class_docstr: Datasets contain a reference to data on disk and provide utilities for
  quickly loading files and objects into a variety of formats. The most
  common use of the Dataset object is to load example datasets provided by
  Yellowbrick to run the examples in the documentation.
  
  The dataset by default will return the data as a numpy array, however if
  Pandas is installed, it is possible to access the data as a DataFrame and
  Series object. In either case, the data is represented by a features table,
  X and a target vector, y.
  
  Parameters
  ----------
  name : str
      The name of the dataset; should either be a folder in data home or
      specified in the yellowbrick.datasets.DATASETS variable. This name is
      used to perform all lookups and identify the dataset externally.
  
  data_home : str, optional
      The path on disk where data is stored. If not passed in, it is looked
      up from YELLOWBRICK_DATA or the default returned by ``get_data_home``.
  
  url : str, optional
      The web location where the archive file of the dataset can be
      downloaded from.
  
  signature : str, optional
      The signature of the data archive file, used to verify that the latest
      version of the data has been downloaded and that the download hasn't
      been corrupted or modified in anyway.
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docstr: Returns the dataset as two pandas objects: X and y.
  
  Returns
  -------
  X : DataFrame with shape (n_instances, n_features)
      A pandas DataFrame containing feature data and named columns.
  
  y : Series with shape (n_instances,)
      A pandas Series containing target data and an index that matches
      the feature DataFrame index.
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code: def to_pandas(self):
      """
      Returns the dataset as two pandas objects: X and y.
  
      Returns
      -------
      X : DataFrame with shape (n_instances, n_features)
          A pandas DataFrame containing feature data and named columns.
  
      y : Series with shape (n_instances,)
          A pandas Series containing target data and an index that matches
          the feature DataFrame index.
      """
      # Ensure the metadata is valid before continuing
      if self.meta is None:
          raise DatasetsError(
              (
                  "the downloaded dataset was improperly packaged without meta.json "
                  "- please report this bug to the Yellowbrick maintainers!"
              )
          )
  
      if "features" not in self.meta or "target" not in self.meta:
          raise DatasetsError(
              (
                  "the downloaded dataset was improperly packaged without features "
                  "or target - please report this bug to the Yellowbrick maintainers!"
              )
          )
  
      # Load data frame and return features and target
      # TODO: Return y as None if there is no self.meta["target"]
      df = self.to_dataframe()
      return df[self.meta["features"]], df[self.meta["target"]]
  
imports: import os
  import json
  import numpy
  from .download import download_data
  from .path import find_dataset_path, dataset_exists
  from yellowbrick.exceptions import DatasetsError
  from yellowbrick.utils.decorators import memoized
label_desc: Use image node_id 3 for calling the Dataset obj's underlying member method code with example usage: obj.to_pandas() and returns: df,  df