class_docstr: The plotting method: `df.hvplot(...)` creates a plot similarly to the familiar Pandas
  `df.plot` method.
  
  For more detailed options use a specific plotting method, e.g. `df.hvplot.line`.
  
  Reference: https://hvplot.holoviz.org/reference/index.html
  
  Parameters
  ----------
  x : string, optional
      Field name(s) to draw x-positions from. If not specified, the index is
      used.
  y : string or list, optional
      Field name(s) to draw y-positions from. If not specified, all numerical
      fields are used.
  kind : string, optional
      The kind of plot to generate, e.g. 'area', 'bar', 'line', 'scatter' etc. To see the
      available plots run `print(df.hvplot.__all__)`.
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('scatter')` or similar
      depending on the kind of plot.
  
  Returns
  -------
  A Holoviews object. You can `print` the object to study its composition and run
  
  .. code-block::
  
      import holoviews as hv
      hv.help(the_holoviews_object)
  
  to learn more about its parameters and options.
  
  Examples
  --------
  
  .. code-block::
  
      import pandas as pd
      import hvplot.pandas
  
      df = pd.DataFrame(
          {
              "actual": [100, 150, 125, 140, 145, 135, 123],
              "forecast": [90, 160, 125, 150, 141, 141, 120],
              "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
              "date": pd.date_range("2022-01-03", "2022-01-09"),
              "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
          },
      )
      line = df.hvplot.line(
          x="numerical",
          y=["actual", "forecast"],
          ylabel="value",
          legend="bottom",
          height=500,
          color=["steelblue", "teal"],
          alpha=0.7,
          line_width=5,
      )
      line
  
  You can can add *markers* to a `line` plot by overlaying with a `scatter` plot.
  
  .. code-block::
  
      markers = df.hvplot.scatter(
          x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], size=50
      )
      line * markers
  
  Please note that you can pass widgets or reactive functions as arguments instead of
  literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html.
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docstr: A `points` plot visualizes positions in a 2D space. This is useful for example for
  geographic plots.
  
  There is no assumption that 'y' depends on 'x'. This is different from a `scatter` plot
  which assumes that `y` depends `x`.
  
  Reference: https://hvplot.holoviz.org/reference/geopandas/points.html
  
  Parameters
  ----------
  x : string, optional
      The coordinate variable along the x-axis. Default is the first numeric field.
  y : string, optional
      The coordinate variable along the y-axis. Default is the second numeric field.
  c : string, optional
      The dimension to color the points by
  s : int, optional, also available as 'size'
      The size of the marker
  marker : string, optional
      The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc.
      See https://matplotlib.org/stable/api/markers_api.html.
  scale: number, optional
      Scaling factor to apply to point scaling.
  logz : bool
      Whether to apply log scaling to the z-axis. Default is False.
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('points')`.
  
  Returns
  -------
  A Holoviews object. You can `print` the object to study its composition and run
  
  .. code-block::
  
      import holoviews as hv
      hv.help(the_holoviews_object)
  
  to learn more about its parameters and options.
  
  Examples
  --------
  
  .. code-block::
  
      import hvplot.pandas
      import pandas as pd
  
      data = pd.DataFrame(dict(x=[49.9, 50.0, 50.1, 50.2], y=[50.2, 49.9, 50.0, 50.2]))
      plot = data.hvplot.points(color="green", size=100, marker="square")
      plot
  
  References
  ----------
  
  - HoloViews: https://holoviews.org/reference/elements/bokeh/Points.html
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code: def points(self, x=None, y=None, **kwds):
      """
      A `points` plot visualizes positions in a 2D space. This is useful for example for
      geographic plots.
  
      There is no assumption that 'y' depends on 'x'. This is different from a `scatter` plot
      which assumes that `y` depends `x`.
  
      Reference: https://hvplot.holoviz.org/reference/geopandas/points.html
  
      Parameters
      ----------
      x : string, optional
          The coordinate variable along the x-axis. Default is the first numeric field.
      y : string, optional
          The coordinate variable along the y-axis. Default is the second numeric field.
      c : string, optional
          The dimension to color the points by
      s : int, optional, also available as 'size'
          The size of the marker
      marker : string, optional
          The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc.
          See https://matplotlib.org/stable/api/markers_api.html.
      scale: number, optional
          Scaling factor to apply to point scaling.
      logz : bool
          Whether to apply log scaling to the z-axis. Default is False.
      **kwds : optional
          Additional keywords arguments are documented in `hvplot.help('points')`.
  
      Returns
      -------
      A Holoviews object. You can `print` the object to study its composition and run
  
      .. code-block::
  
          import holoviews as hv
          hv.help(the_holoviews_object)
  
      to learn more about its parameters and options.
  
      Examples
      --------
  
      .. code-block::
  
          import hvplot.pandas
          import pandas as pd
  
          data = pd.DataFrame(dict(x=[49.9, 50.0, 50.1, 50.2], y=[50.2, 49.9, 50.0, 50.2]))
          plot = data.hvplot.points(color="green", size=100, marker="square")
          plot
  
      References
      ----------
  
      - HoloViews: https://holoviews.org/reference/elements/bokeh/Points.html
      """
      return self(x, y, kind="points", **kwds)
  
imports: import itertools
  from collections import defaultdict
  import param
  from ..converter import HoloViewsConverter
  from ..util import is_list_like, process_dynamic_args
label_desc: Use image node_id 19 for calling the hvPlotTabular obj's underlying member method code with example usage: obj.points(x, y) and returns: self