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

When you use the higher level methods of the classic pg module like DB.insert() or DB.update(), you don’t need to care about adaptation of parameters, since all of this is happening automatically behind the scenes. You only need to consider this issue when creating SQL commands manually and sending them to the database using the DB.query() method.

Imagine you have created a user login form that stores the login name as login and the password as passwd and you now want to get the user data for that user. You may be tempted to execute a query like this:

>>> db = pg.DB(...)
>>> sql = "SELECT * FROM user_table WHERE login = '%s' AND passwd = '%s'"
>>> db.query(sql % (login, passwd)).getresult()[0]

This seems to work at a first glance, but you will notice an error as soon as you try to use a login name containing a single quote. Even worse, this error can be exploited through so-called “SQL injection”, where an attacker inserts malicious SQL statements into the query that you never intended to be executed. For instance, with a login name something like ' OR ''=' the attacker could easily log in and see the user data of another user in the database.

One solution for this problem would be to cleanse your input of “dangerous” characters like the single quote, but this is tedious and it is likely that you overlook something or break the application e.g. for users with names like “D’Arcy”. A better solution is to use the escaping functions provided by PostgreSQL which are available as methods on the DB object:

>>> login = "D'Arcy"
>>> db.escape_string(login)

As you see, DB.escape_string() has doubled the single quote which is the right thing to do in SQL. However, there are better ways of passing parameters to the query, without having to manually escape them. If you pass the parameters as positional arguments to DB.query(), then PyGreSQL will send them to the database separately, without the need for quoting them inside the SQL command, and without the problems inherent with that process. In this case you must put placeholders of the form $1, $2 etc. in the SQL command in place of the parameters that should go there. For instance:

>>> sql = "SELECT * FROM user_table WHERE login = $1 AND passwd = $2"
>>> db.query(sql, login, passwd).getresult()[0]

That’s much better. So please always keep the following warning in mind:


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

If you like the % format specifications of Python better than the placeholders used by PostgreSQL, there is still a way to use them, via the DB.query_formatted() method:

>>> sql = "SELECT * FROM user_table WHERE login = %s AND passwd = %s"
>>> db.query_formatted(sql, (login, passwd)).getresult()[0]

Note that we need to pass the parameters not as positional arguments here, but as a single tuple. Also note again that we did not use the % operator of Python to format the SQL string, we just used the %s format specifications of Python and let PyGreSQL care about the formatting. Even better, you can also pass the parameters as a dictionary if you use the DB.query_formatted() method:

>>> sql = """SELECT * FROM user_table
...     WHERE login = %(login)s AND passwd = %(passwd)s"""
>>> parameters = dict(login=login, passwd=passwd)
>>> db.query_formatted(sql, parameters).getresult()[0]

Here is another example:

>>> sql = "SELECT 'Hello, ' || %s || '!'"
>>> db.query_formatted(sql, (login,)).getresult()[0]

You would think that the following even simpler example should work, too:

>>> sql = "SELECT %s"
>>> db.query_formatted(sql, (login,)).getresult()[0]
ProgrammingError: Could not determine data type of parameter $1

The issue here is that DB.query_formatted() by default still uses PostgreSQL parameters, transforming the Python style %s placeholder into a $1 placeholder, and sending the login name separately from the query. In the query we looked at before, the concatenation with other strings made it clear that it should be interpreted as a string. This simple query however does not give PostgreSQL a clue what data type the $1 placeholder stands for.

This is different when you are embedding the login name directly into the query instead of passing it as parameter to PostgreSQL. You can achieve this by setting the inline parameter of DB.query_formatted(), like so:

>>> sql = "SELECT %s"
>>> db.query_formatted(sql, (login,), inline=True).getresult()[0]

Another way of making this query work while still sending the parameters separately is to simply cast the parameter values:

>>> sql = "SELECT %s::text"
>>> db.query_formatted(sql, (login,), inline=False).getresult()[0]

In real world examples you will rarely have to cast your parameters like that, since in an INSERT statement or a WHERE clause comparing the parameter to a table column, the data type will be clear from the context.

When binding the parameters to a query, PyGreSQL not only adapts 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]])
>>> db.query_formatted("SELECT %(array)s::int[]", params).getresult()[0][0]
[[1, 2], [3, 4]]

Note that again we need to cast the array parameter or use inline parameters only because this simple query does not provide enough context. Also note that the query gives the value back as Python lists again. This is achieved by the typecasting mechanism explained in the next section.

Tuples are adapted as PostgreSQL composite types. If you use inline parameters, they can also be used with the IN syntax.

Let’s think of a more real world example again 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:

>>> db.query_formatted("INSERT INTO on_hand VALUES (%(item)s, %(count)s)",
...     dict(item=inventory_item('fuzzy dice', 42, 1.99), count=1000))
>>> db.query("SELECT * FROM on_hand").getresult()[0][0]
Row(item=inventory_item(name='fuzzy dice', supplier_id=42,
        price=Decimal('1.99')), count=1000)

The DB.insert() method provides a simpler way to achieve the same:

>>> row = dict(item=inventory_item('fuzzy dice', 42, 1.99), count=1000)
>>> db.insert('on_hand', row)
{'count': 1000,  'item': inventory_item(name='fuzzy dice',
        supplier_id=42, price=Decimal('1.99'))}

Perhaps we want to use custom Python classes instead of named tuples to hold our values:

>>> 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. This is because PyGreSQL tries to pass the string representation of the object as a parameter to PostgreSQL, but this is just a human readable string and not useful for PostgreSQL to build a composite type. However, it is possible to make such custom classes adapt themselves to PostgreSQL by adding a “magic” method with the name __pg_str__, like so:

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

Now you can insert class instances the same way as you insert named tuples. You can even make these objects adapt to different types in different ways:

>>> class InventoryItem:
...     ...
...     def __pg_str__(self, typ):
...         if typ == 'text':
...             return str(self)
...        return (, self.supplier_id, self.price)
>>> db.query("ALTER TABLE on_hand ADD COLUMN remark varchar")
>>> item=InventoryItem('fuzzy dice', 42, 1.99)
>>> row = dict(item=item, remark=item, count=1000)
>>> db.insert('on_hand', row)
{'count': 1000, 'item': inventory_item(name='fuzzy dice',
    supplier_id=42, price=Decimal('1.99')),
    'remark': 'fuzzy dice (from 42, at $1.99)'}

There is also another “magic” method __pg_repr__ which does not take the typ parameter. That method is used instead of __pg_str__ when passing parameters inline. You must be more careful when using __pg_repr__, because it must return a properly escaped string that can be put literally inside the SQL. The only exception is when you return a tuple or list, because these will be adapted and properly escaped by PyGreSQL again.

Typecasting to Python

As you noticed, PyGreSQL automatically converted the PostgreSQL data to suitable Python objects when returning values via the DB.get(), Query.getresult() and similar methods. 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. 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:

>>> pg.get_typecast('int4')

In the classic PyGreSQL module, the typecasting for these basic types is always done internally by the C extension module for performance reasons. We can set a different typecast function for int4, but it will not become effective, the C module continues to use its internal typecasting.

However, we can add new typecast functions for the database types that are not supported by the C module. For example, we can create a typecast function that casts items of the composite PostgreSQL type used as example in the previous section to instances of the corresponding Python class.

To do this, at first we get the default typecast function that PyGreSQL has created for the current DB connection. This default function casts composite types to named tuples, as we have seen in the section before. We can grab it from the DB.dbtypes object as follows:

>>> cast_tuple = db.dbtypes.get_typecast('inventory_item')

Now we can create a new typecast function that converts the tuple to an instance of our custom class:

>>> cast_item = lambda value: InventoryItem(*cast_tuple(value))

Finally, we set this typecast function, either globally with set_typecast(), or locally for the current connection like this:

>>> db.dbtypes.set_typecast('inventory_item', cast_item)

Now we can get instances of our custom class directly from the database:

>>> item = db.query("SELECT * FROM on_hand").getresult()[0][0]
>>> str(item)
'fuzzy dice (from 42, at $1.99)'

Note that some of the typecast functions used by the C module are configurable with separate module level functions, such as set_decimal(), set_bool() or set_jsondecode(). You need to use these instead of set_typecast() if you want to change the behavior of the C module.

Also note that after changing global typecast functions with set_typecast(), you may need to run db.dbtypes.reset_typecast() to make these changes effective on connections that were already open.

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:

>>> q = db.query("""SELECT &&
...     FROM circle c1, circle c2
...     WHERE = 'C1' AND = 'C2'""")
>>> q.getresult()[0][0]

However, calculating the intersection points between the two circles using the # operator does not work (at least not as of PostgreSQL version 14). So let’s 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].split(',')
...     p = p[1:-1].split(',')
...     return Circle(Point(float(p[0]), float(p[1])), float(r))
>>> pg.set_typecast('circle', cast_circle)

Now we can import the circles in the table into Python simply using:

>>> circle = db.get_as_dict('circle', scalar=True)

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)
>>> circle['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)]