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: `errorbars` provide a visual indicator for the variability of the plotted data on a graph.
  They are usually overlaid with other plots such as `scatter` , `line` or `bar` plots to
  indicate the variability.
  
  Reference: https://hvplot.holoviz.org/reference/pandas/errorbars.html
  
  Parameters
  ----------
  x : string, optional
      Field name to draw the x-position from. If not specified, the index is
      used. Can refer to continuous and categorical data.
  y : string, optional
      Field name to draw the y-position from
  yerr1 : string, optional
      Field name to draw symmetric / negative errors from
  yerr2 : string, optional
      Field name to draw positive errors from
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('errorbars')`.
  
  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.
  
  Example
  -------
  
  .. code-block::
  
      import hvplot.pandas
      import pandas as pd
  
      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"],
          },
      )
      df["min"] = df[["actual", "forecast"]].min(axis=1)
      df["max"] = df[["actual", "forecast"]].max(axis=1)
      df["mean"] = df[["actual", "forecast"]].mean(axis=1)
      df["yerr2"] = df["max"] - df["mean"]
      df["yerr1"] = df["mean"] - df["min"]
  
      errorbars = df.hvplot.errorbars(
          x="numerical",
          y="mean",
          yerr1="yerr1",
          yerr2="yerr2",
          legend="bottom",
          height=500,
          alpha=0.5,
          line_width=2,
      )
      errorbars
  
  Normally you would overlay the `errorbars` on for example a `scatter` plot.
  
  .. code-block::
  
      mean = df.hvplot.scatter(x="numerical", y=["mean"], color=["#55a194"], size=50)
      errorbars * mean
  
  References
  ----------
  
  - Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/annotations.html#whiskers
  - HoloViews: https://holoviews.org/reference/elements/bokeh/ErrorBars.html
  - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html
  - Pandas: https://pandas.pydata.org/docs/user_guide/visualization.html#visualization-errorbars
  - Plotly: https://plotly.com/python/error-bars/
  - Wikipedia: https://en.wikipedia.org/wiki/Error_bar
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code: def errorbars(self, x=None, y=None, yerr1=None, yerr2=None, **kwds):
      """
      `errorbars` provide a visual indicator for the variability of the plotted data on a graph.
      They are usually overlaid with other plots such as `scatter` , `line` or `bar` plots to
      indicate the variability.
  
      Reference: https://hvplot.holoviz.org/reference/pandas/errorbars.html
  
      Parameters
      ----------
      x : string, optional
          Field name to draw the x-position from. If not specified, the index is
          used. Can refer to continuous and categorical data.
      y : string, optional
          Field name to draw the y-position from
      yerr1 : string, optional
          Field name to draw symmetric / negative errors from
      yerr2 : string, optional
          Field name to draw positive errors from
      **kwds : optional
          Additional keywords arguments are documented in `hvplot.help('errorbars')`.
  
      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.
  
      Example
      -------
  
      .. code-block::
  
          import hvplot.pandas
          import pandas as pd
  
          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"],
              },
          )
          df["min"] = df[["actual", "forecast"]].min(axis=1)
          df["max"] = df[["actual", "forecast"]].max(axis=1)
          df["mean"] = df[["actual", "forecast"]].mean(axis=1)
          df["yerr2"] = df["max"] - df["mean"]
          df["yerr1"] = df["mean"] - df["min"]
  
          errorbars = df.hvplot.errorbars(
              x="numerical",
              y="mean",
              yerr1="yerr1",
              yerr2="yerr2",
              legend="bottom",
              height=500,
              alpha=0.5,
              line_width=2,
          )
          errorbars
  
      Normally you would overlay the `errorbars` on for example a `scatter` plot.
  
      .. code-block::
  
          mean = df.hvplot.scatter(x="numerical", y=["mean"], color=["#55a194"], size=50)
          errorbars * mean
  
      References
      ----------
  
      - Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/annotations.html#whiskers
      - HoloViews: https://holoviews.org/reference/elements/bokeh/ErrorBars.html
      - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html
      - Pandas: https://pandas.pydata.org/docs/user_guide/visualization.html#visualization-errorbars
      - Plotly: https://plotly.com/python/error-bars/
      - Wikipedia: https://en.wikipedia.org/wiki/Error_bar
      """
      return self(
          x, y, kind="errorbars", yerr1=yerr1, yerr2=yerr2, **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 5 for calling the hvPlotTabular obj's underlying member method code with example usage: obj.errorbars(x, y, yerr1, yerr2) and returns: self