rows – row factory implementations

The module exposes a few generic RowFactory implementation, which can be used to retrieve data from the database in more complex structures than the basic tuples.

Check out Row factories for information about how to use these objects.

psycopg.rows.tuple_row(cursor: BaseCursor[Any, Any]) → RowMaker[TupleRow]

Row factory to represent rows as simple tuples.

This is the default factory, used when connect() or cursor() are called without a row_factory parameter.

psycopg.rows.dict_row(cursor: BaseCursor[Any, Any]) → RowMaker[DictRow]

Row factory to represent rows as dictionaries.The dictionary keys are taken from the column names of the returned columns.

psycopg.rows.namedtuple_row(cursor: BaseCursor[Any, Any]) → RowMaker[NamedTuple]

Row factory to represent rows as namedtuple.The field names are taken from the column names of the returned columns, with some mangling to deal with invalid names.

psycopg.rows.class_row(cls: Type[T]) → BaseRowFactory[T]

Generate a row factory to represent rows as instances of the class cls.The class must support every output column name as a keyword parameter.

PARAMETERS:

cls – The class to return for each row. It must support the fields returned by the query as keyword arguments.

RETURN TYPE:

Callable[[Cursor], RowMaker[~T]]

This is not a row factory, but rather a factory of row factories. Specifying row_factory=class_row(MyClass) will create connections and cursors returning MyClass objects on fetch.

Example:

from dataclasses import dataclass
import psycopg
from psycopg.rows import class_row

@dataclass
class Person:
    first_name: str
    last_name: str
    age: int = None

conn = psycopg.connect()
cur = conn.cursor(row_factory=class_row(Person))

cur.execute("select 'John' as first_name, 'Smith' as last_name").fetchone()
# Person(first_name='John', last_name='Smith', age=None)

psycopg.rows.args_row(func: Callable[[…], T]) → BaseRowFactory[T]

Generate a row factory calling func with positional parameters for every row.

PARAMETERS:

func – The function to call for each row. It must support the fields returned by the query as positional arguments.

psycopg.rows.kwargs_row(func: Callable[[…], T]) → BaseRowFactory[T]

Generate a row factory calling func with keyword parameters for every row.

PARAMETERS:

func – The function to call for each row. It must support the fields returned by the query as keyword arguments.

These objects can be used to describe your own rows adapter for static typing checks, such as mypy.

class psycopg.rows.RowMaker

Callable protocol taking a sequence of value and returning an object.

The sequence of value is what is returned from a database query, already adapted to the right Python types. The return value is the object that your program would like to receive: by default (tuple_row()) it is a simple tuple, but it may be any type of object.

Typically, RowMaker functions are returned by RowFactory.

__call__(values: Sequence[Any]) → Row

Convert a sequence of values from the database to a finished object.

class psycopg.rows.RowFactory

Callable protocol taking a Cursor and returning a RowMaker.

A RowFactory is typically called when a Cursor receives a result. This way it can inspect the cursor state (for instance the description attribute) and help a RowMaker to create a complete object.

For instance the dict_row() RowFactory uses the names of the column to define the dictionary key and returns a RowMaker function which would use the values to create a dictionary for each record.

__call__(cursor: Cursor[Row]) → RowMaker[Row]

Inspect the result on a cursor and return a RowMaker to convert rows.

class psycopg.rows.AsyncRowFactory

Like RowFactory, taking an async cursor as argument.

class psycopg.rows.BaseRowFactory

Like RowFactory, taking either type of cursor as argument.

Note that it’s easy to implement an object implementing both RowFactory and AsyncRowFactory: usually, everything you need to implement a row factory is to access the cursor’s description, which is provided by both the cursor flavours.