Python Constants: Improve Your Code's Maintainability

What Variables Are

In math, a variable is defined as a symbol that refers to a value or quantity that can change over time. In programming, a variable is also a symbol or name typically associated with a memory address containing a value, object, or piece of data. Like in math, the content of a programming variable can change during the execution of the code that defines it.

Variables typically have a descriptive name that’s somehow associated with a target value or object. This target value can be of any data type. So, you can use variables to represent numbers, strings, sequences, custom objects, and more.

You can perform two main operations on a variable:

1. Access its value
2. Assign it a new value

In most programming languages, you can access the value associated with a variable by citing the variable’s name in your code. To assign a new value to a given variable, you’ll use an assignment statement, which often consists of the variable’s name, an assignment operator, and the desired value.

In practice, you’ll find many examples of magnitudes, data, and objects that you can represent as variables. A few examples include temperature, speed, time, and length. Other examples of data that you can treat as variables include the number of registered users in a web app, the number of active characters in a video game, and the number of miles covered by a runner.

What Constants Are

Math also has the concept of constants. The term refers to a value or quantity that never changes. In programming, constants refer to names associated with values that never change during a program’s execution.

Just like variables, programming constants consist of two things: a name and an associated value. The name will clearly describe what the constant is all about. The value is the concrete expression of the constant itself.

Like with variables, the value associated with a given constant can be of any of data type. So, you can define integer constants, floating-point constants, character constants, string constants, and more.

After you’ve defined a constant, it’ll only allow you to perform a single operation on it. You can only access the constant’s value but not change it over time. This is different from a variable, which allows you to access its value, but also reassign it.

You’ll use constants to represent values that won’t change. You’ll find lots of these values in your day-to-day programming. A few examples include the speed of light, the number of minutes in an hour, and the name of a project’s root folder.

Why Use Constants

In most programming languages, constants protect you from accidentally changing their values somewhere in the code when you’re coding at two in the morning, causing unexpected and hard-to-debug errors. Constants also help you make your code more readable and maintainable.

Some advantages of using constants instead of using their values directly in your code include:

As you’ve learned in this table, constants are an important concept in programming for good reason. They can make your life more pleasant and your code more reliable, maintainable, and readable. Now, when should you use constants?

When Use Constants

Life, and particularly science, is full of examples of constant values, or values that never change. A few examples include:

• 3.141592653589793: A constant denoted by π, spelled as Pi in English, which represents the ratio of a circle’s circumference to its diameter
• 2.718281828459045: A constant denoted by e and known as Euler’s number, which is closely related to the natural logarithm and compound interest
• 3,600: The number of seconds in one hour, which is considered constant in most applications, even though leap seconds sometimes are added to account for variability in the Earth’s rotation speed
• -273.15: A constant representing absolute zero in degrees Celsius, which is equal to 0 kelvins on the Kelvin temperature scale

All the above examples are constant values that people commonly use in life and science. In programming, you’ll often find yourself dealing with these and many other similar values that you can consider and treat as constants.

In summary, use a constant to represent a quantity, magnitude, object, parameter, or any other piece of data that’s supposed to remain unchanged during its lifetime.

User-Defined Constants

To tell other programmers that a given value should be treated as a constant, you must use a widely accepted naming convention for the constant’s identifier or name. You should write the name in capital letters with underscores separating words, as stated in the Constants section of PEP 8.

Here are a few example of user-defined Python constants:

PI = 3.14
MAX_SPEED = 300
DEFAULT_COLOR = "\033[1;34m"
WIDTH = 20
API_TOKEN = "593086396372"
BASE_URL = "https://api.example.com"
DEFAULT_TIMEOUT = 5
ALLOWED_BUILTINS = ("sum", "max", "min", "abs")
INSTALLED_APPS = [
    "django.contrib.admin",
    "django.contrib.auth",
    "django.contrib.contenttypes",
    ...
]

Note that you’ve created these constants exactly as you’d create variables. You’ve used a descriptive name, the assignment operator (=), and the constant’s specific value.

By using capital letters only, you’re communicating that the current name is intended to be treated as a constant—or more precisely, as a variable that never changes. So, other Python developers will know that and hopefully won’t perform any assignment operation on the variable at hand.

Because Python constants are just variables, both follow similar naming rules, with the only distinction being that constants use uppercase letters only. Following this idea, constants’ names can:

• Be of any length
• Consist of uppercase letters (A–Z)
• Include digits (0–9) but not as their first character
• Use underscore characters (_) to separate words or as their first character

Using uppercase letters makes your constants stand out from your variables. This way, other developers will unambiguously recognize their purpose.

As a general naming recommendation, avoid abbreviated names when defining constants. The purpose of a constant’s name is to clarify the meaning of the constant’s value so that you can reuse it later. This goal demands descriptive names. Avoid using single-letter names, uncommon abbreviations, and generic names like NUMBER or MAGNITUDE.

The recommended practice is to define constants at the top of any .py file right after any import statements. This way, people reading your code will immediately know the constants’ purpose and expected treatment.

Module-Level Dunder Constants

Module-level dunder names are special names that start and end with a double underscore. Some examples include names such as __all__, __author__, and __version__. These names are typically treated as constants in Python projects.

According to Python’s coding style guide, PEP 8, module-level dunder names should appear after the module’s docstring and before any import statement except for __future__ imports.

Here’s a sample module that includes a bunch of dunder names:

# greeting.py

"""This module defines some module-level dunder names."""

from __future__ import barry_as_FLUFL

__all__ = ["greet"]
__author__ = "Real Python"
__version__ = "0.1.0"

import sys

def greet(name="World"):
    print(f"Hello, {name}!")
    print(f"Greetings from version: {__version__}!")
    print(f"Yours, {__author__}!")

In this example, __all__ defines up front the list of names that Python will import when you use the from module import * import construct in your code. In this case, someone importing greeting with a wildcard import will just get the greet() function back. They won’t have access to __author__, __version__, and other names not listed on __all__.

In contrast, __author__ and __version__ have meaning only for the code’s authors and users rather than for the code’s logic itself. These names should be treated as constants since no code should be allowed to change the author or version during the program’s execution.

Note that the greet() function does access the dunder names but doesn’t change them. Here’s how greet() works in practice:

>>> from greeting import *

>>> greet()
Hello, World!
Greetings from version: 0.1.0!
Yours, Real Python!

In general, there are no hard rules that prevent you from defining your own module-level dunder names. However, the Python documentation strongly warns against using dunder names other than those generally accepted and used by the community. The core developers may introduce new dunder names to the language in the future without any warning.

Replacing Magic Numbers for Readability

In programming, the term magic number refers to any number that appears directly in your code without any explanation. It’s a value that comes out of the blue, making your code enigmatic and difficult to understand. Magic numbers also makes programs less readable and more difficult to maintain and update.

For example, say you have the following function:

def compute_net_salary(hours):
    return hours * 35 * (1 - (0.04 + 0.1))

Can you tell up front what the meaning of each number in this computation is? Probably not. The different numbers in this function are magic numbers because you can’t reliably infer their meanings from the numbers themselves.

Check out the following refactored version of this function:

HOURLY_SALARY = 35
SOCIAL_SECURITY_TAX_RATE = 0.04
FEDERAL_TAX_RATE = 0.10

def compute_net_salary(hours):
    return (
        hours
        * HOURLY_SALARY
        * (1 - (SOCIAL_SECURITY_TAX_RATE + FEDERAL_TAX_RATE))
    )

With these minor updates, your function now reads like a charm. You and any other developers reading your code can surely tell what this function does because you’ve replaced the original magic numbers with appropriately named constants. The name of each constant clearly explains its corresponding meaning.

Every time you find yourself using a magic number, take the time to replace it with a constant. This constant’s name must be descriptive and unambiguously explain the meaning of the target magic number. This practice will automatically improve the readability of your code.

Reusing Objects for Maintainability

Another everyday use case of constants is when you have a given value repeatedly appearing in different parts of your code. If you insert the concrete value into the code at every required place, then you’ll be in trouble if you ever need to change the value for any reason. In this situation, you’ll need to change the value in every place.

Changing the target value in multiple places at a time is error-prone. Even if you rely on your editor’s Find and Replace feature, you can leave some unchanged instances of the value, which can lead to unexpected bugs and weird behaviors later.

To prevent these annoying issues, you can replace the value with a properly named constant. This will allow you to set the value once and repeat it in as many locations as needed. If you ever need to change the constant’s value, then you just have to change it in a single place: the constant definition.

For example, say you’re writing a Circle class, and you need methods to compute the circle’s area, perimeter, and so on. After a few coding minutes, you end up with the following class:

# circle.py

class Circle:
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.14 * self.radius**2

    def perimeter(self):
        return 2 * 3.14 * self.radius

    def projected_volume(self):
        return 4/3 * 3.14 * self.radius**3

    def __repr__(self):
        return f"{self.__class__.__name__}(radius={self.radius})"

This example uncovers how the approximate value of Pi (3.14) has been written as a magic number in several methods of your Circle class. Why is this practice a problem? Say you need to increase the precision of Pi. Then you’ll have to manually change the value in at least three different places, which is tedious and error-prone, making your code difficult to maintain.

Using a named constant to store the value of Pi is an excellent approach to solving these issues. Here’s an enhanced version of the above code:

# circle.py

PI = 3.14

class Circle:
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return PI * self.radius**2

    def perimeter(self):
        return 2 * PI * self.radius

    def projected_volume(self):
        return 4/3 * PI * self.radius**3

    def __repr__(self):
        return f"{self.__class__.__name__}(radius={self.radius})"

This version of Circle uses the global constant PI to replace the magic number. This code has several advantages compared to the original code. If you need to increase the precision of Pi, then you just have to update the PI constant’s value at the beginning of the file. This update will immediately reflect on the rest of the code without requiring any additional action on your side.

Another advantage is that now your code is more readable and easier to understand. The constant’s name is self-explanatory and reflects the accepted math terminology.

Declaring a constant once and then reusing it several times, as you did in the above example, represents a significant maintainability improvement. If you ever have to update the constant’s value, then you’ll update it a single place rather than in multiple places, which implies way less effort and error risk.

Providing Default Argument Values

Using named constants to provide default argument values to functions, methods, and classes is another common practice in Python. There are lots of examples of this practice in the Python standard library.

For example, the zipfile module provides tools to create, read, write, append, and list ZIP files. The most relevant class in this module is ZipFile. With ZipFile, you can manipulate your ZIP files efficiently and quickly.

The class constructor of ZipFile takes an argument called compression, which allows you to select among a few available data compression methods. This argument is optional and has ZIP_STORED as its default value, meaning that ZipFile doesn’t compress the input data by default.

In this example, ZIP_STORED is a constant defined in zipfile. The constant holds a numeric value for uncompressed data. You’ll also find other compression methods represented by named constants like ZIP_DEFLATED for the Deflate compression algorithm, for example.

The compression argument in the ZipFile class constructor is a good example of using constants to provide default argument values when you have an argument that can take only a limited number of valid values.

Another example of when constants come in handy as default argument values is when you have several functions with a recurrent argument. Say that you’re developing an application that connects to a local SQLite database. Your app uses the following set of functions to manage the database:

import sqlite3
from sqlite3 import Error

def create_database(db_path):
    # Code to create the initial database goes here...

def create_connection(db_path):
    # Code to create a database connection goes here...

def backup_database(db_path):
    # Code to back up the database goes here...

These functions perform different actions on your SQLite database. Note that all your functions share the db_path argument.

While you’re developing the application, you decide to provide a default database path to your functions so that you can quickly test them. In this case, you can directly use the path as a default value to the db_path argument.

However, it’s better to use a named constant to provide the default database path:

import sqlite3
from sqlite3 import Error

DEFAULT_DB_PATH = "/path/to/database.sqlite"

def create_database(db_path=DEFAULT_DB_PATH):
    # Code to create the initial database goes here...

def create_connection(db_path=DEFAULT_DB_PATH):
    # Code to create a database connection goes here...

def backup_database(db_path=DEFAULT_DB_PATH):
    # Code to back up the database goes here...

This small update enables you to quickly test your app by targeting a sample database during development. It also improves the maintainability of your code because you can reuse this constant in other database-related functions that appear in future versions of your app.

Finally, you’ll find situations in which you want to pass an object with certain behavior to a class, method, or function. This practice is typically known as duck typing and is a fundamental principle in Python. Now say that your code will take care of providing a standard implementation of the required object. If your users want a custom object, then they should provide it themselves.

In this situation, you can use a constant to define the default object and then pass this constant as a default argument value to the target class, method, or function. Check out the following example of a hypothetical FileReader class:

# file_handler.py

from readers import DEFAULT_READER

class FileHandler:
    def __init__(self, file, reader=DEFAULT_READER):
        self._file = file
        self._reader = reader

    def read(self):
        self._reader.read(self._file)

    # FileHandler implementation goes here...

This class provides a way to manipulate different types of files. The .read() method uses the injected reader object to read the input file according to its specific format.

Here’s a toy implementation of a reader class:

# readers.py

class _DefaultReader:
    def read(self, file):
        with open(file, mode="r", encoding="utf-8") as file_obj:
            for line in file_obj:
                print(line)

DEFAULT_READER = _DefaultReader()

The .read() method in this example takes the path to a file, opens it, and prints its content to the screen line by line. This class will play the role of your default reader. The final step is to create a constant, DEFAULT_READER, to store an instance of your default reader. That’s it! You have a class that processes the input files and also a helper class that provides the default reader.

Your users can also code custom readers. For example, they can code readers for CSV and JSON files. Once they’ve written a given reader, they can pass it into the FileHandler class constructor and use the resulting instance to handle files that use the reader’s target file format.

Putting Constants Together With Related Code

The first and maybe most natural strategy to organize and manage your constants is to define them together with the code that uses them. With this approach, you’ll be defining the constants at the top of the module that contains the related code.

For example, say that you’re creating a custom module to perform calculations, and you need to use math constants like Pi, Euler’s number, and a few others. In this case, you can do something like this:

# calculations.py

"""This module implements custom calculations."""

# Imports go here...
import numpy as np

# Constants go here...
PI = 3.141592653589793
EULER_NUMBER = 2.718281828459045
TAU = 6.283185307179586

# Your custom calculations start here...
def circular_land_area(radius):
    return PI * radius**2

def future_value(present_value, interest_rate, years):
    return present_value * EULER_NUMBER ** (interest_rate * years)

# ...

In this example, you define your constants in the same module where the code using them lives.

Putting your constants together with the code that uses them is a quick and appropriate strategy for narrow-scope constants that are only relevant to a single module in a given project. In this case, you probably won’t be using the constants outside the containing module itself.

Creating a Dedicated Module for Constants

Another common strategy for organizing and managing your constants is creating a dedicated module in which to put them all. This strategy is appropriate for constants that are used in many modules and even packages across a given project.

The central idea of this strategy is to create an intuitive and unique namespace for constants. To apply this strategy to your calculations example, you can create a Python package containing the following files:

calc/
├── __init__.py
├── calculations.py
└── constants.py

The __init__.py file will turn the calc/ directory into a Python package. Then you can add the following content to your constants.py file:

# constants.py

"""This module defines project-level constants."""

PI = 3.141592653589793
EULER_NUMBER = 2.718281828459045
TAU = 6.283185307179586

Once you’ve added this code to constants.py, then you can import the module whenever you need to use any of your constants:

# calculations.py

"""This module implements custom calculations."""

# Imports go here...
import numpy as np

from . import constants

# Your custom calculations start here...
def circular_land_area(radius):
    return constants.PI * radius**2

def future_value(present_value, interest_rate, years):
    return present_value * constants.EULER_NUMBER ** (interest_rate * years)

# ...

Note that you import the constants module directly from the calc package using a relative import. Then you use fully qualified names to access any required constants in your calculations. This practice improves your communication of intent. Now it’s completely clear that PI and EULER_NUMBER are constants in your project because of the constants prefix.

To use your calculations module, you can do something like this:

>>> from calc import calculations
>>> calculations.circular_land_area(100)
31415.926535897932

>>> from calc.calculations import circular_land_area
>>> circular_land_area(100)
31415.926535897932

Now your calculations module lives inside the calc package. This means that if you want to use the functions in calculations, then you need to import calculations from calc. You can also import the functions directly by referencing the package and the module like you did in the second example above.

Storing Constants in Configuration Files

Now say that you want to go further when it comes to externalizing the constants of a given project. You may need to keep all your constants out of your project’s source code. To do this, you can use an external configuration file.

Here’s an example of how to move your constants to a configuration file:

; constants.ini

[CONSTANTS]
PI=3.141592653589793
EULER_NUMBER=2.718281828459045
TAU=6.283185307179586

This file uses the INI file format. You can read this type of file using the configparser module from the standard library.

Now get back to calculations.py and update it to look something like the following:

# calculations.py

"""This module implements custom calculations."""

# Imports go here...
from configparser import ConfigParser

import numpy as np

constants = ConfigParser()
constants.read("path/to/constants.ini")

# Your custom calculations start here...
def circular_land_area(radius):
    return float(constants.get("CONSTANTS", "PI")) * radius**2

def future_value(present_value, interest_rate, years):
    return (
        present_value * float(constants.get(
            "CONSTANTS",
            "EULER_NUMBER"
        ))) ** (interest_rate * years)

# ...

In this example, your code first reads the configuration file and stores the resulting ConfigParser object in a global variable, constants. You can also name this variable CONSTANTS and use it globally as a constant. Then you update your calculations to read the constants from the configuration object itself.

Note that ConfigParser objects store the configuration parameters as strings, so you need to use the built-in float() function to convert the values into numbers.

This strategy may be beneficial when you’re creating a graphical user interface (GUI) app and need to set some parameters to define the shape and size of the app’s windows when loading and showing the GUI, for example.

Handling Constants as Environment Variables

Another helpful strategy to handle your constants is to define them as system variables if you’re on Windows or environment variables if you’re on macOS or Linux.

This approach is commonly used to configure deployment in different environments. You can also use environment variables for constants that imply security risks and shouldn’t be directly committed to the source code. Examples of these types of constants include authentication credentials, API access tokens, and so on.

To use this strategy, you first must export your constants as environment or system variables in your operating system. There are at least two ways to do this:

1. Manually export the constants in your current shell session
2. Add your constants to the shell’s configuration file

The first technique is pretty quick and practical. You can use it to run some fast tests on your code. For example, say that you need to export an API token as a system or environment variable. In that case, you just need to run the following command:

• Windows
• Linux + macOS

C:\> set API_TOKEN="593086396372"

$ export API_TOKEN="593086396372"

The main drawback of this technique is that your constants will be accessible only from the command-line session in which you defined them. A much better approach is to make your operating system load the constants whenever you fire up a command-line window.

If you’re on Windows, then check out the Configuring Environment Variables section in Your Python Coding Environment on Windows: Setup Guide to learn how to create system variables. Follow the instructions in this guide and add an API_TOKEN system variable with a value of 593086396372.

If you’re on Linux or macOS, then you can go to your home folder and open your shell’s configuration file. Once you’ve opened that file, add the following line at the end of it:

• Linux
• macOS

# .bashrc

export API_TOKEN="593086396372"

# .zshrc

export API_TOKEN="593086396372"

Linux and macOS automatically load the corresponding shell configuration file whenever you start a terminal or command-line window. This way, you ensure that the API_TOKEN variable is always available on your system.

Once you’ve defined the required environment variables for your Python constant, then you need to load them into your code. To do this, you can use the environ dictionary from Python’s os module. The keys and values of environ are strings representing the environment variables and their values, respectively.

Your API_TOKEN constant is now present in the environ dictionary. Therefore, you can read it from there with just two lines of code:

>>> import os

>>> os.environ["API_TOKEN"]
'593086396372'

Using environment variables to store constants, and the os.environ dictionary to read them into your code, is an effective way of configuring constants that depend on the environment your application is deployed in. It’s particularly useful when working with the cloud, so keep this technique in your Python tool kit.

Built-in Constants

According to the Python documentation, “A small number of constants live in the built-in namespace” (Source). The first two constants listed in the docs are True and False, which are the Python Boolean values. These two values are also instances of int. True has a value of 1, and False has a value of 0:

>>> True
True
>>> False
False

>>> isinstance(True, int)
True
>>> isinstance(False, int)
True

>>> int(True)
1
>>> int(False)
0

>>> True = 42
    ...
SyntaxError: cannot assign to True

>>> True is True
True
>>> False is False
True

Note that the True and False names are strict constants. In other words, they can’t be reassigned. If you try to reassign them, then you get a SyntaxError. These two values are also singleton objects in Python, meaning that there’s only one instance of each. That’s why the identity operator (is) returns True in the final examples above.

Another important and commonplace constant value is None, which is the null value in Python. This constant value comes in handy when you want to express the idea of nullability. Just like True and False, None is also a singleton and strict constant object that can’t be reassigned:

>>> None is None
True

>>> None = 42
    ...
SyntaxError: cannot assign to None

None is quite useful as a default argument value in functions, methods, and class constructors. It’s typically used to communicate that a variable is empty. Internally, Python uses None as the implicit return value of functions that don’t have an explicit return statement.

The ellipsis literal (...) is another constant value in Python. This special value is the same as Ellipsis and is the only instance of the types.EllipsisType type:

>>> Ellipsis
Ellipsis

>>> ...
Ellipsis

>>> ... is Ellipsis
True

You can use Ellipsis as a placeholder for unwritten code. You can also use it to replace the pass statement. In type hints, the ... literal communicates the idea of an unknown-length collection of data with a uniform type:

>>> def do_something():
...     ...  # TODO: Implement this function later
...

>>> class CustomException(Exception): ...
...
>>> raise CustomException("some error message")
Traceback (most recent call last):
    ...
CustomException: some error message

>>> # A tuple of integer values
>>> numbers: tuple[int, ...]

The Ellipsis constant value can come in handy in many situations and help you make your code more readable because of its semantic equivalence to the English ellipsis punctuation sign (…).

Another interesting and potentially useful built-in constant is __debug__, as you already learned at the beginning of this section. Python’s __debug__ is a Boolean constant that defaults to True. It’s a strict constant because you can’t change its value once your interpreter is running:

>>> __debug__
True

>>> __debug__ = False
    ...
SyntaxError: cannot assign to __debug__

The __debug__ constant is closely related to the assert statement. In short, if __debug__ is True, then all your assert statements will run. If __debug__ is False, then your assert statements will be disabled and won’t run at all. This feature can slightly improve the performance of your production code.

To change the value of __debug__ to False, you must run Python in optimized mode by using the -O or -OO command-line options, which provide two levels of bytecode optimization. Both levels generate an optimized Python bytecode that doesn’t contain assertions.

Internal Dunder Names

Python also has a broad set of internal dunder names that you can consider as constants. Because there are several of these special names, you’ll just learn about __name__ and __file__ in this tutorial.

The __name__ attribute is closely related to how you run a given piece of code. When importing a module, Python internally sets __name__ to a string containing the name of the module that you’re importing.

Fire up your code editor and create the following sample module:

# sample_name.py

print(f"The type of __name__ is: {type(__name__)}")
print(f"The value of __name__ is: {__name__}")

Once you have this file in place, get back to your command-line window and run the following command:

$ python -c "import sample_name"
The type of __name__ is: <class 'str'>
The value of __name__ is: sample_name

With the -c switch, you can execute a small piece of Python code at the command line. In this example, you import the sample_name module, which prints some messages to the screen. The first message tells you that __name__ is of type str, or string. The second message shows that __name__ was set to sample_name, which is the name of the module you just imported.

Alternatively, if you take sample_name.py and run it as a script, then Python will set __name__ to the "__main__" string . To confirm this fact, go ahead and run the following command:

$ python sample_name.py
The type of __name__ is: <class 'str'>
The value of __name__ is: __main__

Note that now __name__ holds the "__main__" string. This behavior indicates that you’ve run the file directly as an executable Python program.

The __file__ attribute will contain the path to the file that Python is currently importing or executing. You can use __file__ from inside a given module when you need to get the path to the module itself.

As an example of how __file__ works, go ahead and create the following module:

# sample_file.py

print(f"The type of __file__ is: {type(__file__)}")
print(f"The value of __file__ is: {__file__}")

If you import the sample_file module in your Python code, then __file__ will store the path to its containing module on your file system. Check this out by running the following command:

$ python -c "import sample_file"
The type of __file__ is: <class 'str'>
The value of __file__ is: /path/to/sample_file.py

Likewise, if you run sample_file.py as a Python executable program, then you get the same output as before:

$ python sample_file.py
The type of __file__ is: <class 'str'>
The value of __file__ is: /path/to/sample_file.py

In short, Python sets __file__ to contain the path to the module from which you’re using or accessing this attribute.

Useful String and Math Constants

You’ll find many useful constants in the standard library. Some of them are tightly connected to some specific modules, functions, and classes. Others are more generic, and you can use them in various scenarios. That’s the case with some math and string-related constants that you can find in the math and string modules, respectively.

The math module provides the following constants:

>>> import math

>>> # Euler's number (e)
>>> math.e
2.718281828459045

>>> # Pi (π)
>>> math.pi
3.141592653589793

>>> # Infinite (∞)
>>> math.inf
inf

>>> # Not a number (NaN)
>>> math.nan
nan

>>> # Tau (τ)
>>> math.tau
6.283185307179586

These constants will come in handy whenever you’re writing math-related code or even code that just uses them to perform specific computations, like your Circle class back in the Reusing Objects for Maintainability section.

Here’s an updated implementation of Circle using math.pi instead of your custom PI constant:

# circle.py

import math

class Circle:
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return math.pi * self.radius**2

    def perimeter(self):
        return 2 * math.pi * self.radius

    def projected_volume(self):
        return 4/3 * math.pi * self.radius**3

    def __repr__(self):
        return f"{self.__class__.__name__}(radius={self.radius})"

This updated version of Circle is more readable than your original version because it provides more context on where the Pi constant comes from, making it clear that it’s a math-related constant.

The math.pi constant also has the advantage that if you’re using an older version of Python, then you’ll get a 32-bit version of Pi. In contrast, if you use Circle in a modern version of Python, then you’ll get a 64-bit version of Pi. So, your program will self-adapt to its concrete execution environment.

The string module also defines several useful string constants. The table below shows the name and value of each constant:

These string-related constants come in handy in many situations. You can use them when you’re doing a lot of string processing, working with regular expressions, processing natural language, and more.

The .__slots__ Attribute

Python classes allow you to define a special class attribute called .__slots__. This attribute will hold a sequence of names that’ll work as instance attributes.

You won’t be able to add new instance attribute to a class with a .__slots__ attribute, because .__slots__ prevents the creation of an instance .__dict__ attribute. Additionally, not having a .__dict__ attribute implies an optimization in terms of memory consumption.

Using .__slots__, you can create a class that works as a namespace for read-only constants:

>>> class ConstantsNamespace:
...     __slots__ = ()
...     PI = 3.141592653589793
...     EULER_NUMBER = 2.718281828459045
...

>>> constants = ConstantsNamespace()

>>> constants.PI
3.141592653589793
>>> constants.EULER_NUMBER
2.718281828459045

>>> constants.PI = 3.14
Traceback (most recent call last):
    ...
AttributeError: 'ConstantsNamespace' object attribute 'PI' is read-only

In this example, you define ConstantsNamespace. The class’s .__slots__ attribute holds an empty tuple, meaning that instances of this class will have no attributes. Then you define your constants as class attributes.

The next step is to instantiate the class to create a variable holding the namespace with all your constants. Note that you can quickly access any constant in your special namespace, but you can’t assign it a new value. If you try to do it, then you get an AttributeError.

With this technique, you’re guaranteeing that no one else on your team can change the value of your constants. You’ve achieved the expected behavior of a strict constant.

The @property Decorator

You can also take advantage of the @property decorator to create a class that works as a namespace for your constants. To do this, you just need to define your constants as properties without providing them with a setter method:

>>> class ConstantsNamespace:
...     @property
...     def PI(self):
...         return 3.141592653589793
...     @property
...     def EULER_NUMBER(self):
...         return 2.718281828459045
...

>>> constants = ConstantsNamespace()

>>> constants.PI
3.141592653589793
>>> constants.EULER_NUMBER
2.718281828459045

>>> constants.PI = 3.14
Traceback (most recent call last):
    ...
AttributeError: can't set attribute 'PI'

Because you don’t provide setter methods for the PI and EULER_NUMBER properties, they’re read-only properties. This means that you can only access their values. It’s impossible to assign a new value to either one. If you try to do it, then you get an AttributeError.

The namedtuple() Factory Function

Python’s collections module provides a factory function called namedtuple(). This function lets you create tuple subclasses that allow the use of named fields and the dot notation to access their items, like in tuple_obj.attribute.

Like regular tuples, named tuple instances are immutable, which implies that you can’t modify an existing named tuple object in place. Being immutable sounds appropriate for creating a class that works as a namespace of strict constants.

Here’s how to do it:

>>> from collections import namedtuple

>>> ConstantsNamespace = namedtuple(
...     "ConstantsNamespace", ["PI", "EULER_NUMBER"]
... )
>>> constants = ConstantsNamespace(3.141592653589793, 2.718281828459045)

>>> constants.PI
3.141592653589793
>>> constants.EULER_NUMBER
2.718281828459045

>>> constants.PI = 3.14
Traceback (most recent call last):
    ...
AttributeError: can't set attribute

In this example, your constants play the role of fields in the underlying named tuple, ConstantsNamespace. Once you’ve created the named tuples instance, constants, you can access your constants by using the dot notation, like in constants.PI.

Because tuples are immutable, there’s no way for you to modify the value of any field. So, your constants named tuple object is a full-fledged namespace of strict constants.

The @dataclass Decorator

Data classes are classes that contain mainly data, as their name indicates. They can also have methods, but that’s not their primary goal. To create a data class, you need to use the @dataclass decorator from the dataclasses module.

How can you use this type of class to create a namespace of strict constants? The @dataclass decorator accepts a frozen argument that allows you to mark your data class as immutable. If it’s immutable, then once you’ve created an instance of a given data class, you have no way to modify its instance attributes.

Here’s how you can use a data class to create a namespace containing your constants:

>>> from dataclasses import dataclass

>>> @dataclass(frozen=True)
... class ConstantsNamespace:
...     PI = 3.141592653589793
...     EULER_NUMBER = 2.718281828459045
...

>>> constants = ConstantsNamespace()

>>> constants.PI
3.141592653589793
>>> constants.EULER_NUMBER
2.718281828459045

>>> constants.PI = 3.14
Traceback (most recent call last):
    ...
dataclasses.FrozenInstanceError: cannot assign to field 'PI'

In this example, you first import the @dataclass decorator. Then you use this decorator to turn ConstantsNamespace into a data class. To make the data class immutable, you set the frozen argument to True. Finally, you define ConstantsNamespace with your constants as class attributes.

You can create an instance of this class and use it as your constants namespace. Again, you can access all the constants, but you can’t modify their values, because the data class is frozen.

The .__setattr__() Special Method

Python classes let you define a special method called .__setattr__(). This method allows you to customize the attribute assignment process because Python automatically calls the method on every attribute assignment.

In practice, you can override .__setattr__() to prevent all attribute reassignments and make your attributes immutable. Here’s how you can override this method to create a class that works as a namespace for your constants:

>>> class ConstantsNamespace:
...     PI = 3.141592653589793
...     EULER_NUMBER = 2.718281828459045
...     def __setattr__(self, name, value):
...         raise AttributeError(f"can't reassign constant '{name}'")
...

>>> constants = ConstantsNamespace()

>>> constants.PI
3.141592653589793
>>> constants.EULER_NUMBER
2.718281828459045

>>> constants.PI = 3.14
Traceback (most recent call last):
    ...
AttributeError: can't reassign constant 'PI'

Your custom implementation of .__setattr__() doesn’t perform any assignment operation on the class’s attributes. It just raises an AttributeError when you try to set any attribute. This implementation makes the attributes immutable. Again, your ConstantsNamespace behaves as a namespace for constants.