How Enums in Python Improve Code Readability

The Readability Struggle is Real

We‘ve all seen ugly, confusing code that makes our eyes glaze over. Complex code littered with numbers and disconnected constants can be difficult to parse. Readability issues lead to higher defects – a study by MIT found that over 50% of bugs are caused by poor readability and lack of documentation.

By explicitly defining valid values and abstracting away magic numbers, enums produce self-documenting code that clearly communicates developer intent. As an experienced coder, I firmly believe adopting enums leads to more readable and maintainable programs.

So What Exactly Are Enums?

Enumerations or "enums" are a data type that defines a fixed set of constant values called "members". By restricting a variable to only these member values, enums create self-documenting, safer code. An enum definition looks like:

from enum import Enum

class Fruit(Enum): APPLE = 1 BANANA = 2 ORANGE = 3

Now the Fruit type can only be an APPLE, BANANA, or ORANGE. Some key advantages over regular constants:

  • Self-documenting – no magic numbers
  • Type safety – can only be valid enum values
  • Distinct – different enums share no values
  • Extensible – can add more members later

Enums shine when representing a fixed group of values. Common use cases include:

  • Days, months
  • HTTP status codes
  • Application state machines
  • Error or event codes

Let‘s compare an enum approach to using plain constants:

Enums Constants
from enum import Enum

class Fruit(Enum):
    APPLE = 1
    BANANA = 2

APPLE = 1 

Self-documenting, descriptive Opaque magic numbers

The Fruit enum makes the code more explicit and readable. Next let‘s explore how to define enums in Python…

Creating Enums in Python

Python supports both class-based and functional enum creation:

The Enum Class

To create an enum class:

  1. Import Enum class
  2. Make subclass of Enum
  3. Declare members as class variables

For example:

from enum import Enum

class Fruit(Enum):
    APPLE = 1
    BANANA = 2


Access members via class attributes.

The Functional API

Alternatively, use the functional API:

from enum import Enum

Fruit = Enum(‘Fruit‘, [‘APPLE‘, ‘BANANA‘])  

This functional form is concise but less flexible.

Initial Values

We can choose member values manually or autogenerate them:

# Manual values
class Fruit(Enum):
    APPLE = 1
    BANANA = 2

# Autovalues    
class Fruit(Enum): 

Autovalues increment starting from 1.


Initialize enums from tuples or dictionaries:

# dict 
Fruit = Enum(‘Fruit‘, {‘APPLE‘: 1, ‘BANANA‘: 2})

# tuples
members = [
    (‘APPLE‘, 1),
    (‘BANANA‘: 2)

Fruit = Enum(‘Fruit‘, members) 

This helps automate creation.

Methods and Attributes

We can also attach custom methods and properties:

from enum import Enum

class Fruit(Enum):
    APPLE = 1
    BANANA = 2

    def color(self):
        if self == Fruit.APPLE:
            return ‘Red‘
            return ‘Yellow‘

print(Fruit.APPLE.color) # ‘Red‘           

Overall, enums provide flexible value generation, initialization, and customization.

Using Enums Effectively

Let‘s go over effective usage – accessing members, getting values, comparisons, and leveraging IDE typechecking.

Accessing Members

We can access members via:


This is like other Python enums.

Values and Names

Enums expose convenience properties:

memb = Fruit.APPLE # ‘APPLE‘
memb.value # 1

So we can directly get names and values.

Comparing Members

To compare members, use is or == rather than value. This checks identity:

fruit = Fruit.BANANA  
if fruit is Fruit.BANANA:
   print(‘We have bananas!‘)  

Using identity comparisons avoids bugs.

Membership and Existence

We can also check membership and existence in the enum‘s namespace:

print(Fruit.BANANA in Fruit) # True
print(‘PEAR‘ in Fruit) # False

This provides safer value checking.

IDE Usage

Modern Python IDEs can leverage type information from enums to provide autocomplete, typechecking, and documentation:

Enums improve editor integration and dev productivity through mature type support.

Overall, Python‘s enums allow ergonomic, type-safe usage – a huge boon for readable code.

Now let‘s look at some more advanced enum techniques available…

Advanced Python Enum Techniques

Python‘s enums provide a robust, full-featured implementation. Some neat tricks include:

Unique Members

The @unique decorator enforces unique values:

from enum import unique, Enum

class Fruit(Enum):
    APPLE = 1 
    APPLE = 2 # Error!

This prevents logical bugs from duplicate values.

Bit Flags

The Flag and IntFlag enum groups work well for bitmasks and flags:

from enum import IntFlag  

class Perms(IntFlag):
    READ = 1 
    WRITE = 2
    EXECUTE = 4

p = Perms.READ | Perms.WRITE  

We can bitwise-OR the members together easily.


Enum pickle serialization leverages the member names:

import pickle
serialized = pickle.dumps(Fruit.APPLE) 

So serialization works out of the box.

Overall, Python‘s enum support is quite full-featured – these advanced techniques further improve readability and correctess.

Closing Thoughts

I hope this post has convinced you to start using enums! Explicit is better than implicit, and enums force us to be clear. By defining exact value sets, enums act as domain-specific types that document and typecheck themselves.

Even simple enums help limit bugs and cognitive load. I suggest starting by converting existing constants and then gradually adopting more advanced enum usage. Your fellow devs will thank you for the increased readability!