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 entire dataset as a single pandas DataFrame.
  
  Returns
  -------
  df : DataFrame with shape (n_instances, n_columns)
      A pandas DataFrame containing the complete original data table
      including all targets (specified by the meta data) and all
      features (including those that might have been filtered out).
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code: def to_dataframe(self):
      """
      Returns the entire dataset as a single pandas DataFrame.
  
      Returns
      -------
      df : DataFrame with shape (n_instances, n_columns)
          A pandas DataFrame containing the complete original data table
          including all targets (specified by the meta data) and all
          features (including those that might have been filtered out).
      """
      if pd is None:
          raise DatasetsError(
              "pandas is required to load DataFrame, it can be installed with pip"
          )
  
      path = find_dataset_path(
          self.name, ext=".csv.gz", data_home=self.data_home
      )
      return pd.read_csv(path, compression="gzip")
  
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 4 for calling the Dataset obj's underlying member method code with example usage: obj.to_dataframe() and returns: pd