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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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")