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# Adapting basic Python types

Many standard Python types are adapted into SQL and returned as Python objects when a query is executed.

Converting the following data types between Python and PostgreSQL works out-of-the-box and doesn’t require any configuration. In case you need to customise the conversion you should take a look at Data adaptation configuration.

## Booleans adaptation

Python `bool` values `True` and `False` are converted to the equivalent PostgreSQL boolean type:

    >>> cur.execute("SELECT %s, %s", (True, False))
    # equivalent to "SELECT true, false"

Changed in version 3.2: `numpy.bool_` values can be dumped too.

## Numbers adaptation

See also

- PostgreSQL numeric types

- Python `int` values can be converted to PostgreSQL `smallint`, `integer`, `bigint`, or `numeric`, according to their numeric value. Psycopg will choose the smallest data type available, because PostgreSQL can automatically cast a type up (e.g. passing a `smallint` where PostgreSQL expect an `integer` is gladly accepted) but will not cast down automatically (e.g. if a function has an `integer` argument, passing it a `bigint` value will fail, even if the value is 1).

- Python `float` values are converted to PostgreSQL `float8`.

- Python `Decimal` values are converted to PostgreSQL `numeric`.

On the way back, smaller types (`int2`, `int4`, `float4`) are promoted to the larger Python counterpart.

Note

Sometimes you may prefer to receive `numeric` data as `float` instead, for performance reason or ease of manipulation: you can configure an adapter to cast PostgreSQL numeric to Python float. This of course may imply a loss of precision.

Changed in version 3.2: NumPy integer and floating point values can be dumped too.

## Strings adaptation

See also

- PostgreSQL character types

Python `str` are converted to PostgreSQL string syntax, and PostgreSQL types such as `text` and `varchar` are converted back to Python `str`:

    conn = psycopg.connect()
    conn.execute(
        "INSERT INTO menu (id, entry) VALUES (%s, %s)",
        (1, "Crème Brûlée at 4.99€"))
    conn.execute("SELECT entry FROM menu WHERE id = 1").fetchone()[0]
    'Crème Brûlée at 4.99€'

PostgreSQL databases have an encoding, and the session has an encoding too, exposed in the `Connection.info.``encoding` attribute. If your database and connection are in UTF-8 encoding you will likely have no problem, otherwise you will have to make sure that your application only deals with the non-ASCII chars that the database can handle; failing to do so may result in encoding/decoding errors:

    # The encoding is set at connection time according to the db configuration
    conn.info.encoding
    'utf-8'

    # The Latin-9 encoding can manage some European accented letters
    # and the Euro symbol
    conn.execute("SET client_encoding TO LATIN9")
    conn.execute("SELECT entry FROM menu WHERE id = 1").fetchone()[0]
    'Crème Brûlée at 4.99€'

    # The Latin-1 encoding doesn't have a representation for the Euro symbol
    conn.execute("SET client_encoding TO LATIN1")
    conn.execute("SELECT entry FROM menu WHERE id = 1").fetchone()[0]
    # Traceback (most recent call last)
    # ...
    # UntranslatableCharacter: character with byte sequence 0xe2 0x82 0xac
    # in encoding "UTF8" has no equivalent in encoding "LATIN1"

In rare cases you may have strings with unexpected encodings in the database. Using the `SQL_ASCII` client encoding will disable decoding of the data coming from the database, which will be returned as `bytes`:

    conn.execute("SET client_encoding TO SQL_ASCII")
    conn.execute("SELECT entry FROM menu WHERE id = 1").fetchone()[0]
    b'Cr\xc3\xa8me Br\xc3\xbbl\xc3\xa9e at 4.99\xe2\x82\xac'

Alternatively you can cast the unknown encoding data to `bytea` to retrieve it as bytes, leaving other strings unaltered: see Binary adaptation

Note that PostgreSQL text cannot contain the `0x00` byte. If you need to store Python strings that may contain binary zeros you should use a `bytea` field.

## Binary adaptation

Python types representing binary objects (`bytes`, `bytearray`, `memoryview`) are converted by default to `bytea` fields. By default data received is returned as `bytes`.

If you are storing large binary data in bytea fields (such as binary documents or images) you should probably use the binary format to pass and return values, otherwise binary data will undergo ASCII escaping, taking some CPU time and more bandwidth. See Binary parameters and results for details.

## Date/time types adaptation

See also

- PostgreSQL date/time types

- Python `date` objects are converted to PostgreSQL `date`.

- Python `datetime` objects are converted to PostgreSQL `timestamp` (if they don’t have a `tzinfo` set) or `timestamptz` (if they do).

- Python `time` objects are converted to PostgreSQL `time` (if they don’t have a `tzinfo` set) or `timetz` (if they do).

- Python `timedelta` objects are converted to PostgreSQL `interval`.

PostgreSQL `timestamptz` values are returned with a timezone set to the connection TimeZone setting, which is available as a Python `ZoneInfo` object in the `Connection.info`.`timezone` attribute:

    >>> conn.info.timezone
    zoneinfo.ZoneInfo(key='Europe/London')

    >>> conn.execute("select '2048-07-08 12:00'::timestamptz").fetchone()[0]
    datetime.datetime(2048, 7, 8, 12, 0, tzinfo=zoneinfo.ZoneInfo(key='Europe/London'))

Note

PostgreSQL `timestamptz` doesn’t store “a timestamp with a timezone attached”: it stores a timestamp always in UTC, which is converted, on output, to the connection TimeZone setting:

    >>> conn.execute("SET TIMEZONE to 'Europe/Rome'")  # UTC+2 in summer

    >>> conn.execute("SELECT '2042-07-01 12:00Z'::timestamptz").fetchone()[0]  # UTC input
    datetime.datetime(2042, 7, 1, 14, 0, tzinfo=zoneinfo.ZoneInfo(key='Europe/Rome'))

Check out the PostgreSQL documentation about timezones for all the details.

Warning

Times with timezone are silly objects, because you cannot know the offset of a timezone with daylight saving time rules without knowing the date too.

Although silly, times with timezone are supported both by Python and by PostgreSQL. However they are only supported with fixed offset timezones: Postgres `timetz` values loaded from the database will result in Python `time` objects with `tzinfo` attributes specified as fixed offset, for instance by a `timezone` value:

    >>> conn.execute("SET TIMEZONE to 'Europe/Rome'")

    # UTC+1 in winter
    >>> conn.execute("SELECT '2042-01-01 12:00Z'::timestamptz::timetz").fetchone()[0]
    datetime.time(13, 0, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600)))

    # UTC+2 in summer
    >>> conn.execute("SELECT '2042-07-01 12:00Z'::timestamptz::timetz").fetchone()[0]
    datetime.time(14, 0, tzinfo=datetime.timezone(datetime.timedelta(seconds=7200)))

Dumping Python `time` objects is only supported with fixed offset `tzinfo`, such as the ones returned by Postgres, or by whatever `tzinfo` implementation resulting in the time’s `utcoffset` returning a value.

### Dates and times limits in Python

PostgreSQL date and time objects can represent values that cannot be represented by the Python `datetime` objects:

- dates and timestamps after the year 9999, the special value “infinity”;

- dates and timestamps before the year 1, the special value “-infinity”;

- the time 24:00:00.

Loading these values will raise a `DataError`.

If you need to handle these values you can define your own mapping (for instance mapping every value greater than `datetime.date.max` to `date.max`, or the time 24:00 to 00:00) and write a subclass of the default loaders implementing the added capability; please see this example for a reference.

### DateStyle and IntervalStyle limits

Loading `timestamp with time zone` in text format is only supported if the connection DateStyle is set to `ISO` format; time and time zone representation in other formats is ambiguous.

Furthermore, at the time of writing, the only supported value for IntervalStyle is `postgres`; loading `interval` data in text format with a different setting is not supported.

If your server is configured with different settings by default, you can obtain a connection in a supported style using the `options` connection parameter; for example:

    >>> conn = psycopg.connect(options="-c datestyle=ISO,YMD")
    >>> conn.execute("show datestyle").fetchone()[0]
    # 'ISO, YMD'

These GUC parameters only affects loading in text format; loading timestamps or intervals in binary format is not affected by DateStyle or IntervalStyle.

## JSON adaptation

Psycopg can map between Python objects and PostgreSQL json/jsonb types, allowing to customise the load and dump function used.

Because several Python objects could be considered JSON (dicts, lists, scalars, even date/time if using a dumps function customised to use them), Psycopg requires you to wrap the object to dump as JSON into a wrapper: either `psycopg.types.json.Json` or `Jsonb`.

    from psycopg.types.json import Jsonb

    thing = {"foo": ["bar", 42]}
    conn.execute("INSERT INTO mytable VALUES (%s)", [Jsonb(thing)])

By default Psycopg uses the standard library `json.dumps` and `json.loads` functions to serialize and de-serialize Python objects to JSON. If you want to customise how serialization happens, for instance changing serialization parameters or using a different JSON library, you can specify your own functions using the `psycopg.types.json.set_json_dumps()` and `set_json_loads()` functions, to apply either globally or to a specific context (connection or cursor).

    from functools import partial
    from psycopg.types.json import Jsonb, set_json_dumps, set_json_loads
    import ujson

    # Use a faster dump function
    set_json_dumps(ujson.dumps)

    # Return floating point values as Decimal, just in one connection
    set_json_loads(partial(json.loads, parse_float=Decimal), conn)

    conn.execute("SELECT %s", [Jsonb({"value": 123.45})]).fetchone()[0]
    # {'value': Decimal('123.45')}

If you need an even more specific dump customisation only for certain objects (including different configurations in the same query) you can specify a `dumps` parameter in the `Json`/`Jsonb` wrapper, which will take precedence over what is specified by `set_json_dumps()`.

    from uuid import UUID, uuid4

    class UUIDEncoder(json.JSONEncoder):
        """A JSON encoder which can dump UUID."""
        def default(self, obj):
            if isinstance(obj, UUID):
                return str(obj)
            return json.JSONEncoder.default(self, obj)

    uuid_dumps = partial(json.dumps, cls=UUIDEncoder)
    obj = {"uuid": uuid4()}
    cnn.execute("INSERT INTO objs VALUES %s", [Json(obj, dumps=uuid_dumps)])
    # will insert: {'uuid': '0a40799d-3980-4c65-8315-2956b18ab0e1'}

## Lists adaptation

Python `list` objects are adapted to PostgreSQL arrays and back. Only lists containing objects of the same type can be dumped to PostgreSQL (but the list may contain `None` elements).

Note

If you have a list of values which you want to use with the `IN` operator… don’t. It won’t work (neither with a list nor with a tuple):

    >>> conn.execute("SELECT * FROM mytable WHERE id IN %s", [[10,20,30]])
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    psycopg.errors.SyntaxError: syntax error at or near "$1"
    LINE 1: SELECT * FROM mytable WHERE id IN $1
                                              ^

What you want to do instead is to use the ‘= ANY()’ expression and pass the values as a list (not a tuple).

    >>> conn.execute("SELECT * FROM mytable WHERE id = ANY(%s)", [[10,20,30]])

This has also the advantage of working with an empty list, whereas `IN ()` is not valid SQL.

## UUID adaptation

Python `uuid.UUID` objects are adapted to PostgreSQL UUID type and back:

    >>> conn.execute("select gen_random_uuid()").fetchone()[0]
    UUID('97f0dd62-3bd2-459e-89b8-a5e36ea3c16c')

    >>> from uuid import uuid4
    >>> conn.execute("select gen_random_uuid() = %s", [uuid4()]).fetchone()[0]
    False  # long shot

## Network data types adaptation

Objects from the `ipaddress` module are converted to PostgreSQL network address types:

- `IPv4Address`, `IPv4Interface` objects are converted to the PostgreSQL `inet` type. On the way back, `inet` values indicating a single address are converted to `IPv4Address`, otherwise they are converted to `IPv4Interface`

- `IPv4Network` objects are converted to the `cidr` type and back.

- `IPv6Address`, `IPv6Interface`, `IPv6Network` objects follow the same rules, with IPv6 `inet` and `cidr` values.

    >>> conn.execute("select '192.168.0.1'::inet, '192.168.0.1/24'::inet").fetchone()
    (IPv4Address('192.168.0.1'), IPv4Interface('192.168.0.1/24'))

    >>> conn.execute("select '::ffff:1.2.3.0/120'::cidr").fetchone()[0]
    IPv6Network('::ffff:102:300/120')

## Enum adaptation

Added in version 3.1.

Psycopg can adapt Python `Enum` subclasses into PostgreSQL enum types (created with the `CREATE TYPE ... AS ENUM (...)` command).

In order to set up a bidirectional enum mapping, you should get information about the PostgreSQL enum using the `EnumInfo` class and register it using `register_enum()`. The behaviour of unregistered and registered enums is different.

- If the enum is not registered with `register_enum()`:

  - Pure `Enum` classes are dumped as normal strings, using their member names as value. The unknown oid is used, so PostgreSQL should be able to use this string in most contexts (such as an enum or a text field).

    Changed in version 3.1: In previous version dumping pure enums is not supported and raise a “cannot adapt” error.

  - Mix-in enums are dumped according to their mix-in type (because a `class MyIntEnum(int, Enum)` is more specifically an `int` than an `Enum`, so it’s dumped by default according to `int` rules).

  - PostgreSQL enums are loaded as Python strings. If you want to load arrays of such enums you will have to find their OIDs using `types.TypeInfo.fetch()` and register them using `register()`.

- If the enum is registered (using `EnumInfo``.fetch()` and `register_enum()`):

  - Enums classes, both pure and mixed-in, are dumped by name.

  - The registered PostgreSQL enum is loaded back as the registered Python enum members.

class psycopg.types.enum.EnumInfo(name: str, oid: int, array_oid: int, labels: Sequence[str])

  Manage information about an enum type.

  `EnumInfo` is a subclass of `TypeInfo`: refer to the latter’s documentation for generic usage, especially the `fetch()` method.

  labels

    After `fetch()`, it contains the labels defined in the PostgreSQL enum type.

  enum

    After `register_enum()` is called, it will contain the Python type mapping to the registered enum.

psycopg.types.enum.register_enum(info: EnumInfo, context: AdaptContext | None = None, enum: type[E] | None = None, *, mapping: EnumMapping[E] = None) → None

  Register the adapters to load and dump a enum type.

  Parameters:

    - info – The object with the information about the enum to register.

    - context – The context where to register the adapters. If `None`, register it globally.

    - enum – Python enum type matching to the PostgreSQL one. If `None`, a new enum will be generated and exposed as `EnumInfo.enum`.

    - mapping – Override the mapping between `enum` members and `info` labels.

  After registering, fetching data of the registered enum will cast PostgreSQL enum labels into corresponding Python enum members.

  If no `enum` is specified, a new `Enum` is created based on PostgreSQL enum labels.

Example:

    >>> from enum import Enum, auto
    >>> from psycopg.types.enum import EnumInfo, register_enum

    >>> class UserRole(Enum):
    ...     ADMIN = auto()
    ...     EDITOR = auto()
    ...     GUEST = auto()

    >>> conn.execute("CREATE TYPE user_role AS ENUM ('ADMIN', 'EDITOR', 'GUEST')")

    >>> info = EnumInfo.fetch(conn, "user_role")
    >>> register_enum(info, conn, UserRole)

    >>> some_editor = info.enum.EDITOR
    >>> some_editor
    <UserRole.EDITOR: 2>

    >>> conn.execute(
    ...     "SELECT pg_typeof(%(editor)s), %(editor)s",
    ...     {"editor": some_editor}
    ... ).fetchone()
    ('user_role', <UserRole.EDITOR: 2>)

    >>> conn.execute(
    ...     "SELECT ARRAY[%s, %s]",
    ...     [UserRole.ADMIN, UserRole.GUEST]
    ... ).fetchone()
    [<UserRole.ADMIN: 1>, <UserRole.GUEST: 3>]

If the Python and the PostgreSQL enum don’t match 1:1 (for instance if members have a different name, or if more than one Python enum should map to the same PostgreSQL enum, or vice versa), you can specify the exceptions using the `mapping` parameter.

`mapping` should be a dictionary with Python enum members as keys and the matching PostgreSQL enum labels as values, or a list of `(member, label)` pairs with the same meaning (useful when some members are repeated). Order matters: if an element on either side is specified more than once, the last pair in the sequence will take precedence:

    # Legacy roles, defined in medieval times.
    >>> conn.execute(
    ...     "CREATE TYPE abbey_role AS ENUM ('ABBOT', 'SCRIBE', 'MONK', 'GUEST')")

    >>> info = EnumInfo.fetch(conn, "abbey_role")
    >>> register_enum(info, conn, UserRole, mapping=[
    ...     (UserRole.ADMIN, "ABBOT"),
    ...     (UserRole.EDITOR, "SCRIBE"),
    ...     (UserRole.EDITOR, "MONK")])

    >>> conn.execute("SELECT '{ABBOT,SCRIBE,MONK,GUEST}'::abbey_role[]").fetchone()[0]
    [<UserRole.ADMIN: 1>,
     <UserRole.EDITOR: 2>,
     <UserRole.EDITOR: 2>,
     <UserRole.GUEST: 3>]

    >>> conn.execute("SELECT %s::text[]", [list(UserRole)]).fetchone()[0]
    ['ABBOT', 'MONK', 'GUEST']

A particularly useful case is when the PostgreSQL labels match the values of a `str`-based Enum. In this case it is possible to use something like `{m: m.value for m in enum}` as mapping:

    >>> class LowercaseRole(str, Enum):
    ...     ADMIN = "admin"
    ...     EDITOR = "editor"
    ...     GUEST = "guest"

    >>> conn.execute(
    ...     "CREATE TYPE lowercase_role AS ENUM ('admin', 'editor', 'guest')")

    >>> info = EnumInfo.fetch(conn, "lowercase_role")
    >>> register_enum(
    ...     info, conn, LowercaseRole, mapping={m: m.value for m in LowercaseRole})

    >>> conn.execute("SELECT 'editor'::lowercase_role").fetchone()[0]
    <LowercaseRole.EDITOR: 'editor'>