class_docstr: The condensed tree structure, which provides a simplified or smoothed version
  of the :class:`~hdbscan.plots.SingleLinkageTree`.
  
  Parameters
  ----------
  condensed_tree_array : numpy recarray from :class:`~hdbscan.HDBSCAN`
      The raw numpy rec array version of the condensed tree as produced
      internally by hdbscan.
  
  cluster_selection_method : string, optional (default 'eom')
      The method of selecting clusters. One of 'eom' or 'leaf'
  
  allow_single_cluster : Boolean, optional (default False)
      Whether to allow the root cluster as the only selected cluster
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docstr: Return a pandas dataframe representation of the condensed tree.
  
  Each row of the dataframe corresponds to an edge in the tree.
  The columns of the dataframe are `parent`, `child`, `lambda_val`
  and `child_size`.
  
  The `parent` and `child` are the ids of the
  parent and child nodes in the tree. Node ids less than the number
  of points in the original dataset represent individual points, while
  ids greater than the number of points are clusters.
  
  The `lambda_val` value is the value (1/distance) at which the `child`
  node leaves the cluster.
  
  The `child_size` is the number of points in the `child` node.
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code: def to_pandas(self):
      """Return a pandas dataframe representation of the condensed tree.
  
      Each row of the dataframe corresponds to an edge in the tree.
      The columns of the dataframe are `parent`, `child`, `lambda_val`
      and `child_size`.
  
      The `parent` and `child` are the ids of the
      parent and child nodes in the tree. Node ids less than the number
      of points in the original dataset represent individual points, while
      ids greater than the number of points are clusters.
  
      The `lambda_val` value is the value (1/distance) at which the `child`
      node leaves the cluster.
  
      The `child_size` is the number of points in the `child` node.
      """
      try:
          from pandas import DataFrame, Series
      except ImportError:
          raise ImportError(
              "You must have pandas installed to export pandas DataFrames"
          )
  
      result = DataFrame(self._raw_tree)
  
      return result
  
imports: import numpy
  from scipy.cluster.hierarchy import dendrogram
  from sklearn.manifold import TSNE
  from sklearn.decomposition import PCA
  from sklearn.metrics import pairwise_distances
  from warnings import warn
  from ._hdbscan_tree import compute_stability, labelling_at_cut, recurse_leaf_dfs
label_desc: Use image node_id 6 for calling the CondensedTree obj's underlying member method code with example usage: obj.to_pandas() and returns: result