Remove method

sdv/metadata/multi_table.py

@@ -560,25 +560,6 @@ def detect_from_dataframes(self, data):
 
         self._detect_relationships(data)
 
-    def detect_table_from_csv(self, table_name, filepath, read_csv_parameters=None):
-        """Detect the metadata for a table from a csv file.
-
-        Args:
-            table_name (str):
-                Name of the table to detect.
-            filepath (str):
-                String that represents the ``path`` to the ``csv`` file.
-            read_csv_parameters (dict):
-                A python dictionary of with string and value accepted by ``pandas.read_csv``
-                function. Defaults to ``None``.
-        """
-        self._validate_table_not_detected(table_name)
-        table = SingleTableMetadata()
-        data = _load_data_from_csv(filepath, read_csv_parameters)
-        table._detect_columns(data)
-        self.tables[table_name] = table
-        self._log_detected_table(table)
-
     def detect_from_csvs(self, folder_name, read_csv_parameters=None):
         """Detect the metadata for all tables in a folder of csv files.
 

tests/integration/metadata/test_multi_table.py

@@ -287,56 +287,6 @@ def test_detect_from_csvs(tmp_path):
     assert metadata.to_dict() == expected_metadata
 
 
-def test_detect_table_from_csv(tmp_path):
-    """Test the ``detect_table_from_csv`` method."""
-    # Setup
-    real_data, _ = download_demo(modality='multi_table', dataset_name='fake_hotels')
-
-    metadata = MultiTableMetadata()
-
-    for table_name, dataframe in real_data.items():
-        csv_path = tmp_path / f'{table_name}.csv'
-        dataframe.to_csv(csv_path, index=False)
-
-    # Run
-    metadata.detect_table_from_csv('hotels', tmp_path / 'hotels.csv')
-
-    # Assert
-    metadata.update_column(
-        table_name='hotels',
-        column_name='city',
-        sdtype='categorical',
-    )
-    metadata.update_column(
-        table_name='hotels',
-        column_name='state',
-        sdtype='categorical',
-    )
-    metadata.update_column(
-        table_name='hotels',
-        column_name='classification',
-        sdtype='categorical',
-    )
-    expected_metadata = {
-        'tables': {
-            'hotels': {
-                'columns': {
-                    'hotel_id': {'sdtype': 'id'},
-                    'city': {'sdtype': 'categorical'},
-                    'state': {'sdtype': 'categorical'},
-                    'rating': {'sdtype': 'numerical'},
-                    'classification': {'sdtype': 'categorical'},
-                },
-                'primary_key': 'hotel_id',
-            }
-        },
-        'relationships': [],
-        'METADATA_SPEC_VERSION': 'MULTI_TABLE_V1',
-    }
-
-    assert metadata.to_dict() == expected_metadata
-
-
 def test_get_column_names():
     """Test the ``get_column_names`` method."""
     # Setup

tests/unit/metadata/test_multi_table.py

@@ -2310,82 +2310,10 @@ def test__detect_relationships_circular(self):
         instance.add_relationship.assert_called_once_with('users', 'sessions', 'user_id', 'user_id')
         assert instance.tables['sessions'].columns['user_id']['sdtype'] == 'categorical'
 
-    @patch('sdv.metadata.multi_table.LOGGER')
-    @patch('sdv.metadata.multi_table.SingleTableMetadata')
-    @patch('sdv.metadata.multi_table._load_data_from_csv')
-    def test_detect_table_from_csv(self, load_csv_mock, single_table_mock, log_mock):
-        """Test the ``detect_table_from_csv`` method.
-
-        If the table does not already exist, a ``SingleTableMetadata`` instance
-        should be created and call the ``detect_from_csv`` method.
-
-        Setup:
-            - Mock the ``SingleTableMetadata`` class and the logger.
-
-        Assert:
-            - Table should be added to ``self.tables``.
-        """
-        # Setup
-        metadata = MultiTableMetadata()
-        fake_data = Mock()
-        load_csv_mock.return_value = fake_data
-        single_table_mock.return_value.to_dict.return_value = {
-            'columns': {'a': {'sdtype': 'numerical'}}
-        }
-
-        # Run
-        metadata.detect_table_from_csv('table', 'path.csv')
-
-        # Assert
-        load_csv_mock.assert_called_once_with('path.csv', None)
-        single_table_mock.return_value._detect_columns.assert_called_once_with(fake_data)
-        assert metadata.tables == {'table': single_table_mock.return_value}
-
-        expected_log_calls = call(
-            'Detected metadata:\n'
-            '{\n'
-            '    "columns": {\n'
-            '        "a": {\n'
-            '            "sdtype": "numerical"\n'
-            '        }\n'
-            '    }'
-            '\n}'
-        )
-        log_mock.info.assert_has_calls([expected_log_calls])
-
-    def test_detect_table_from_csv_table_already_exists(self):
-        """Test the ``detect_table_from_csv`` method.
-
-        If the table already exists, an error should be raised.
-
-        Setup:
-            - Set the ``_tables`` dict to already have the table.
-
-        Input:
-            - Table name.
-            - Path.
-
-        Side effect:
-            - An error should be raised.
-        """
-        # Setup
-        metadata = MultiTableMetadata()
-        metadata.tables = {'table': Mock()}
-
-        # Run
-        error_message = (
-            "Metadata for table 'table' already exists. Specify a new table name or "
-            'create a new MultiTableMetadata object for other data sources.'
-        )
-        with pytest.raises(InvalidMetadataError, match=error_message):
-            metadata.detect_table_from_csv('table', 'path.csv')
 
     @patch('sdv.metadata.multi_table._load_data_from_csv')
     def test_detect_from_csvs(self, load_data_mock, tmp_path):
-        """Test the ``detect_from_csvs`` method.
-
-        The method should call ``detect_table_from_csv`` for each csv in the folder.
-        """
+        """Test the ``detect_from_csvs`` method."""
         # Setup
         instance = MultiTableMetadata()
         instance.detect_table_from_dataframe = Mock()