Multi tool use

What are “named tuples” in Python?

Reading the changes in Python 3.1, I found something... unexpected:

The sys.version_info tuple is now a named tuple:

I never heard about named tuples before, and I thought elements could either be indexed by numbers (like in tuples and lists) or by keys (like in dicts). I never expected they could be indexed both ways.

Thus, my questions are:

10 Answers
10

Named tuples are basically easy-to-create, lightweight object types. Named tuple instances can be referenced using object-like variable dereferencing or the standard tuple syntax. They can be used similarly to struct or other common record types, except that they are immutable. They were added in Python 2.6 and Python 3.0, although there is a recipe for implementation in Python 2.4.

struct

For example, it is common to represent a point as a tuple (x, y). This leads to code like the following:

(x, y)

pt1 = (1.0, 5.0)
pt2 = (2.5, 1.5)

from math import sqrt
line_length = sqrt((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)


Using a named tuple it becomes more readable:

from collections import namedtuple
Point = namedtuple('Point', 'x y')
pt1 = Point(1.0, 5.0)
pt2 = Point(2.5, 1.5)

from math import sqrt
line_length = sqrt((pt1.x-pt2.x)**2 + (pt1.y-pt2.y)**2)


However, named tuples are still backwards compatible with normal tuples, so the following will still work:

Point = namedtuple('Point', 'x y')
pt1 = Point(1.0, 5.0)
pt2 = Point(2.5, 1.5)

from math import sqrt
# use index referencing
line_length = sqrt((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)
# use tuple unpacking
x1, y1 = pt1


Thus, you should use named tuples instead of tuples anywhere you think object notation will make your code more pythonic and more easily readable. I personally have started using them to represent very simple value types, particularly when passing them as parameters to functions. It makes the functions more readable, without seeing the context of the tuple packing.

Furthermore, you can also replace ordinary immutable classes that have no functions, only fields with them. You can even use your named tuple types as base classes:

class Point(namedtuple('Point', 'x y')):
[...]


However, as with tuples, attributes in named tuples are immutable:

>>> Point = namedtuple('Point', 'x y')
>>> pt1 = Point(1.0, 5.0)
>>> pt1.x = 2.0
AttributeError: can't set attribute


If you want to be able change the values, you need another type. There is a handy recipe for mutable recordtypes which allow you to set new values to attributes.

>>> from rcdtype import *
>>> Point = recordtype('Point', 'x y')
>>> pt1 = Point(1.0, 5.0)
>>> pt1 = Point(1.0, 5.0)
>>> pt1.x = 2.0
>>> print(pt1[0])
2.0


I am not aware of any form of "named list" that lets you add new fields, however. You may just want to use a dictionary in this situation. Named tuples can be converted to dictionaries using pt1._asdict() which returns {'x': 1.0, 'y': 5.0} and can be operated upon with all the usual dictionary functions.

pt1._asdict()

{'x': 1.0, 'y': 5.0}

As already noted, you should check the documentation for more information from which these examples were constructed.

__slots__

namedtuple is a factory function for making a tuple class. With that class we can create tuples that are callable by name also.

import collections

#Create a namedtuple class with names "a" "b" "c"
Row = collections.namedtuple("Row", ["a", "b", "c"], verbose=False, rename=False)

row = Row(a=1,b=2,c=3) #Make a namedtuple from the Row class we created

print row #Prints: Row(a=1, b=2, c=3)
print row.a #Prints: 1
print row[0] #Prints: 1

row = Row._make([2, 3, 4]) #Make a namedtuple from a list of values

print row #Prints: Row(a=2, b=3, c=4)


A named tuple is a tuple.

It does everything a tuple can.

But it's more than just a tuple.

It's a specific subclass of a tuple that is programmatically created to your specification, with named fields and a fixed length.

This, for example, creates a subclass of tuple, and aside from being of fixed length (in this case, three), it can be used everywhere a tuple is used without breaking. This is known as Liskov substitutability:

>>> from collections import namedtuple
>>> class_name = 'ANamedTuple'
>>> fields = 'foo bar baz'
>>> ANamedTuple = namedtuple(class_name, fields)


This instantiates it:

>>> ant = ANamedTuple(1, 'bar', )


We can inspect it and use its attributes:

>>> ant
ANamedTuple(foo=1, bar='bar', baz=)
>>> ant.foo
1
>>> ant.bar
'bar'
>>> ant.baz.append('anything')
>>> ant.baz
['anything']


To understand named tuples, you first need to know what a tuple is. A tuple is essentially an immutable (can't be changed in-place in memory) list.

Here's how you might use a regular tuple:

>>> student_tuple = 'Lisa', 'Simpson', 'A'
>>> student_tuple
('Lisa', 'Simpson', 'A')
>>> student_tuple[0]
'Lisa'
>>> student_tuple[1]
'Simpson'
>>> student_tuple[2]
'A'


You can expand a tuple with iterable unpacking:

>>> first, last, grade = student_tuple
>>> first
'Lisa'
>>> last
'Simpson'
>>> grade
'A'


Named tuples are tuples that allow their elements to be accessed by name instead of just index!

You make a namedtuple like this:

>>> from collections import namedtuple
>>> Student = namedtuple('Student', ['first', 'last', 'grade'])


You can also use a single string with the names separated by spaces, a slightly more readable use of the API:

>>> Student = namedtuple('Student', 'first last grade')


You can do everything tuples can do (see above) as well as do the following:

>>> named_student_tuple = Student('Lisa', 'Simpson', 'A')
>>> named_student_tuple.first
'Lisa'
>>> named_student_tuple.last
'Simpson'
>>> named_student_tuple.grade
'A'
>>> named_student_tuple._asdict()
OrderedDict([('first', 'Lisa'), ('last', 'Simpson'), ('grade', 'A')])
>>> vars(named_student_tuple)
OrderedDict([('first', 'Lisa'), ('last', 'Simpson'), ('grade', 'A')])
>>> new_named_student_tuple = named_student_tuple._replace(first='Bart', grade='C')
>>> new_named_student_tuple
Student(first='Bart', last='Simpson', grade='C')


A commenter asked:

In a large script or programme, where does one usually define a named tuple?

The types you create with namedtuple are basically classes you can create with easy shorthand. Treat them like classes. Define them on the module level, so that pickle and other users can find them.

namedtuple

The working example, on the global module level:

>>> from collections import namedtuple
>>> NT = namedtuple('NT', 'foo bar')
>>> nt = NT('foo', 'bar')
>>> import pickle
>>> pickle.loads(pickle.dumps(nt))
NT(foo='foo', bar='bar')


And this demonstrates the failure to lookup the definition:

>>> def foo():
... LocalNT = namedtuple('LocalNT', 'foo bar')
... return LocalNT('foo', 'bar')
...
>>> pickle.loads(pickle.dumps(foo()))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
_pickle.PicklingError: Can't pickle <class '__main__.LocalNT'>: attribute lookup LocalNT on __main__ failed


Use them when it improves your code to have the semantics of tuple elements expressed in your code. You can use them instead of an object if you would otherwise use an object with unchanging data attributes and no functionality. You can also subclass them to add functionality, for example:

class Point(namedtuple('Point', 'x y')):
"""adding functionality to a named tuple"""
__slots__ = ()
@property
def hypot(self):
return (self.x ** 2 + self.y ** 2) ** 0.5
def __str__(self):
return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)


It would probably be a regression to switch from using named tuples to tuples. The upfront design decision centers around whether the cost from the extra code involved is worth the improved readability when the tuple is used.

There is no extra memory used by named tuples versus tuples.

You're looking for either a slotted object that implements all of the functionality of a statically sized list or a subclassed list that works like a named tuple (and that somehow blocks the list from changing in size.)

A now expanded, and perhaps even Liskov substitutable, example of the first:

from collections import Sequence

class MutableTuple(Sequence):
"""Abstract Base Class for objects that work like mutable
namedtuples. Subclass and define your named fields with
__slots__ and away you go.
"""
__slots__ = ()
def __init__(self, *args):
for slot, arg in zip(self.__slots__, args):
setattr(self, slot, arg)
def __repr__(self):
return type(self).__name__ + repr(tuple(self))
# more direct __iter__ than Sequence's
def __iter__(self):
for name in self.__slots__:
yield getattr(self, name)
# Sequence requires __getitem__ & __len__:
def __getitem__(self, index):
return getattr(self, self.__slots__[index])
def __len__(self):
return len(self.__slots__)


And to use, just subclass and define __slots__:

__slots__

class Student(MutableTuple):
__slots__ = 'first', 'last', 'grade' # customize


>>> student = Student('Lisa', 'Simpson', 'A')
>>> student
Student('Lisa', 'Simpson', 'A')
>>> first, last, grade = student
>>> first
'Lisa'
>>> last
'Simpson'
>>> grade
'A'
>>> student[0]
'Lisa'
>>> student[2]
'A'
>>> len(student)
3
>>> 'Lisa' in student
True
>>> 'Bart' in student
False
>>> student.first = 'Bart'
>>> for i in student: print(i)
...
Bart
Simpson
A


namedtuples are a great feature, they are perfect container for data. When you have to "store" data you would use tuples or dictionaries, like:

user = dict(name="John", age=20)


or:

user = ("John", 20)


The dictionary approach is overwhelming, since dict are mutable and slower than tuples. On the other hand, the tuples are immutable and lightweight but lack readability for a great number of entries in the data fields.

namedtuples are the perfect compromise for the two approaches, the have great readability, lightweightness and immutability (plus they are polymorphic!).

ntuple.foo

ntuple[1]

named tuples allow backward compatibility with code that checks for the version like this

>>> sys.version_info[0:2]
(3, 1)


while allowing future code to be more explicit by using this syntax

>>> sys.version_info.major
3
>>> sys.version_info.minor
1


is one of the easiest ways to clean up your code and make it more readable. It self-documents what is happening in the tuple. Namedtuples instances are just as memory efficient as regular tuples as they do not have per-instance dictionaries, making them faster than dictionaries.

from collections import namedtuple

Color = namedtuple('Color', ['hue', 'saturation', 'luminosity'])

p = Color(170, 0.1, 0.6)
if p.saturation >= 0.5:
print "Whew, that is bright!"
if p.luminosity >= 0.5:
print "Wow, that is light"


Without naming each element in the tuple, it would read like this:

p = (170, 0.1, 0.6)
if p[1] >= 0.5:
print "Whew, that is bright!"
if p[2]>= 0.5:
print "Wow, that is light"


It is so much harder to understand what is going on in the first example. With a namedtuple, each field has a name. And you access it by name rather than position or index. Instead of p[1], we can call it p.saturation. It's easier to understand. And it looks cleaner.

p[1]

Creating an instance of the namedtuple is easier than creating a dictionary.

# dictionary
>>>p = dict(hue = 170, saturation = 0.1, luminosity = 0.6)
>>>p['hue']
170

#nametuple
>>>from collections import namedtuple
>>>Color = namedtuple('Color', ['hue', 'saturation', 'luminosity'])
>>>p = Color(170, 0.1, 0.6)
>>>p.hue
170


p.hue

p['hue']

The syntax

collections.namedtuple(typename, field_names[, verbose=False][, rename=False])


['x', 'y', 'z']

x y z

x, y, z

True

['abc', 'def', 'ghi','abc']

['abc', '_1', 'ghi', '_3']

'def'

'abc'

True

You can still access namedtuples by their position, if you so choose. p[1] == p.saturation. It still unpacks like a regular tuple.

p[1] == p.saturation

All the regular tuple methods are supported. Ex: min(), max(), len(), in, not in, concatenation (+), index, slice, etc. And there are a few additional ones for namedtuple. Note: these all start with an underscore. _replace, _make, _asdict.

_replace

_make

_asdict

_replace
Returns a new instance of the named tuple replacing specified fields with new values.

_replace

The syntax

somenamedtuple._replace(kwargs)


Example

>>>from collections import namedtuple

>>>Color = namedtuple('Color', ['hue', 'saturation', 'luminosity'])
>>>p = Color(170, 0.1, 0.6)

>>>p._replace(hue=87)
Color(87, 0.1, 0.6)

>>>p._replace(hue=87, saturation=0.2)
Color(87, 0.2, 0.6)


Notice: The field names are not in quotes; they are keywords here.
Remember: Tuples are immutable - even if they are namedtuples and have the _replace method. The _replace produces a new instance; it does not modify the original or replace the old value. You can of course save the new result to the variable. p = p._replace(hue=169)

_replace

_replace

new

p = p._replace(hue=169)

_make

_make

Makes a new instance from an existing sequence or iterable.

The syntax

somenamedtuple._make(iterable)


Example

>>>data = (170, 0.1, 0.6)
>>>Color._make(data)
Color(hue=170, saturation=0.1, luminosity=0.6)

>>>Color._make([170, 0.1, 0.6]) #the list is an iterable
Color(hue=170, saturation=0.1, luminosity=0.6)

>>>Color._make((170, 0.1, 0.6)) #the tuple is an iterable
Color(hue=170, saturation=0.1, luminosity=0.6)

>>>Color._make(170, 0.1, 0.6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 15, in _make
TypeError: 'float' object is not callable


What happened with the last one? The item inside the parenthesis should be the iterable. So a list or tuple inside the parenthesis works, but the sequence of values without enclosing as an iterable returns an error.

_asdict

_asdict

Returns a new OrderedDict which maps field names to their corresponding values.

The syntax

somenamedtuple._asdict()


Example

>>>p._asdict()
OrderedDict([('hue', 169), ('saturation', 0.1), ('luminosity', 0.6)])


Reference: https://www.reddit.com/r/Python/comments/38ee9d/intro_to_namedtuple/

There is also named list which is similar to named tuple but mutable
https://pypi.python.org/pypi/namedlist

_

What is namedtuple ?

As the name suggests, namedtuple is a tuple with name. In standard tuple, we access the elements using the index, whereas namedtuple allows user to define name for elements. This is very handy especially processing csv (comma separated value) files and working with complex and large dataset, where the code becomes messy with the use of indices (not so pythonic).

How to use them ?

>>>from collections import namedtuple
>>>saleRecord = namedtuple('saleRecord','shopId saleDate salesAmout totalCustomers')
>>>
>>>
>>>#Assign values to a named tuple
>>>shop11=saleRecord(11,'2015-01-01',2300,150)
>>>shop12=saleRecord(shopId=22,saleDate="2015-01-01",saleAmout=1512,totalCustomers=125)


Reading

>>>#Reading as a namedtuple
>>>print("Shop Id =",shop12.shopId)
12
>>>print("Sale Date=",shop12.saleDate)
2015-01-01
>>>print("Sales Amount =",shop12.salesAmount)
1512
>>>print("Total Customers =",shop12.totalCustomers)
125


Interesting Scenario in CSV Processing :

from csv import reader
from collections import namedtuple

saleRecord = namedtuple('saleRecord','shopId saleDate totalSales totalCustomers')
fileHandle = open("salesRecord.csv","r")
csvFieldsList=csv.reader(fileHandle)
for fieldsList in csvFieldsList:
shopRec = saleRecord._make(fieldsList)
overAllSales += shopRec.totalSales;

print("Total Sales of The Retail Chain =",overAllSales)


In Python inside there is a good use of container called a named tuple, it can be used to create a definition of class and has all the features of the original tuple.

Using named tuple will be directly applied to the default class template to generate a simple class, this method allows a lot of code to improve readability and it is also very convenient when defining a class.

Try this:

collections.namedtuple()


Basically, namedtuples are easy to create, lightweight object types.
They turn tuples into convenient containers for simple tasks.
With namedtuples, you don’t have to use integer indices for accessing members of a tuple.

namedtuples

namedtuples

Examples:

Code 1:

>>> from collections import namedtuple

>>> Point = namedtuple('Point','x,y')

>>> pt1 = Point(1,2)

>>> pt2 = Point(3,4)

>>> dot_product = ( pt1.x * pt2.x ) +( pt1.y * pt2.y )

>>> print dot_product
11


Code 2:

>>> from collections import namedtuple

>>> Car = namedtuple('Car','Price Mileage Colour Class')

>>> xyz = Car(Price = 100000, Mileage = 30, Colour = 'Cyan', Class = 'Y')

>>> print xyz

Car(Price=100000, Mileage=30, Colour='Cyan', Class='Y')
>>> print xyz.Class
Y


Everyone else has already answered it, but I think I still have something else to add.

Namedtuple could be intuitively deemed as a shortcut to define a class.

See a cumbersome and conventional way to define a class .

class

class Duck:
def __init__(self, color, weight):
self.color = color
self.weight = weight
red_duck = Duck('red', '10')

In [50]: red_duck
Out[50]: <__main__.Duck at 0x1068e4e10>
In [51]: red_duck.color
Out[51]: 'red'


As for namedtuple

namedtuple

from collections import namedtuple
Duck = namedtuple('Duck', ['color', 'weight'])
red_duck = Duck('red', '10')

In [54]: red_duck
Out[54]: Duck(color='red', weight='10')
In [55]: red_duck.color
Out[55]: 'red'


red_duck[0]

len(red_duck)

for x in red_duck: print(x)

red_duck[0] = 2

red_duck.foo = 'bar'

dict

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