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Python namedtuple tutorial

last modified July 6, 2020

Python namedtuple tutorial shows how to work with namedtuples in Python.

Python namedtuple

Python namedtuple is an immutable container type, whose values can be accessed with indexes and named attributes. It has functionality like tuples with additional features. A named tuple is created with the collections.namedtuple factory function.

Named tuples are essentially easy-to-create, immutable, lightweight object types. Named tuples can be used to make the code more clean and Pythonic. They are similar to records in other languages (C#, Java).

Python namedtuple basic example

The following is a simple example with a namedtuple.

basic.py
#!/usr/bin/env python3

from collections import namedtuple


City = namedtuple('City' , 'name population')

c1 = City('Bratislava', 432000)
c2 = City('Budapest', 1759000)

print(c1)
print(c2)

The example create city namedtuples.

from collections import namedtuple

First, we import the namedtuple type from the collections module.

City = namedtuple('City' , 'name population')

We define the namedtuple. The first argument is the name for the namedtuple. The second argument are the field names. These can be specified in a string 'name population' or in a list ['name', 'population'].

c1 = City('Bratislava', 432000)
c2 = City('Budapest', 1759000)

Here we create two namedtuple objects.

$ ./basic.py 
City(name='Bratislava', population=432000)
City(name='Budapest', population=1759000)

This is the output.

Python namedtuple accessing

The namedtuples can be accessed using indexing and their named attributes.

accessing.py
#!/usr/bin/env python3

from collections import namedtuple


City = namedtuple('City' , 'name population')

c1 = City('Bratislava', 432000)
c2 = City('Budapest', 1759000)

print(c1[0])
print(c1[1])

print(c2.name)
print(c2.population)

In the example, we demonstrate both ways.

$ ./accessing.py 
Bratislava
432000
Budapest
1759000

This is the output.

Python namedtuple unpacking

The unpacking is storing iterable elements into variables or function arguments.

unpacking.py
#!/usr/bin/env python3

from collections import namedtuple


City = namedtuple('City' , 'name population')

c1 = City('Bratislava', 432000)
c2 = City('Budapest', 1759000)

name, population = c1
print(f'{name}: {population}')

print('----------------------')

print(c2)
print(*c2, sep=': ')

In the example, we unpack our namedtuples.

name, population = c1

Here we unpack the c1 namedtuple into two variables.

print(*c2, sep=': ')

Here we unpack the c2 namedtuple with the * operator into print function arguments, which are joined with the given separator into the final output.

$ ./unpacking.py 
Bratislava: 432000
----------------------
City(name='Budapest', population=1759000)
Budapest: 1759000

This is the output.

unpacking2.py
#!/usr/bin/env python3

from collections import namedtuple


City = namedtuple('City' , 'name population')

d = { 'name': 'Bratislava', 'population': 432000}

c = City(**d)
print(c)

With the ** operator, we can unpack a dictionary into arguments of a namedtuple.

Python namedtuple subclassing

Since namedtuples are built on top of regular classes, we can add functionality to them.

subclassing.py
#!/usr/bin/env python3

from collections import namedtuple
from math import sqrt

class Point(namedtuple('Point', 'x y')):

    __slots__ = ()

    @property
    def hypot(self):
        return sqrt((self.x ** 2 + self.y ** 2))

    def __str__(self):
        return f'Point: x={self.x}  y={self.y}  hypot={self.hypot}'


p = Point(5, 5)
print(p.hypot)
print(p)

We have a Point namedtuple. We add the hypot property to it.

$ ./subclassing.py 
7.0710678118654755
Point: x=5  y=5  hypot=7.0710678118654755

This is the output.

Python typing.NamedTuple

Since Python 3.6, we can use the typing.NamedTuple to create a namedtuple.

named_tuple.py
#!/usr/bin/env python3

from typing import NamedTuple


class City(NamedTuple):
    name: str
    population: int


c1 = City('Bratislava', 432000)
c2 = City('Budapest', 1759000)

print(c1)
print(c2)

In the example, we have a City class that inherits from the typing.NamedTuple. The attributes have typehints.

Python namedtuple defaults

The defaults parameter can be used to provide default values to fields.

defaults.py
#!/usr/bin/env python3

from collections import namedtuple
from math import sqrt

class Point(namedtuple('Point', 'x y', defaults=[1, 1])):

    __slots__ = ()

    @property
    def hypot(self):
        return sqrt((self.x ** 2 + self.y ** 2))

    def __str__(self):
        return f'Point: x={self.x}  y={self.y}  hypot={self.hypot}'


p1 = Point(5, 5)
print(p1)

p2 = Point()
print(p2)

The default value for x and y is 1.

$ ./defaults.py 
Point: x=5  y=5  hypot=7.0710678118654755
Point: x=1  y=1  hypot=1.4142135623730951

This is the output.

Python namedtuple helpers

Python provides several helper methods for a namedtuple.

helpers.py
#!/usr/bin/env python3

from typing import NamedTuple


class Point(NamedTuple):

    x: int = 1
    y: int = 1


p = Point(5, 5)

print(p._fields)
print(p._field_defaults)
print(p._asdict())

The _fields is a tuple of strings listing the field names. The _field_defaults is a dictionary mapping field names to default values. The _asdict method returns a new ordered dictionary, which maps field names to their corresponding values.

$ ./helpers.py 
('x', 'y')
{'x': 1, 'y': 1}
OrderedDict([('x', 5), ('y', 5)])

This is the output.

Python namedtuple - serialize to JSON

The _asdict() method can be used to serialize namedtuples into JSON format.

json_output.py
#!/usr/bin/env python3

from typing import NamedTuple
import json


class City(NamedTuple):
    name: str
    population: int


c1 = City('Bratislava', 432000)
c2 = City('Budapest', 1759000)
c3 = City('Prague', 1280000)
c4 = City('Warsaw', 1748000)

cities = [c1, c2, c3, c4]

print(json.dumps(c1._asdict()))

json_string = json.dumps([city._asdict() for city in cities])
print(json_string)

With the help of the json.dumps method, we serialize a single city and a list of cities.

$ ./json_output.py 
{"name": "Bratislava", "population": 432000}
[{"name": "Bratislava", "population": 432000}, {"name": "Budapest", "population": 1759000}, 
{"name": "Prague", "population": 1280000}, {"name": "Warsaw", "population": 1748000}]

This is the output.

Python namedtuple sort

In the following example, we sort a list of namedtuples.

sorting.py
#!/usr/bin/env python3

from typing import NamedTuple


class City(NamedTuple):
    id: int
    name: str
    population: int


c1 = City(1, 'Bratislava', 432000)
c2 = City(2, 'Budapest', 1759000)
c3 = City(3, 'Prague', 1280000)
c4 = City(4, 'Warsaw', 1748000)
c5 = City(5, 'Los Angeles', 3971000)
c6 = City(6, 'Edinburgh', 464000)
c7 = City(7, 'Berlin', 3671000)

cities = [c1, c2, c3, c4, c5, c6, c7]

cities.sort(key=lambda e: e.name)

for city in cities:
    print(city)

With the help of the sort method and the lambda function, we sort cities by their name.

$ ./sorting.py 
City(id=7, name='Berlin', population=3671000)
City(id=1, name='Bratislava', population=432000)
City(id=2, name='Budapest', population=1759000)
City(id=6, name='Edinburgh', population=464000)
City(id=5, name='Los Angeles', population=3971000)
City(id=3, name='Prague', population=1280000)
City(id=4, name='Warsaw', population=1748000)

The cities are sorted by their names in ascending order.

Python namedtuple _make helper

The _make is method that makes a new instance of a namedtuple from an existing sequence or iterable.

making.py
#!/usr/bin/env python3

from collections import namedtuple


City = namedtuple('City' , 'name population')

c1 = City._make(('Bratislava', 432000))
c2 = City._make(('Budapest', 1759000))

print(c1)
print(c2)

The example creates City namedtuples from tuples with the help of the _make method.

Python namedtuple - read CSV data

Python namedtuples are helpful when we read CSV data.

cities.csv
Bratislava, 432000
Budapest, 1759000
Prague, 1280000
Warsaw, 1748000
Los Angeles, 3971000
New York, 8550000
Edinburgh, 464000
Berlin, 3671000

We have this CSV file.

read_csv.py
#!/usr/bin/env python3

from collections import namedtuple
import csv


City = namedtuple('City' , 'name population')

f = open('cities.csv', 'r')

with f:

    reader = csv.reader(f)
    
    for city in map(City._make, reader):
        print(city)

We use the map and the _make functions to create clean code.

$ ./read_csv.py 
City(name='Bratislava', population=' 432000')
City(name='Budapest', population=' 1759000')
City(name='Prague', population=' 1280000')
City(name='Warsaw', population=' 1748000')
City(name='Los Angeles', population=' 3971000')
City(name='New York', population=' 8550000')
City(name='Edinburgh', population=' 464000')
City(name='Berlin', population=' 3671000')

This is the output.

Python namedtuple - read SQLite database

In the following example, we use a namedtuple to read data from SQLite database.

cities.sql
DROP TABLE IF EXISTS cities;
CREATE TABLE cities(id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT, 
  population INTEGER);

INSERT INTO cities(name, population) VALUES('Bratislava', 432000);
INSERT INTO cities(name, population) VALUES('Budapest', 1759000);
INSERT INTO cities(name, population) VALUES('Prague', 1280000);
INSERT INTO cities(name, population) VALUES('Warsaw', 1748000);
INSERT INTO cities(name, population) VALUES('Los Angeles', 3971000);
INSERT INTO cities(name, population) VALUES('New York', 8550000);
INSERT INTO cities(name, population) VALUES('Edinburgh', 464000);
INSERT INTO cities(name, population) VALUES('Berlin', 3671000);

These are SQL statements to create the cities table.

$ sqlite3 ydb.db
SQLite version 3.31.1 2020-01-27 19:55:54
Enter ".help" for usage hints.
sqlite> .read cities.sql

With the sqlite3 command line tool, we generate the SQLite database and the cities table.

read_sql.py
#!/usr/bin/env python3

from typing import NamedTuple
import sqlite3 as sqlite


class City(NamedTuple):
    
    id: int
    name: str
    population: int


con = sqlite.connect('ydb.db')

with con:

    cur = con.cursor()

    cur.execute('SELECT * FROM cities')
    
    for city in map(City._make, cur.fetchall()):
        print(city)

We read all data from the cities table and transform each table row into a City namedtuple.

In this tutorial, we have worked with Python namedtuple.

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