Remarks on Adaptation and Typecasting

Both PostgreSQL and Python have the concept of data types, but there are of course differences between the two type systems. Therefore PyGreSQL needs to adapt Python objects to the representation required by PostgreSQL when passing values as query parameters, and it needs to typecast the representation of PostgreSQL data types returned by database queries to Python objects. Here are some explanations about how this works in detail in case you want to better understand or change the default behavior of PyGreSQL.

Supported data types

The following automatic data type conversions are supported by PyGreSQL out of the box. If you need other automatic type conversions or want to change the default conversions, you can achieve this by using the methods explained in the next two sections.



char, bpchar, name, text, varchar






int2, int4, int8, oid, serial



list of int

float4, float8


numeric, money



time, timetz


timestamp, timestamptz






json, jsonb

list or dict




list [1]




Elements of arrays and records will also be converted accordingly.

Adaptation of parameters

PyGreSQL knows how to adapt the common Python types to get a suitable representation of their values for PostgreSQL when you pass parameters to a query. For example:

>>> con = pgdb.connect(...)
>>> cur = con.cursor()
>>> parameters = (144, 3.75, 'hello', None)
>>> tuple(cur.execute('SELECT %s, %s, %s, %s', parameters).fetchone()
(144, Decimal('3.75'), 'hello', None)

This is the result we can expect, so obviously PyGreSQL has adapted the parameters and sent the following query to PostgreSQL:

SELECT 144, 3.75, 'hello', NULL

Note the subtle, but important detail that even though the SQL string passed to cur.execute() contains conversion specifications normally used in Python with the % operator for formatting strings, we didn’t use the % operator to format the parameters, but passed them as the second argument to cur.execute(). I.e. we didn’t write the following:

>>> tuple(cur.execute('SELECT %s, %s, %s, %s' % parameters).fetchone()

If we had done this, PostgreSQL would have complained because the parameters were not adapted. Particularly, there would be no quotes around the value 'hello', so PostgreSQL would have interpreted this as a database column, which would have caused a ProgrammingError. Also, the Python value None would have been included in the SQL command literally, instead of being converted to the SQL keyword NULL, which would have been another reason for PostgreSQL to complain about our bad query:

SELECT 144, 3.75, hello, None

Even worse, building queries with the use of the % operator makes us vulnerable to so called “SQL injection” exploits, where an attacker inserts malicious SQL statements into our queries that we never intended to be executed. We could avoid this by carefully quoting and escaping the parameters, but this would be tedious and if we overlook something, our code will still be vulnerable. So please don’t do this. This cannot be emphasized enough, because it is such a subtle difference and using the % operator looks so natural:


Remember to never insert parameters directly into your queries using the % operator. Always pass the parameters separately.

The good thing is that by letting PyGreSQL do the work for you, you can treat all your parameters equally and don’t need to ponder where you need to put quotes or need to escape strings. You can and should also always use the general %s specification instead of e.g. using %d for integers. Actually, to avoid mistakes and make it easier to insert parameters at more than one location, you can and should use named specifications, like this:

>>> params = dict(greeting='Hello', name='HAL')
>>> sql = """SELECT %(greeting)s || ', ' || %(name)s
...    || '. Do you read me, ' || %(name)s || '?'"""
>>> cur.execute(sql, params).fetchone()[0]
'Hello, HAL. Do you read me, HAL?'

PyGreSQL does not only adapt the basic types like int, float, bool and str, but also tries to make sense of Python lists and tuples.

Lists are adapted as PostgreSQL arrays:

>>> params = dict(array=[[1, 2],[3, 4]])
>>> cur.execute("SELECT %(array)s", params).fetchone()[0]
[[1, 2], [3, 4]]

Note that the query gives the value back as Python lists again. This is achieved by the typecasting mechanism explained in the next section. The query that was actually executed was this:

SELECT ARRAY[[1,2],[3,4]]

Again, if we had inserted the list using the % operator without adaptation, the ARRAY keyword would have been missing in the query.

Tuples are adapted as PostgreSQL composite types:

>>> params = dict(record=('Bond', 'James'))
>>> cur.execute("SELECT %(record)s", params).fetchone()[0]
('Bond', 'James')

You can also use this feature with the IN syntax of SQL:

>>> params = dict(what='needle', where=('needle', 'haystack'))
>>> cur.execute("SELECT %(what)s IN %(where)s", params).fetchone()[0]

Sometimes a Python type can be ambiguous. For instance, you might want to insert a Python list not into an array column, but into a JSON column. Or you want to interpret a string as a date and insert it into a DATE column. In this case you can give PyGreSQL a hint by using Type constructors:

>>> cur.execute("CREATE TABLE json_data (data json, created date)")
>>> params = dict(
...     data=pgdb.Json([1, 2, 3]), created=pgdb.Date(2016, 1, 29))
>>> sql = ("INSERT INTO json_data VALUES (%(data)s, %(created)s)")
>>> cur.execute(sql, params)
>>> cur.execute("SELECT * FROM json_data").fetchone()
Row(data=[1, 2, 3], created='2016-01-29')

Let’s think of another example where we create a table with a composite type in PostgreSQL:

CREATE TABLE on_hand (
    item      inventory_item,
    count     integer)

We assume the composite type inventory_item has been created like this:

CREATE TYPE inventory_item AS (
    name            text,
    supplier_id     integer,
    price           numeric)

In Python we can use a named tuple as an equivalent to this PostgreSQL type:

>>> from collections import namedtuple
>>> inventory_item = namedtuple(
...     'inventory_item', ['name', 'supplier_id', 'price'])

Using the automatic adaptation of Python tuples, an item can now be inserted into the database and then read back as follows:

>>> cur.execute("INSERT INTO on_hand VALUES (%(item)s, %(count)s)",
...     dict(item=inventory_item('fuzzy dice', 42, 1.99), count=1000))
>>> cur.execute("SELECT * FROM on_hand").fetchone()
Row(item=inventory_item(name='fuzzy dice', supplier_id=42,
        price=Decimal('1.99')), count=1000)

However, we may not want to use named tuples, but custom Python classes to hold our values, like this one:

>>> class InventoryItem:
...     def __init__(self, name, supplier_id, price):
... = name
...         self.supplier_id = supplier_id
...         self.price = price
...     def __str__(self):
...         return '{} (from {}, at ${})'.format(
...   , self.supplier_id, self.price)

But when we try to insert an instance of this class in the same way, we will get an error:

>>> cur.execute("INSERT INTO on_hand VALUES (%(item)s, %(count)s)",
...     dict(item=InventoryItem('fuzzy dice', 42, 1.99), count=1000))
InterfaceError: Do not know how to adapt type <class 'InventoryItem'>

While PyGreSQL knows how to adapt tuples, it does not know what to make out of our custom class. To simply convert the object to a string using the str function is not a solution, since this yields a human readable string that is not useful for PostgreSQL. However, it is possible to make such custom classes adapt themselves to PostgreSQL by adding a “magic” method with the name __pg_repr__, like this:

>>> class InventoryItem:
  ...     ...
  ...     def __str__(self):
  ...         return '{} (from {}, at ${})'.format(
  ...   , self.supplier_id, self.price)
  ...     def __pg_repr__(self):
  ...         return (, self.supplier_id, self.price)

Now you can insert class instances the same way as you insert named tuples.

Note that PyGreSQL adapts the result of __pg_repr__ again if it is a tuple or a list. Otherwise, it must be a properly escaped string.

Typecasting to Python

As you noticed, PyGreSQL automatically converted the PostgreSQL data to suitable Python objects when returning values via one of the “fetch” methods of a cursor. This is done by the use of built-in typecast functions.

If you want to use different typecast functions or add your own if no built-in typecast function is available, then this is possible using the set_typecast() function. With the get_typecast() function you can check which function is currently set, and reset_typecast() allows you to reset the typecast function to its default. If no typecast function is set, then PyGreSQL will return the raw strings from the database.

For instance, you will find that PyGreSQL uses the normal int function to cast PostgreSQL int4 type values to Python:

>>> pgdb.get_typecast('int4')

You can change this to return float values instead:

>>> pgdb.set_typecast('int4', float)
>>> con = pgdb.connect(...)
>>> cur = con.cursor()
>>> cur.execute('select 42::int4').fetchone()[0]

Note that the connections cache the typecast functions, so you may need to reopen the database connection, or reset the cache of the connection to make this effective, using the following command:

>>> con.type_cache.reset_typecast()

The TypeCache of the connection can also be used to change typecast functions locally for one database connection only.

As a more useful example, we can create a typecast function that casts items of the composite type used as example in the previous section to instances of the corresponding Python class:

>>> con.type_cache.reset_typecast()
>>> cast_tuple = con.type_cache.get_typecast('inventory_item')
>>> cast_item = lambda value: InventoryItem(*cast_tuple(value))
>>> con.type_cache.set_typecast('inventory_item', cast_item)
>>> str(cur.execute("SELECT * FROM on_hand").fetchone()[0])
'fuzzy dice (from 42, at $1.99)'

As you saw in the last section you, PyGreSQL also has a typecast function for JSON, which is the default JSON decoder from the standard library. Let’s assume we want to use a slight variation of that decoder in which every integer in JSON is converted to a float in Python. This can be accomplished as follows:

>>> from json import loads
>>> cast_json = lambda v: loads(v, parse_int=float)
>>> pgdb.set_typecast('json', cast_json)
>>> cur.execute("SELECT data FROM json_data").fetchone()[0]
[1.0, 2.0, 3.0]

Note again that you may need to run con.type_cache.reset_typecast() to make this effective. Also note that the two types json and jsonb have their own typecast functions, so if you use jsonb instead of json, you need to use this type name when setting the typecast function:

>>> pgdb.set_typecast('jsonb', cast_json)

As one last example, let us try to typecast the geometric data type circle of PostgreSQL into a SymPy Circle object. Let’s assume we have created and populated a table with two circles, like so:

    name varchar(8) primary key, circle circle);
INSERT INTO circle VALUES ('C1', '<(2, 3), 3>');
INSERT INTO circle VALUES ('C2', '<(1, -1), 4>');

With PostgreSQL we can easily calculate that these two circles overlap:

>>> con.cursor().execute("""SELECT &&
...     FROM circle c1, circle c2
...     WHERE = 'C1' AND = 'C2'""").fetchone()[0]

However, calculating the intersection points between the two circles using the # operator does not work (at least not as of PostgreSQL version 9.5). So let’ resort to SymPy to find out. To ease importing circles from PostgreSQL to SymPy, we create and register the following typecast function:

>>> from sympy import Point, Circle
>>> def cast_circle(s):
...     p, r = s[1:-1].rsplit(',', 1)
...     p = p[1:-1].split(',')
...     return Circle(Point(float(p[0]), float(p[1])), float(r))
>>> pgdb.set_typecast('circle', cast_circle)

Now we can import the circles in the table into Python quite easily:

>>> circle = { for c in con.cursor().execute(
...     "SELECT * FROM circle").fetchall()}

The result is a dictionary mapping circle names to SymPy Circle objects. We can verify that the circles have been imported correctly:

>>> circle
{'C1': Circle(Point(2, 3), 3.0),
 'C2': Circle(Point(1, -1), 4.0)}

Finally we can find the exact intersection points with SymPy:

>>> circle['C1'].intersection(circle['C2'])
[Point(29/17 + 64564173230121*sqrt(17)/100000000000000,
    -80705216537651*sqrt(17)/500000000000000 + 31/17),
 Point(-64564173230121*sqrt(17)/100000000000000 + 29/17,
    80705216537651*sqrt(17)/500000000000000 + 31/17)]