我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用sqlalchemy.insert()。
def _versioned_update(row, session, user_id=None): if not utils.is_modified(row, ignore={'va_id'}): return # Check if composite key has been changed for col in row.va_version_columns: hist = getattr(sa.inspect(row).attrs, col).history if hist.has_changes(): # delete the original row from the archive table session.execute( sa.insert(row.ArchiveTable), row.ArchiveTable.build_row_dict( row, session, user_id=user_id, deleted=True, use_dirty=False ) ) result = session.execute( sa.insert(row.ArchiveTable), row.ArchiveTable.build_row_dict(row, session, user_id=user_id) ) return result.inserted_primary_key[0]
def load_session(db_path): """Load and return a new SQLalchemy session and engine. Parameters ---------- db_path : str Path to desired database location, can be relative or use tilde to specify the user $HOME. Returns ------- session : sqlalchemy.orm.session.Session Session instance. engine : sqlalchemy.engine.Engine Engine instance. """ db_path = "sqlite:///" + path.abspath(path.expanduser(db_path)) engine = create_engine(db_path, echo=False) #it is very important that `autoflush == False`, otherwise if "treatments" or "measurements" entried precede "external_ids" the latter will insert a null on the animal_id column Session = sessionmaker(bind=engine, autoflush=False) session = Session() Base.metadata.create_all(engine) return session, engine
def write_version_info(version_table, version_value): """ Inserts the version value in to the version table. Parameters ---------- version_table : sa.Table The version table of the asset database version_value : int The version to write in to the database """ sa.insert(version_table, values={'version': version_value}).execute()
def _generate_output_dataframe(data_subset, defaults): """ Generates an output dataframe from the given subset of user-provided data, the given column names, and the given default values. Parameters ---------- data_subset : DataFrame A DataFrame, usually from an AssetData object, that contains the user's input metadata for the asset type being processed defaults : dict A dict where the keys are the names of the columns of the desired output DataFrame and the values are the default values to insert in the DataFrame if no user data is provided Returns ------- DataFrame A DataFrame containing all user-provided metadata, and default values wherever user-provided metadata was missing """ # The columns provided. cols = set(data_subset.columns) desired_cols = set(defaults) # Drop columns with unrecognised headers. data_subset.drop(cols - desired_cols, axis=1, inplace=True) # Get those columns which we need but # for which no data has been supplied. for col in desired_cols - cols: # write the default value for any missing columns data_subset[col] = defaults[col] return data_subset
def write_version_info(conn, version_table, version_value): """ Inserts the version value in to the version table. Parameters ---------- conn : sa.Connection The connection to use to execute the insert. version_table : sa.Table The version table of the asset database version_value : int The version to write in to the database """ conn.execute(sa.insert(version_table, values={'version': version_value}))
def test_json_encode_read_failure(self): self.session.execute( sa.insert(sa.table('test', sa.column('json_list')), values={'json_list': '{"a": "b"}'}) ) try: self.session.query(TestModel).first() except ValueError as e: self.assertEqual(e.message, "value of type <type 'dict'> is not <type 'list'>") return self.assertTrue(False, 'Test should have raised ValueError')
def _versioned_delete(row, session, user_id=None): result = session.execute( sa.insert(row.ArchiveTable), row.ArchiveTable.build_row_dict(row, session, deleted=True, user_id=user_id) ) # This should not matter since the row is being deleted anyways from the user # table but is here if we ever decide to implement soft deletes on the user table return result.inserted_primary_key[0]
def _versioned_insert(row, session, user_id=None): result = session.execute( sa.insert(row.ArchiveTable), row.ArchiveTable.build_row_dict(row, session, user_id=user_id) ) return result.inserted_primary_key[0]
def get_or_create(session, table, thread, board): instance = session.query(table).filter_by( thread=thread, archived=False, board=board).first() if instance: return instance else: try: i = insert(table) i = i.values({"thread": thread, "archived": False, "board": board}) session.execute(i) session.commit() except IntegrityError as e: logger.info(e)
def _generate_output_dataframe(data_subset, defaults): """ Generates an output dataframe from the given subset of user-provided data, the given column names, and the given default values. Parameters ---------- data_subset : DataFrame A DataFrame, usually from an AssetData object, that contains the user's input metadata for the asset type being processed defaults : dict A dict where the keys are the names of the columns of the desired output DataFrame and the values are the default values to insert in the DataFrame if no user data is provided Returns ------- DataFrame A DataFrame containing all user-provided metadata, and default values wherever user-provided metadata was missing """ # The columns provided. cols = set(data_subset.columns) desired_cols = set(defaults) # Drop columns with unrecognised headers. data_subset.drop(cols - desired_cols, axis=1, inplace=True) # Get those columns which we need but # for which no data has been supplied. need = desired_cols - cols # Combine the users supplied data with our required columns. output = pd.concat( (data_subset, pd.DataFrame( {k: defaults[k] for k in need}, data_subset.index, )), axis=1, copy=False ) return output
