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"

Numbers adaptation

  • 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, flaot4) 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.

Strings adaptation

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

  • 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.

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 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')