How to use the cartoframes.data.Dataset function in cartoframes

def test_dataset_download_validations(self):
        self.assertNotExistsTable(self.test_write_table)

        df = load_geojson(self.test_geojson)
        dataset = Dataset(df)
        error_msg = 'You should provide a context and a table_name or query to download data.'
        with self.assertRaises(ValueError, msg=error_msg):
            dataset.download()

        query = 'SELECT 1 as fakec'
        dataset = Dataset(query, credentials=self.credentials)
        dataset.upload(table_name=self.test_write_table)

        dataset._table_name = 'non_used_table'
        df = dataset.download()
        self.assertEqual('fakec' in df.columns, True)

        dataset = Dataset(self.test_write_table, credentials=self.credentials)
        df = dataset.download()
        self.assertEqual('fakec' in df.columns, True)

def test_dataset_write_polygons_dataset(self):
        self.assertNotExistsTable(self.test_write_table)

        from cartoframes.examples import read_brooklyn_poverty
        df = read_brooklyn_poverty()
        dataset = Dataset(df).upload(table_name=self.test_write_table, credentials=self.credentials)
        self.test_write_table = dataset.table_name

        query = 'SELECT cartodb_id FROM {} WHERE the_geom IS NOT NULL'.format(self.test_write_table)
        result = self.sql_client.query(query, verbose=True)
        self.assertEqual(result['total_rows'], 2049)

def test_isochrones_from_table_dataset_as_new_table(self):
        self.skip(if_no_credits=True, if_no_credentials=True)
        iso = Isolines(credentials=self.credentials)

        df = pd.DataFrame(self.points, columns=['name', 'the_geom'])
        table_name = self.get_test_table_name('isotb')
        Dataset(df).upload(table_name=table_name, credentials=self.credentials)
        ds = Dataset(table_name, credentials=self.credentials)

        result_table_name = self.get_test_table_name('isotbr')

        quota = self.used_quota(iso)

        # Preview
        result = iso.isochrones(ds, [100, 1000], mode='car', table_name=result_table_name, dry_run=True).metadata
        self.assertEqual(result.get('required_quota'), 6)
        self.assertEqual(self.used_quota(iso), quota)

        # Isochrones
        result = iso.isochrones(ds, [100, 1000], mode='car', table_name=result_table_name).data
        self.assertTrue(isinstance(result, Dataset))
        self.assertTrue(result.is_remote())
        quota += 6

def test_dataset_download_and_upload(self):
        self.assertNotExistsTable(self.test_write_table)

        query = 'SELECT 1 as fakec'
        dataset = Dataset(query, credentials=self.credentials)
        dataset.upload(table_name=self.test_write_table)

        dataset = Dataset(self.test_write_table, credentials=self.credentials)
        df = dataset.download()

        dataset = Dataset(df)
        dataset.upload(table_name=self.test_write_table,
                       credentials=self.credentials,
                       if_exists=Dataset.IF_EXISTS_REPLACE)

def test_isochrones_from_query_dataset(self):
        self.skip(if_no_credits=True, if_no_credentials=True)
        iso = Isolines(credentials=self.credentials)

        ds = Dataset(self.points_query(), credentials=self.credentials)

        quota = self.used_quota(iso)

        # Preview
        result = iso.isochrones(ds, [100, 1000], mode='car', dry_run=True).metadata
        self.assertEqual(result.get('required_quota'), 6)
        self.assertEqual(self.used_quota(iso), quota)

        # Isochrones
        result = iso.isochrones(ds, [100, 1000], mode='car').data
        self.assertTrue(isinstance(result, Dataset))
        self.assertTrue(result.is_local())
        quota += 6
        self.assertEqual(self.used_quota(iso), quota)
        result_columns = result.get_column_names()
        self.assertTrue('the_geom' in result_columns)

def test_dataset_from_dataframe(self):
        df = load_geojson(self.test_geojson)
        dataset = Dataset(df)

        self.assertIsInstance(dataset, Dataset)
        self.assertIsNotNone(dataset.dataframe)
        self.assertIsNone(dataset.table_name)
        self.assertIsNone(dataset.credentials)

def tearDown(self):
        """restore to original state"""
        tables = (self.test_write_table, )
        sql_drop = 'DROP TABLE IF EXISTS {};'

        for table in tables:
            try:
                Dataset(table, credentials=self.credentials).delete()
                self.sql_client.query(sql_drop.format(table))
            except CartoException:
                warnings.warn('Error deleting tables')

        StrategiesRegistry.instance = None

def test_isochrones_from_table_dataset(self):
        self.skip(if_no_credits=True, if_no_credentials=True)
        iso = Isolines(credentials=self.credentials)

        df = pd.DataFrame(self.points, columns=['name', 'the_geom'])
        table_name = self.get_test_table_name('isotb')
        Dataset(df).upload(table_name=table_name, credentials=self.credentials)
        ds = Dataset(table_name, credentials=self.credentials)

        quota = self.used_quota(iso)

        # Preview
        result = iso.isochrones(ds, [100, 1000], mode='car', dry_run=True).metadata
        self.assertEqual(result.get('required_quota'), 6)
        self.assertEqual(self.used_quota(iso), quota)

        # Isochrones
        result = iso.isochrones(ds, [100, 1000], mode='car').data
        self.assertTrue(isinstance(result, Dataset))
        self.assertTrue(result.is_local())
        quota += 6
        self.assertEqual(self.used_quota(iso), quota)
        result_columns = result.get_column_names()

def tearDown(self):
        """restore to original state"""
        sql_drop = 'DROP TABLE IF EXISTS {};'

        for table in self.test_tables:
            try:
                Dataset(table, credentials=self.credentials).delete()
                self.sql_client.query(sql_drop.format(table))
            except CartoException:
                warnings.warn('Error deleting tables')

def read_nat(self, limit=None, **kwargs):
        """Historical homicide rates for the United States at the county level.
        See the function :py:func:`read_nat
        ` for more information

        Example:

        .. code::

            from cartoframes.examples import examples
            df = examples.read_nat()

        """
        return Dataset('nat', self._credentials).download(limit, **kwargs)