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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 execute(self, input_data):
 """This worker puts the output of pe_features into a dictionary of dataframes"""

        if 'sample' in input_data:
            print 'Warning: PEFeaturesDF is supposed to be called on a sample_set'
            self.samples.append(input_data['sample']['md5'])
        else:
            self.samples = input_data['sample_set']['md5_list']

        # Make a sample set
        sample_set = self.workbench.store_sample_set(self.samples)

        # Dense Features               
        dense_features = self.workbench.set_work_request('pe_features', sample_set, ['md5', 'tags', 'dense_features'])

        # Fixme: There's probably a nicer/better way to do this
        flat_features = []
        for feat in dense_features:
            feat['dense_features'].update({'md5': feat['md5'], 'tags': feat['tags']})
            flat_features.append(feat['dense_features'])
        dense_df = pd.DataFrame(flat_features)
        df_packed = dense_df.to_msgpack()
        dense_df_md5 = self.workbench.store_sample(df_packed, 'pe_features_dense_df', 'dataframe')
        
        # Sparse Features
        sparse_features = self.workbench.set_work_request('pe_features', sample_set, ['md5', 'tags', 'sparse_features'])

        # Fixme: There's probably a nicer/better way to do this
        flat_features = []
        for feat in sparse_features:
            feat['sparse_features'].update({'md5': feat['md5'], 'tags': feat['tags']})
            flat_features.append(feat['sparse_features'])
        sparse_df = pd.DataFrame(flat_features)
        df_packed = sparse_df.to_msgpack()
        sparse_df_md5 = self.workbench.store_sample(df_packed, 'pe_features_sparse_df', 'dataframe')

        # Return the dataframes
        return {'dense_features': dense_df_md5, 'sparse_features': sparse_df_md5}