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: Labels plot.
  
  `labels` are mostly useful when overlaid on top of other plots using the `*`
  operator.
  
  Reference: https://hvplot.holoviz.org/reference/pandas/labels.html
  
  Parameters
  ----------
  x : string, optional
      The coordinate variable along the x-axis
  y : string, optional
      The coordinate variable along the y-axis
  text : string, optional
      The column to draw the text labels from; it's also possible to
      provide a template string containing the column names to
      automatically format the text, e.g. "{col1}, {col2}".
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('labels')`.
  
  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
  
      df = pd.DataFrame(
          {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
          'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
          'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
          'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86],
          'Color': ['blue', 'green', 'white', 'black', 'yellow']})
  
      df.hvplot.points(x='Longitude', y='Latitude') *             df.hvplot.labels(x='Longitude', y='Latitude', text='City', text_baseline="bottom")
  
  References
  ----------
  
  - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/text.html
  - HoloViews: https://holoviews.org/reference/elements/bokeh/Labels.html
  - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text
  - Plotly: https://plotly.com/python/text-and-annotations/
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code: def labels(self, x=None, y=None, text=None, **kwds):
      """
          Labels plot.
  
          `labels` are mostly useful when overlaid on top of other plots using the `*`
          operator.
  
          Reference: https://hvplot.holoviz.org/reference/pandas/labels.html
  
          Parameters
          ----------
          x : string, optional
              The coordinate variable along the x-axis
          y : string, optional
              The coordinate variable along the y-axis
          text : string, optional
              The column to draw the text labels from; it's also possible to
              provide a template string containing the column names to
              automatically format the text, e.g. "{col1}, {col2}".
          **kwds : optional
              Additional keywords arguments are documented in `hvplot.help('labels')`.
  
          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
  
              df = pd.DataFrame(
                  {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
                  'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
                  'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
                  'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86],
                  'Color': ['blue', 'green', 'white', 'black', 'yellow']})
  
              df.hvplot.points(x='Longitude', y='Latitude') * \
              df.hvplot.labels(x='Longitude', y='Latitude', text='City', text_baseline="bottom")
  
          References
          ----------
  
          - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/text.html
          - HoloViews: https://holoviews.org/reference/elements/bokeh/Labels.html
          - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text
          - Plotly: https://plotly.com/python/text-and-annotations/
          """
      return self(x, y, text=text, kind="labels", **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 23 for calling the hvPlotTabular obj's underlying member method code with example usage: obj.labels(x, y, text) and returns: self