class_docstr: Series/Frames plotting accessor and method.
  
  Uses the backend specified by the
  option ``plotting.backend``. By default, plotly is used.
  
  Plotting methods can also be accessed by calling the accessor as a method
  with the ``kind`` argument:
  ``s.plot(kind='hist')`` is equivalent to ``s.plot.hist()``
docstr: Create a scatter plot with varying marker point size and color.
  
  The coordinates of each point are defined by two dataframe columns and
  filled circles are used to represent each point. This kind of plot is
  useful to see complex correlations between two variables. Points could
  be for instance natural 2D coordinates like longitude and latitude in
  a map or, in general, any pair of metrics that can be plotted against
  each other.
  
  Parameters
  ----------
  x : int or str
      The column name or column position to be used as horizontal
      coordinates for each point.
  y : int or str
      The column name or column position to be used as vertical
      coordinates for each point.
  s : scalar or array_like, optional
      (matplotlib-only).
  c : str, int or array_like, optional
      (matplotlib-only).
  
  **kwds: Optional
      Keyword arguments to pass on to :meth:`pyspark.pandas.DataFrame.plot`.
  
  Returns
  -------
  :class:`plotly.graph_objs.Figure`
      Return an custom object when ``backend!=plotly``.
      Return an ndarray when ``subplots=True`` (matplotlib-only).
  
  See Also
  --------
  plotly.express.scatter : Scatter plot using multiple input data
      formats (plotly).
  matplotlib.pyplot.scatter : Scatter plot using multiple input data
      formats (matplotlib).
  
  Examples
  --------
  Let's see how to draw a scatter plot using coordinates from the values
  in a DataFrame's columns.
  
  .. plotly::
  
      >>> df = ps.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
      ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
      ...                   columns=['length', 'width', 'species'])
      >>> df.plot.scatter(x='length', y='width')  # doctest: +SKIP
  
  And now with dark scheme:
  
  .. plotly::
  
      >>> df = ps.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
      ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
      ...                   columns=['length', 'width', 'species'])
      >>> fig = df.plot.scatter(x='length', y='width')
      >>> fig.update_layout(template="plotly_dark")  # doctest: +SKIP
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code: def scatter(self, x, y, **kwds):
      """
      Create a scatter plot with varying marker point size and color.
  
      The coordinates of each point are defined by two dataframe columns and
      filled circles are used to represent each point. This kind of plot is
      useful to see complex correlations between two variables. Points could
      be for instance natural 2D coordinates like longitude and latitude in
      a map or, in general, any pair of metrics that can be plotted against
      each other.
  
      Parameters
      ----------
      x : int or str
          The column name or column position to be used as horizontal
          coordinates for each point.
      y : int or str
          The column name or column position to be used as vertical
          coordinates for each point.
      s : scalar or array_like, optional
          (matplotlib-only).
      c : str, int or array_like, optional
          (matplotlib-only).
  
      **kwds: Optional
          Keyword arguments to pass on to :meth:`pyspark.pandas.DataFrame.plot`.
  
      Returns
      -------
      :class:`plotly.graph_objs.Figure`
          Return an custom object when ``backend!=plotly``.
          Return an ndarray when ``subplots=True`` (matplotlib-only).
  
      See Also
      --------
      plotly.express.scatter : Scatter plot using multiple input data
          formats (plotly).
      matplotlib.pyplot.scatter : Scatter plot using multiple input data
          formats (matplotlib).
  
      Examples
      --------
      Let's see how to draw a scatter plot using coordinates from the values
      in a DataFrame's columns.
  
      .. plotly::
  
          >>> df = ps.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
          ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
          ...                   columns=['length', 'width', 'species'])
          >>> df.plot.scatter(x='length', y='width')  # doctest: +SKIP
  
      And now with dark scheme:
  
      .. plotly::
  
          >>> df = ps.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
          ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
          ...                   columns=['length', 'width', 'species'])
          >>> fig = df.plot.scatter(x='length', y='width')
          >>> fig.update_layout(template="plotly_dark")  # doctest: +SKIP
      """
      return self(kind="scatter", x=x, y=y, **kwds)
  
imports: import importlib
  import pandas
  import numpy
  from pandas.core.base import PandasObject
  from pandas.core.dtypes.inference import is_integer
  from pyspark.ml.feature import Bucketizer
  from pyspark.mllib.stat import KernelDensity
  from pyspark.sql import functions
  from pyspark.pandas.missing import unsupported_function
  from pyspark.pandas.config import get_option
  from pyspark.pandas.utils import name_like_string
label_desc: Use image node_id 13 for calling the PandasOnSparkPlotAccessor obj's underlying member method code with example usage: obj.scatter(x, y) and returns: self