Dataclass vs namedtuple in Python

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Tips and Traps¶

Prefer Dataclass to namedtuple for many reasons.
- A namedtuple is immutable while a dataclass can be both mutable (frozen=False which is the default) or immutable (frozen=True).
However, namedtuple does have one advantage over dataclass. Members of a namedtuple is assible both via the dot operator and index. In situations where both dot accessing and index accessing of members is required, a namedtuple comes handy. For examples, a list of namedtuple objects can be used as the data for creating a pandas DataFrame but not a list of dataclass objects.

dataclass - mutable¶

from dataclasses import dataclass


@dataclass
class Person:
    name: str
    age: int = 10

Person("Ben")

Person(name='Ben', age=10)

p = Person("Ben", 34)
p

Person(name='Ben', age=34)

str(p)

"Person(name='Ben', age=34)"

dataclass - immutable¶

Attributes of the class Person is mutable since frozen=False (default).

p.age = 20
p

Person(name='Ben', age=20)

@dataclass(frozen=True)
class PersonImmutable:
    name: str
    age: int = 10

PersonImmutable("Ben")

PersonImmutable(name='Ben', age=10)

p = PersonImmutable("Ben", 30)
p

PersonImmutable(name='Ben', age=30)

Attribute of PersonImmutable is immutable since frozen=True.

p.age = 20


FrozenInstanceErrorTraceback (most recent call last)
<ipython-input-22-b29e99bf1482> in <module>
----> 1 p.age = 20

<string> in __setattr__(self, name, value)

FrozenInstanceError: cannot assign to field 'age'

namedtuple¶

from collections import namedtuple

PersonNT = namedtuple("PersonNT", ["name", "age"])

p = PersonNT("Ben Du", 30)
p

PersonNT(name='Ben Du', age=30)

p[0]

'Ben Du'

p[1]

30

Objects of namedtuple as pandas DataFrame Data¶

import pandas as pd

pd.DataFrame(data=[p])

References¶

https://docs.python.org/3/library/collections.html#collections.namedtuple

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