdash', 5 = 'longdash', 6 = 'twodash'.
      - size : line width.
  
      |
  
      The `data` and `map` parameters of `GeoDataFrame` type support shapes `LineString` and `MultiLineString`.
  
      |
  
      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: 7
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 100
          t = np.linspace(0, 2 * np.pi, n)
          data = {'x': t * np.sin(t), 'y': t * np.cos(t)}
          ggplot(data, aes(x='x', y='y')) + geom_path()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 11
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          T = 50
          np.random.seed(42)
          x = np.cumsum(np.random.normal(size=2*T))
          y = np.cumsum(np.random.normal(size=2*T))
          c = [0] * T + [1] * T
          data = {'x': x, 'y': y, 'c': c}
          ggplot(data, aes(x='x', y='y', group='c')) + \\
              geom_path(aes(color='c'), size=2, alpha=.5) + \\
              scale_color_discrete()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 15
  
          from lets_plot import *
          from lets_plot.geo_data import *
          LetsPlot.setup_html()
          pushkin_1829_journey = {
              "city": ["Moscow", "Oryol", "Novocherkassk", "Stavropol", \\
                       "Georgiyevsk", "Vladikavkaz", "Tiflis", "Kars", "Erzurum"],
              "latitude": [55.751244, 52.929697, 47.414101, 45.0428, \\
                           44.1497667, 43.03667, 41.716667, 40.60199, 39.90861],
              "longitude": [37.618423, 36.098689, 40.110401, 41.9734, \\
                            43.4577689, 44.66778, 44.783333, 43.09495, 41.27694],
          }
          ggplot(pushkin_1829_journey, aes("longitude", "latitude")) + \\
              geom_livemap(const_size_zoomin=0) + \\
              geom_point(size=3, color="#fc4e2a", tooltips=layer_tooltips().line("@city")) + \\
              geom_path(color="#fc4e2a")
  
      """
  
      return _geom(
          "path",
          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,
          flat=flat,
          geodesic=geodesic,
          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 2 for calling a global function with example usage: geom_path(mapping) and returns: _geom