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 bivariate, density plot uses nested contours (or contours plus colors) to indicate
  regions of higher local density.
  
  `bivariate` plots can be a useful alternative to scatter plots, if your data are too dense
  to plot each point individually.
  
  Reference: https://hvplot.holoviz.org/reference/pandas/bivariate.html
  
  Parameters
  ----------
  x : string, optional
      Field name to draw x-positions from. If not specified, the index is used.
  y : string, optional
      Field name to draw y-positions from
  colorbar: boolean
      Whether to display a colorbar
  bandwidth: int, optional
      The bandwidth of the kernel for the density estimate. Default is None.
  cut: Integer, Optional
      Draw the estimate to cut * bw from the extreme data points. Default is None.
  filled : bool, optional
      If True the the contours will be filled. Default is False.
  levels: int, optional
      The number of contour lines to draw. Default is 10.
  
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('bivariate')`.
  
  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
      from bokeh.sampledata.autompg import autompg_clean as df
  
      bivariate = df.hvplot.bivariate("accel", "mpg", filled=True, cmap="blues")
      bivariate
  
  To get a better intuitive understanding of the `bivariate` plot, you can try overlaying the
  corresponding scatter plot.
  
  .. code-block::
  
      scatter = df.hvplot.scatter("accel", "mpg")
      bivariate * scatter
  
  References
  ----------
  
  - ggplot: https://bio304-class.github.io/bio304-fall2017/ggplot-bivariate.html
  - HoloViews: https://holoviews.org/reference/elements/bokeh/Bivariate.html
  - Plotly: https://plotly.com/python/2d-histogram-contour/
  - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html
  - Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html
  - Wiki: https://en.wikipedia.org/wiki/Bivariate_analysis
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code: def bivariate(self, x=None, y=None, colorbar=True, **kwds):
      """
      A bivariate, density plot uses nested contours (or contours plus colors) to indicate
      regions of higher local density.
  
      `bivariate` plots can be a useful alternative to scatter plots, if your data are too dense
      to plot each point individually.
  
      Reference: https://hvplot.holoviz.org/reference/pandas/bivariate.html
  
      Parameters
      ----------
      x : string, optional
          Field name to draw x-positions from. If not specified, the index is used.
      y : string, optional
          Field name to draw y-positions from
      colorbar: boolean
          Whether to display a colorbar
      bandwidth: int, optional
          The bandwidth of the kernel for the density estimate. Default is None.
      cut: Integer, Optional
          Draw the estimate to cut * bw from the extreme data points. Default is None.
      filled : bool, optional
          If True the the contours will be filled. Default is False.
      levels: int, optional
          The number of contour lines to draw. Default is 10.
  
      **kwds : optional
          Additional keywords arguments are documented in `hvplot.help('bivariate')`.
  
      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
          from bokeh.sampledata.autompg import autompg_clean as df
  
          bivariate = df.hvplot.bivariate("accel", "mpg", filled=True, cmap="blues")
          bivariate
  
      To get a better intuitive understanding of the `bivariate` plot, you can try overlaying the
      corresponding scatter plot.
  
      .. code-block::
  
          scatter = df.hvplot.scatter("accel", "mpg")
          bivariate * scatter
  
      References
      ----------
  
      - ggplot: https://bio304-class.github.io/bio304-fall2017/ggplot-bivariate.html
      - HoloViews: https://holoviews.org/reference/elements/bokeh/Bivariate.html
      - Plotly: https://plotly.com/python/2d-histogram-contour/
      - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html
      - Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html
      - Wiki: https://en.wikipedia.org/wiki/Bivariate_analysis
      """
      return self(x, y, kind="bivariate", colorbar=colorbar, **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 9 for calling the hvPlotTabular obj's underlying member method code with example usage: obj.bivariate(x, y, colorbar) and returns: self