class_docstr: Container for scatter plots.
  
  This class (object) is returned from the ``scatter_plots`` function. The
  attributes documented below can be accessed or extracted.
  
  Attributes:
      input_data: The input data.
      num_data: Numeric data only
      mode: {'diagnostic', 'matrix', 'all'} The visualization mode.
          **diagnostic**: Plots selected by scagnostics (scatter plot diagnostics)
          **matrix**: Generate the full scatter plot matrix
          **all**: Generate all individual scatter plots
      sample: The sampling method to use. Currently not used.
      diagnostics: The diagnostics from ``pyscagnostics.scagnostics``
      threshold: The scatter plot diagnostic threshold value [0,1] for returning a
          plot. Only used with "diagnostic" mode. For example, ``{"Outlying": 0.9}``
          returns plots with outlier metrics above 0.9. See
          ``pyscagnostics.measure_names`` for a list of metrics.
          **If a number**: Returns all plots where at least one metric is above this threshold
          **If a dictionary**: Returns plots where the metric is above its threshold.
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docstr: Data heatmap.
  
  Args:
      input_data: The input data.
      num_data: Numeric data only
      mode: {'diagnostic', 'matrix', 'all'} The visualization mode.
          **diagnostic**: Plots selected by scagnostics (scatter plot diagnostics)
          **matrix**: Generate the full scatter plot matrix
          **all**: Generate all individual scatter plots
      sample: The sampling method to use. Currently not used.
      diagnostics: The diagnostics from ``pyscagnostics.scagnostics``
      threshold: The scatter plot diagnostic threshold value [0,1] for returning a
          plot. Only used with "diagnostic" mode. For example, ``{"Outlying": 0.9}``
          returns plots with outlier metrics above 0.9. See
          ``pyscagnostics.measure_names`` for a list of metrics.
          **If a number**: Returns all plots where at least one metric is above this threshold
          **If a dictionary**: Returns plots where the metric is above its threshold.
      compute_backend: The compute backend.
      viz_backend: The visualization backend.
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code: def __init__(
      self,
      input_data=None,
      num_data=None,
      mode=None,
      sample=None,
      diagnostics=None,
      threshold=None,
      compute_backend=None,
      viz_backend=None,
      **kwargs,
  ):
      """Data heatmap.
  
      Args:
          input_data: The input data.
          num_data: Numeric data only
          mode: {'diagnostic', 'matrix', 'all'} The visualization mode.
              **diagnostic**: Plots selected by scagnostics (scatter plot diagnostics)
              **matrix**: Generate the full scatter plot matrix
              **all**: Generate all individual scatter plots
          sample: The sampling method to use. Currently not used.
          diagnostics: The diagnostics from ``pyscagnostics.scagnostics``
          threshold: The scatter plot diagnostic threshold value [0,1] for returning a
              plot. Only used with "diagnostic" mode. For example, ``{"Outlying": 0.9}``
              returns plots with outlier metrics above 0.9. See
              ``pyscagnostics.measure_names`` for a list of metrics.
              **If a number**: Returns all plots where at least one metric is above this threshold
              **If a dictionary**: Returns plots where the metric is above its threshold.
          compute_backend: The compute backend.
          viz_backend: The visualization backend.
      """
      super(ScatterWidget, self).__init__(**kwargs)
      self.input_data = input_data
      self.num_data = num_data
      self.mode = mode
      self.sample = sample
      self.diagnostics = diagnostics
      self.threshold = threshold
      self.compute_backend = compute_backend
      self.viz_backend = viz_backend
      self.kwargs = kwargs
  
file_path: data-describe/data_describe/core/scatter.py
imports: from itertools import combinations
  import warnings
  import seaborn
  from data_describe._widget import BaseWidget
  from data_describe.config._config import get_option
  from data_describe.compat import _is_dataframe, _requires, _compat
  from data_describe.backends import _get_compute_backend, _get_viz_backend
label_desc: Use image node_id 1 to create a new ScatterWidget object from inherited base classes: BaseWidget with example: obj = ScatterWidget(input_data, num_data, mode, sample, diagnostics, threshold, compute_backend, viz_backend)