Skip to content

Understanding Constants in Python

Python Constants: Improve Your Code’s Maintainability

In programming, constants are names that represent values that do not change during a program’s execution. While Python does not have a dedicated syntax for defining constants, they can be implemented using uppercase letters as a naming convention. In this tutorial, you will learn how to properly define constants in Python, identify built-in constants, and utilize constants to improve code readability, reusability, and maintainability. You will also explore different techniques for organizing and managing constants in a project, as well as how to make constants strictly constant in Python.

To fully understand this tutorial, it is recommended to have a basic knowledge of Python variables, functions, modules, packages, and namespaces. Additionally, familiarity with object-oriented programming in Python will be helpful.

Understanding Constants and Variables

Variables and constants are fundamental concepts in computer programming. They are used to manipulate data and work effectively in a logical manner. In most programming languages, including Python, variables and constants are essential components of projects, applications, libraries, and other pieces of code.

What Variables Are

Variables are names that are used to store data and can be assigned different values throughout a program’s execution. They are used to manipulate and process data dynamically.

What Constants Are

Constants, on the other hand, are names that represent values that remain the same throughout a program’s execution. Unlike variables, their values cannot be changed once assigned. In Python, constants are implemented using uppercase letters as a naming convention, although there is no strict enforcement of constant behavior.

Why Use Constants

Using constants in your code provides several benefits:

  • Readability: Constants make it clear that specific values should not be modified, improving code comprehension.
  • Reusability: Constants can be reused in multiple parts of your code, avoiding the need for hard-coded values.
  • Maintainability: By centralizing and organizing constants, code maintenance becomes easier and less error-prone.

When to Use Constants

Constants can be used whenever you have a value that should remain unchanged throughout your program. Some common use cases for constants include:

  • Mathematical and scientific calculations that require predefined numerical values (e.g., pi).
  • Configuration settings that don’t change during runtime (e.g., database credentials).
  • Status codes or error codes that have a specific meaning throughout the application.

Defining Your Own Constants in Python

Although Python does not have a dedicated syntax for defining constants, you can define your own constants by adopting a naming convention. By using uppercase letters, you indicate that a name should be treated as a constant and not be modified.

User-Defined Constants

To define your own constants, you simply assign a value to a variable using uppercase letters for the variable name. Here is an example:


Module-Level Dunder Constants

Another way to define constants is within a module using dunder (double underscore) variables. By convention, these variables should also be written in uppercase letters to indicate that they are constants. Here is an example:
PI = 3.14159
DAY_OF_WEEK = "Monday"

You can then import these module-level constants into other Python scripts for use:
import constants
print(constants.PI) # Output: 3.14159
print(constants.DAY_OF_WEEK) # Output: Monday

Putting Constants Into Action

Now that you understand how to define constants in Python, let’s explore how they can be used to improve code readability, reusability, and maintainability.

Replacing Magic Numbers for Readability

Magic numbers are hard-coded numerical values that are used without any contextual explanation. They make code hard to read and understand. By replacing magic numbers with named constants, you can improve code readability. Consider the following example:

# Bad code with magic number
def calculate_area(radius):
return 3.14159 * radius * radius
# Improved code with named constant
PI = 3.14159
def calculate_area(radius):
return PI * radius * radius

Reusing Objects for Maintainability

In some cases, you may need to reuse the same object multiple times throughout your code. By using a constant, you can ensure that the object remains the same and is not accidentally modified. This helps maintain consistency and minimizes the risk of introducing bugs. Here is an example:

# Bad code with repeated object creations
config = Config()
# Improved code with object reuse using a constant
CONFIG = Config()

Providing Default Argument Values

When defining functions in Python, you may want to provide default values for arguments. By using constants as default argument values, you ensure consistency and express the intended behavior of the function. Here is an example:

# Bad code with magic number as default argument
def multiply(a, b=2):
return a * b
# Improved code with constant as default argument
def multiply(a, b=DEFAULT_MULTIPLIER):
return a * b

Handling Your Constants in a Real-World Project

As your codebase grows larger, it becomes necessary to organize and manage constants effectively. Let’s discuss some techniques for handling constants in a real-world project:

To improve code organization, you can define constants alongside the related code they are used for. This makes it easier for developers to find and understand the purpose of each constant. Here is an example:

# Constants for mathematics calculations
PI = 3.14159
EULER_NUMBER = 2.71828
def calculate_area(radius):
return PI * radius * radius
def calculate_volume(radius):
return (4 3) * PI * radius * radius * radius
def calculate_exponential(base, exponent):
return EULER_NUMBER ** (base * exponent)

Creating a Dedicated Module for Constants

For larger projects, it may be beneficial to centralize all constants in a dedicated module. This module can be imported across different parts of the project, making constants easily accessible. Here is an example:
DATABASE_URL = "mysql:"
# other constants...
import constants
print(constants.CONFIG_FILE_PATH) # Output:
print(constants.DATABASE_URL) # Output: mysql:

Storing Constants in Configuration Files

In some cases, it may be preferable to store constants in configuration files. This allows you to change the values without modifying the code. Python provides several libraries, such as configparser and dotenv, for reading and parsing configuration files.

Handling Constants as Environment Variables

Another approach to managing constants is by storing them as environment variables. This allows you to configure constants dynamically based on the environment in which the code is running. The os.environ dictionary in Python provides access to environment variables.

Exploring Other Constants in Python

In addition to user-defined constants, Python provides several built-in constants that you can use in your code. Let’s explore some of these constants:

Built-in Constants

Python includes built-in constants for representing common values, such as:

  • True and False: Boolean values representing the truth and falsehood.
  • None: A special constant representing the absence of a value.

Internal Dunder Names

Python uses double underscores (dunder) for a variety of internal names. Some commonly used dunder constants include:

  • __name__: Name of the current module.
  • __file__: Path to the current module’s source file.
  • __doc__: Documentation string for a module, function, or class.

Useful String and Math Constants

The Python math and string modules provide additional constants that are useful in mathematical and string operations, such as:

  • math.pi: The mathematical constant pi (approximately 3.14159).
  • string.ascii_lowercase: A string containing all lowercase letters.
  • string.ascii_uppercase: A string containing all uppercase letters.
  • string.digits: A string containing all numeric digits.

Type-Annotating Constants

As with any other variables or functions in Python, you can also add type annotations to constants using Python’s type hinting syntax. This can improve code readability and enable static analysis tools to catch potential type errors. Here is an example:

# Type-annotated constant
PI: float = 3.14159
def calculate_area(radius: float) -> float:
return PI * radius * radius

Defining Strict Constants in Python

While Python does not provide built-in features for enforcing strict constant behavior, you can apply several techniques to achieve this effect.

The .__slots__ Attribute

By defining the .__slots__ attribute in a class, you can restrict the creation of new attributes. This can be useful for creating classes that behave similarly to constants. Here is an example:

class Point:
__slots__ = ("x", "y")
def __init__(self, x, y):
self.x = x
self.y = y
point = Point(1, 2)
point.x = 3 # Allowed
point.z = 4 # Raises AttributeError: 'Point' object has no attribute 'z'

The @property Decorator

By using the @property decorator, you can define read-only properties that behave like constants. These properties cannot be set directly and can only be accessed. Here is an example:

class Circle:
def __init__(self, radius):
self._radius = radius
def radius(self):
return self._radius
def area(self):
return 3.14159 * self._radius * self._radius
circle = Circle(5)
print(circle.radius) # Output: 5
circle.radius = 10 # Raises AttributeError: can't set attribute

The namedtuple() Factory Function

The namedtuple() factory function from the collections module creates classes that can behave like immutable constants. These classes have named fields and cannot be modified after creation. Here is an example:

from collections import namedtuple
Point = namedtuple("Point", ["x", "y"])
point = Point(1, 2)
point.x = 3 # Raises AttributeError: can't set attribute 'x'

The @dataclass Decorator

With the @dataclass decorator from the dataclasses module, you can create classes that have immutable data-like behavior. These classes can be used as constants since their state cannot be changed after creation. Here is an example:

from dataclasses import dataclass
class Rectangle:
width: float
height: float
rectangle = Rectangle(10, 20)
rectangle.width = 30 # Raises dataclasses.FrozenInstanceError: cannot assign to field 'width'

The .__setattr__() Special Method

By overriding the .__setattr__() special method in a class, you can prevent the creation of new attributes. This can be used to create classes that behave similarly to constants. Here is an example:

class Circle:
def __init__(self, radius):
self._radius = radius
def __setattr__(self, name, value):
if name != "_radius":
raise AttributeError("Circle object does not support attribute assignment")
super().__setattr__(name, value)
circle = Circle(5)
circle._radius = 10 # Allowed
circle.radius = 20 # Raises AttributeError: Circle object does not support attribute assignment


Defining and using constants in Python can greatly improve code readability, reusability, and maintainability. By adopting a naming convention for constants and utilizing different techniques for organizing and managing them, you can write more expressive and maintainable code. Remember to use constants when you have values that should remain unchanged throughout your program, such as mathematical calculations or configuration settings.