AutoEnum: Delightfully Simple Python Enums 🎯

A fuzzy-matched, Pydantic-compatible enum library for Python 3.

What's an AutoEnum?

AutoEnum is a replacement for Python's Enum, which has many problems.

The standard way of defining enums is not Pythonic:

from enum import Enum
class Animal(Enum):
    Antelope = 1
    Bandicoot = 2
    Cat = 3
    Dog = 4

A while ago, Python 3 introduced the auto function to automatically assign values, which was an improvement:

from enum import Enum, auto
class Animal(Enum):
    Antelope = auto()
    Bandicoot = auto()
    Cat = auto()
    Dog = auto()

But inbuilt Python enums still have a lot of problems:

• Case-sensitivity
• No fuzzy-matching
• No support for aliases
• Incompatible str() and repr() outputs
• Unable to convert to JSON.
• No Pydantic compatibility

The autoenum library fixes all these problems. It is a single-file library with behavior very similar to auto() usage above:

from autoenum import AutoEnum, auto
class Animal(AutoEnum):   ## Only the superclass is changed.
    Antelope = auto()
    Bandicoot = auto()
    Cat = auto()
    Dog = auto()

AutoEnum allows you to do things like this:

>>> Animal.Antelope   ## Default usage, recommended in main codebase
Antelope

>>> Animal('Antelope')  ## Fuzzy-match a string entered by a user
Antelope

>>> Animal('     antElope_ ')  ## Spacing, casing and underscores are handled
Antelope

>>> Animal('Jaguar')  ## Throws an error 
ValueError: Could not find enum with value Jaguar; available values are: [Antelope, Bandicoot, Cat, Dog].

>>> Animal.from_str('Jaguar', raise_error=False)  ## The error can be suppressed
None

Accessing an enum value directly, e.g. Animal.Antelope, carries the same overhead as a normal enum access (~50 nanoseconds). Fuzzy matching runs very fast (~175 ns/lookup i.e. ~5,700,000 lookups/second on a 26-item enum using the default fuzzy-matching algorithm). AutoEnum has been used for years in production systems, and has only gotten faster over time.

Features

Consider an enum of 26 US cities with their common aliases:

from autoenum import AutoEnum, auto, alias
class City(AutoEnum):
    Atlanta = auto()
    Boston = auto()
    Chicago = auto()
    Denver = auto()
    El_Paso = auto()
    Fresno = auto()
    Greensboro = auto()
    Houston = auto()
    Indianapolis = auto()
    Jacksonville = auto()
    Kansas_City = auto()
    Los_Angeles = auto()
    Miami = auto()
    New_York_City = alias('New York', 'NYC')
    Orlando = auto()
    Philadelphia = auto()
    Quincy = auto()
    Reno = auto()
    San_Francisco = auto()
    Tucson = auto()
    Union_City = auto()
    Virginia_Beach = auto()
    Washington = alias('Washington D.C.')
    Xenia = auto()
    Yonkers = auto()
    Zion = auto()

Construct Enum from string

In regular Python enums, its impossible to directly create the enum value from a string: you have to match it with every possible value. With an AutoEnum, you can just do:

>>> City('Boston')
Boston

Which functions the same as:

>>> City.Boston
Boston

is and ==

Both is and == can be used, as with current Enums:

>>> City.Los_Angeles is City('Los_Angeles')
True
>>> City.Los_Angeles == City('Los_Angeles')
True

In Python code (if statements etc), it is preferred to match using is:

city = ... ## From previous code
if city is City.Boston:
    ...

Robust naming conventions

Different teams use different naming-conventions for their enums:

• Some use NamesLikeThis (PascalCase; class-name convention)
• Others use NAMES_LIKE_THIS (Java and C++ enum convention)
• Some even use namesLikeThis (camelCase; JS convention)
• I somtimes use Names_Like_this for proper nouns (for example Los_Angeles above). Its difficult to remember which convention is followed by each project, and sometimes in your own code.

AutoEnum accepts all the above conventions and gives you the enum value you need:

>>> City.Los_Angeles == City('Los_Angeles') == City('LosAngeles') == City('LOS_ANGELES') == City('losAngeles')
True

Fuzzy-matching

In most applications, you use an enum to match as user-entered input. Thus, the string value from which you construct and enum is likely to have minor typos like spacing, underscores, hyphens, or extra periods. In inbuilt enums, cleaning of the input must be done separately for every enum. With AutoEnum, forget about cleaning. So long as you have the same alphabets in the same order, it will work.

>>> City.Los_Angeles == City('Los Angeles') == City('Los__Angeles') == City(' _Los_Angeles   ') == City('LOS-Angeles')
True

However, typos such missing chars, extra or modified chars are not permitted, as they can change the meaning of the enum (for example, Face vs Fate vs Fat).

>>> City('Lozz Angeles')
ValueError: Could not find enum with value Lozz Angeles; available values are: [Atlanta, Boston, Chicago, Denver, El_Paso, Fresno, Greensboro, Houston, Indianapolis, Jacksonville, Kansas_City, Los_Angeles, Miami, New_York_City, Orlando, Philadelphia, Quincy, Reno, San_Francisco, Tucson, Union_City, Virginia_Beach, Washington, Xenia, Yonkers, Zion].

By default, the following characters are ignored: (' ', '-', '_', '.', ':', ';', ',')

You can also write your own fuzzy-matching logic by overriding _normalize:

class Animal(AutoEnum):
    Antelope = auto()
    Bandicoot = auto()
    Cat = alias('Feline')
    Dog = auto()

    @classmethod
    def _normalize(cls, x: str) -> str:
      return str(x)  ## Exact matching

Aliasing

Python enums, contrary to belief, do support aliasing:

from enum import Enum
class Animal(Enum):
    Antelope = 1
    Bandicoot = 2
    Cat = 3
    Feline = 3  ## Same number as before indicates an alias
    Dog = 4

It is not possible to mix the auto keyword with this style of aliasing in Python enums.

In autoenum however, the alias function allows you to create aliases for an enum value:

from autoenum import AutoEnum, auto, alias
class Animal(AutoEnum):
    Antelope = auto()
    Bandicoot = auto()
    Cat = alias('Feline')
    Dog = auto()

This lets you do:

>>> Animal('Cat')
Cat

>>> Animal('Feline')
Cat

In code which consumes the alias, you should use Animal.Cat everywhere.

If you are parsing addresses, it is pretty common to see multiple variants of city names, and aliases become very useful:

>>> City('Washington') == City('Washington DC') == City('Washington D.C.')
Washington

JSON compatibility

Regular enums cannot be converted to JSON:

import json
from enum import Enum
class Animal(Enum):
    Antelope = 1
    Bandicoot = 2
    Cat = 3
    Dog = 4

If you run json.dumps, it will throw an error:

>>> json.dumps([Animal.Cat, Animal.Dog])
TypeError: Object of type Animal is not JSON serializable

The standard way to get around this is to convert all values to strings:

>>> json.dumps([str(a) for a in [Animal.Cat, Animal.Dog]])
'["Animal.Cat", "Animal.Dog"]'

...but after de-jsonifying it, you get strings, not enums. You cannot convert these back into enums easily:

>>> animals: List[str] = json.loads(json.dumps([str(a) for a in [Animal.Cat, Animal.Dog]]))
>>> animals
['Animal.Cat', 'Animal.Dog']
>>> animals: List[Animal] = [Animal(a) for a in animals]
ValueError: 'Animal.Cat' is not a valid Animal

AutoEnum fixes all these problems; it is natively json-encodable and structures can be converted using AutoEnum.convert_values(...):

import json
from autoenum import AutoEnum, auto
class Animal(AutoEnum):
    Antelope = auto()
    Bandicoot = auto()
    Cat = auto()
    Dog = auto()

>>> json.dumps([Animal.Cat, Animal.Dog])
'["Cat", "Dog"]'
>>> animals: List[Animal] = Animal.convert_values(json.loads(json.dumps([Animal.Cat, Animal.Dog])))
>>> animals
[Cat, Dog]
>>> assert isinstance(animals[0], Animal) and isinstance(animals[1], Animal)

Pydantic compatibility

You can use AutoEnum directly in Pydantic BaseModels alongside other Pydantic type-verification:

from pydantic import BaseModel, conint, confloat, constr
class Company(BaseModel):
    name: constr(min_length=1)
    headquarters: City   ## AutoEnum 
    num_employees: conint(ge=1)

When creating such a Pydantic object, you can pass either the enum value, or a string which is fuzzy-matched:

>>> netflix = Company(name='Netflix', headquarters='Los Angeles', num_employees=12_000)
>>> netflix.json()
{"name": "Netflix", "headquarters": "Los_Angeles", "num_employees": 12000}
>>> if City(json.loads(netflix.json())['headquarters']) is City.Los_Angeles:
...     print(f'Headquarters is in "{City.Los_Angeles}"')
Headquarters is in "Los_Angeles"

Unified str() and repr()

Inbuilt Python Enums have a fairly gaudy string representation:

>>> str(City.Boston)
'City.Boston'
>>> repr(City.Boston)
'<City.Boston: 2>'  ## why?

It's usually clear from context that Boston belongs to the City enum, we don't need City.Boston. 2 is also conveying no information here.

AutoEnums are printed and represented in a minmial, uniform fashion:

>>> str(City.Boston)
'Boston'
>>> repr(City.Boston)
'Boston'

Dynamic Enum creation

You don't need to define an enum subclass at all! You can use make_autoenum to create one dynamically. All strings are converted to valid Python identifiers.

from autoenum import make_autoenum
Color = make_autoenum("Color", ["RED", "Green   as   grass", "Blue33", "Yellow!!!"])
>>> list(Color)
[Color.Red, Color.Green_As_Grass, Color.Blue33, Color.Yellow]
>>> Color("Red")
Color.Red
>>> Color("Green as grass")
Color.Green_As_Grass
>>> Color("blue33")
Color.Blue33
>>> Color("Yellow")
Color.Yellow