Python's Assignment Operator: Write Robust Assignments

The Assignment Statement Syntax

To create a new variable or to update the value of an existing one in Python, you’ll use an assignment statement. This statement has the following three components:

1. A left operand, which must be a variable
2. The assignment operator (=)
3. A right operand, which can be a concrete value, an object, or an expression

Here’s how an assignment statement will generally look in Python:

variable = expression

Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value.

To execute an assignment statement like the above, Python runs the following steps:

1. Evaluate the right-hand expression to produce a concrete value or object. This value will live at a specific memory address in your computer.
2. Store the object’s memory address in the left-hand variable. This step creates a new variable if the current one doesn’t already exist or updates the value of an existing variable.

The second step shows that variables work differently in Python than in other programming languages. In Python, variables aren’t containers for objects. Python variables point to a value or object through its memory address. They store memory addresses rather than objects.

This behavior difference directly impacts how data moves around in Python, which is always by reference. In most cases, this difference is irrelevant in your day-to-day coding, but it’s still good to know.

The Assignment Operator

The central component of an assignment statement is the assignment operator. This operator is represented by the = symbol, which separates two operands:

1. A variable
2. A value or an expression that evaluates to a concrete value

Operators are special symbols that perform mathematical, logical, and bitwise operations in a programming language. The objects (or object) on which an operator operates are called operands.

Unary operators, like the not Boolean operator, operate on a single object or operand, while binary operators act on two. That means the assignment operator is a binary operator.

The right-hand operand in an assignment statement can be any Python object, such as a number, list, string, dictionary, or even a user-defined object. It can also be an expression. In the end, expressions always evaluate to concrete objects, which is their return value.

Here are a few examples of assignments in Python:

>>> number = 42
>>> greeting = "Hello, World!"

>>> total = 15 + 25
>>> is_true = 42 < 84

The first two sample assignments in this code snippet use concrete values, also known as literals, to create and initialize number and greeting. The third example assigns the result of a math expression to the total variable, while the last example uses a Boolean expression.

>>> number = 42
>>> id(number)
4311827984

Unlike expressions, assignment statements don’t have a return value because their purpose is to make the association between the variable and its value. That’s why the Python interpreter doesn’t issue any output in the above examples.

Now that you know the basics of how to write an assignment statement, it’s time to tackle why you would want to use one.

Assignments and Variables

The assignment statement is the explicit way for you to associate a name with an object in Python. You can use this statement for two main purposes:

1. Creating and initializing new variables
2. Updating the values of existing variables

When you use a variable name as the left operand in an assignment statement for the first time, you’re creating a new variable. At the same time, you’re initializing the variable to point to the value of the right operand.

On the other hand, when you use an existing variable in a new assignment, you’re updating or mutating the variable’s value. Strictly speaking, every new assignment will make the variable refer to a new value and stop referring to the old one. Python will garbage-collect all the values that are no longer referenced by any existing variable.

Assignment statements not only assign a value to a variable but also determine the data type of the variable at hand. This additional behavior is another important detail to consider in this kind of statement.

Because Python is a dynamically typed language, successive assignments to a given variable can change the variable’s data type. Changing the data type of a variable during a program’s execution is considered bad practice and highly discouraged. It can lead to subtle bugs that can be difficult to track down.

Unlike in math equations, in Python assignments, the left operand must be a variable rather than an expression or a value. For example, the following construct is illegal, and Python flags it as invalid syntax:

>>> a = 3
>>> b = 4
>>> hypotenuse ** 2 = a ** 2 + b ** 2
    ...
SyntaxError: cannot assign to expression here.
    Maybe you meant '==' instead of '='?

In this example, you have expressions on both sides of the = sign, and this isn’t allowed in Python code. The error message suggests that you may be confusing the equality operator with the assignment one, but that’s not the case. You’re really running an invalid assignment.

To correct this construct and convert it into a valid assignment, you’ll have to do something like the following:

>>> from math import sqrt

>>> a = 3
>>> b = 4
>>> hypotenuse = sqrt(a ** 2 + b ** 2)
>>> hypotenuse
5.0

In this code snippet, you first import the sqrt() function from the math module. Then you isolate the hypotenuse variable in the original equation by using the sqrt() function. Now your code works correctly.

Now you know what kind of syntax is invalid. But don’t get the idea that assignment statements are rigid and inflexible. In fact, they offer lots of room for customization, as you’ll learn next.

Other Assignment Syntax

Python’s assignment statements are pretty flexible and versatile. You can write them in several ways, depending on your specific needs and preferences. Here’s a quick summary of the main ways to write assignments in Python:

# Base assignments
variable = value  # A literal, such as None, 0, 3.14, "Hello", {}
variable = expression  # Math, Boolean, bitwise expression, function calls...

# Multiple assignments
variable_1 = variable_2 = ... = variable_n = expression

# Parallel assignments
variable_1, variable_2, ..., variable_n = value_1, value_2, ..., value_n
variable_1, variable_2, ..., variable_n = exp_1, exp_2, ..., exp_n

# Augmented assignments
existing_variable += value
existing_variable += expression

# Parallel assignments with iterable unpacking
variable_1, variable_2, ..., variable_n  = n_length_iterable
(variable_1, variable_2, ..., variable_n) = n_length_iterable
[variable_1, variable_2, ..., variable_n] = n_length_iterable
variable, *bag_variable, ..., variable_n = unknown_length_iterable

Up to this point, you’ve mostly learned about the base assignment syntax in the above code snippet. In the following sections, you’ll learn about multiple, parallel, and augmented assignments. You’ll also learn about assignments with iterable unpacking.

Read on to see the assignment statements in action!

Initializing and Updating Variables

The most elementary use case of an assignment statement is to create a new variable and initialize it using a particular value or expression:

>>> counter = 0
>>> celsius = 25
>>> fahrenheit = (celsius * 9 / 5) + 32
>>> user_template = {"id": None, "name": "", "permissions": ("r",)}
>>> welcome_message = "Welcome to Real Python!"
>>> is_empty = False

All these statements create new variables, assigning them initial values or expressions. For an initial value, you should always use the most sensible and least surprising value that you can think of. For example, initializing a counter to something different from 0 may be confusing and unexpected because counters almost always start having counted no objects.

Updating a variable’s current value or state is another common use case of assignment statements. In Python, assigning a new value to an existing variable doesn’t modify the variable’s current value. Instead, it causes the variable to refer to a different value. The previous value will be garbage-collected if no other variable refers to it.

Consider the following examples:

>>> greeting = "Hello, World!"
>>> greeting
'Hello, World!'

>>> greeting = "Hi, Pythonistas!"
>>> greeting
'Hi, Pythonistas!'

These examples run two consecutive assignments on the same variable. The first one assigns the string "Hello, World!" to a new variable named greeting.

The second assignment updates the value of greeting by reassigning it the "Hi, Pythonistas!" string. In this example, the original value of greeting —the "Hello, World!" string— is lost and garbage-collected. From this point on, you can’t access the old "Hello, World!" string.

Even though running multiple assignments on the same variable during a program’s execution is common practice, you should use this feature with caution. Changing the value of a variable can make your code difficult to read, understand, and debug. To comprehend the code fully, you’ll have to remember all the places where the variable was changed and the sequential order of those changes.

Because assignments also define the data type of their target variables, it’s also possible for your code to accidentally change the type of a given variable at runtime. A change like this can lead to breaking errors, like AttributeError exceptions. Remember that strings don’t have the same methods and attributes as lists or dictionaries, for example.

Making Multiple Variables Refer to the Same Object

In Python, you can make several variables reference the same object in a multiple-assignment line. This can be useful when you want to initialize several similar variables using the same initial value:

>>> letter_counter = word_counter = 0
>>> id(letter_counter) == id(word_counter)
True

In this example, you chain two assignment operators in a single line. This way, your two variables refer to the same initial value of 0. Note how both variables hold the same memory address, so they point to the same instance of 0.

When it comes to integer variables, Python exhibits a curious behavior. It provides a numeric interval where multiple assignments behave the same as independent assignments. Consider the following examples:

>>> # Independent assignments
>>> n = 42
>>> m = 42
>>> id(n) == id(m)
True

>>> # Multiple assignments
>>> x = y = 42
>>> id(x) == id(y)
True

To create n and m, you use independent assignments. Therefore, they should point to different instances of the number 42. However, both variables hold the same object, which you confirm by comparing their corresponding memory addresses.

Now check what happens when you use a greater initial value:

>>> n = 300
>>> m = 300
>>> id(x) == id(y)
False

>>> x = y = 300
>>> id(x) == id(y)
True

Now n and m hold different memory addresses, which means they point to different instances of the integer number 300. In contrast, when you use multiple assignments, both variables refer to the same object. This tiny difference can save you small bits of memory if you frequently initialize integer variables in your code.

The implicit behavior of making independent assignments point to the same integer number is actually an optimization called interning. It consists of globally caching the most commonly used integer values in day-to-day programming.

Under the hood, Python defines a numeric interval in which interning takes place. That’s the interning interval for integer numbers. You can determine this interval using a small script like the following:

# interning.py

from platform import python_version

interning = [
    x
    for x, y in zip(range(-10, 500), range(-10, 500))
    if x is y
]

print(
    f"Interning interval for Python {python_version()} is:"
    f" [{interning[0]} to {interning[-1]}]"
)

This script helps you determine the interning interval by comparing integer numbers from -10 to 500. If you run the script from your command line, then you’ll get an output like the following:

$ python interning.py
Interning interval for Python 3.11.0 is: (-5 to 256)

This output means that if you use a single number between -5 and 256 to initialize several variables in independent statements, then all these variables will point to the same object, which will help you save small bits of memory in your code.

In contrast, if you use a number that falls outside of the interning interval, then your variables will point to different objects instead. Each of these objects will occupy a different memory spot.

Updating Lists Through Indices and Slices

You can use the assignment operator to mutate the value stored at a given index in a Python list. The operator also works with list slices. The syntax to write these types of assignment statements is the following:

a_list[index] = expression

a_list[start:stop:step] = expression

In the first construct, expression can return any Python object, including another list. In the second construct, expression must return a series of values as a list, tuple, or any other sequence. You’ll get a TypeError if expression returns a single value.

Here’s an example of updating an individual value in a list:

>>> numbers = [1, 2, 7, 4, 5]
>>> numbers
[1, 2, 7, 4, 5]

>>> numbers[2] = 3
>>> numbers
[1, 2, 3, 4, 5]

In this example, you update the value at index 2 using an assignment statement. The original number at that index was 7, and after the assignment, the number is 3.

>>> numbers = (1, 2, 2, 4, 5)
>>> numbers[2] = 3
Traceback (most recent call last):
    ...
TypeError: 'tuple' object does not support item assignment

>>> letters = "ABcDE"
>>> letters[2] = "C"
Traceback (most recent call last):
    ...
TypeError: 'str' object does not support item assignment

It’s important to note that you can’t add new values to a list by using indices that don’t exist in the target list:

>>> numbers[5] = 6
Traceback (most recent call last):
    ...
IndexError: list assignment index out of range

In this example, you try to add a new value to the end of numbers by using an index that doesn’t exist. This assignment isn’t allowed because there’s no way to guarantee that new indices will be consecutive. If you ever want to add a single value to the end of a list, then use the .append() method.

If you want to update several consecutive values in a list, then you can use slicing and an assignment statement:

>>> letters = ["A", "b", "c", "D"]
>>> letters[1:3] = ["B", "C"]
>>> letters
['A', 'B', 'C', 'D']

>>> letters[3:] = ("F", "G")
>>> letters
['A', 'B', 'C', 'F', 'G']

>>> letters[3:3] = ["D"]
>>> letters
['A', 'B', 'C', 'D', 'F', 'G']

>>> letters[1::2] = ["b", "d", "g"]
>>> letters
['A', 'b', 'C', 'd', 'F', 'g']

In the first example, you update the letters between indices 1 and 3 without including the letter at 3. The second example updates the letters from index 3 until the end of the list. Note that this slicing appends a new value to the list because the target slice is shorter than the assigned values.

Also note that the new values were provided through a tuple, which means that this type of assignment allows you to use other types of sequences to update your target list.

The third example updates a single value using a slice where both indices are equal. In this example, the assignment inserts a new item into your target list.

In the final example, you use a step of 2 to replace alternating letters with their lowercase counterparts. This slicing starts at index 1 and runs through the whole list, stepping by two items each time.

Adding and Updating Dictionary Keys

Updating the value of an existing key or adding new key-value pairs to a dictionary is another common use case of assignment statements. To do these operations, you can use the following syntax:

a_dict[existing_key] = expression

a_dict[new_key] = expression

The first construct helps you update the current value of an existing key, while the second construct allows you to add a new key-value pair to the dictionary.

For example, to update an existing key, you can do something like this:

>>> inventory = {"apple": 100, "orange": 80, "banana": 120}
>>> inventory
{'apple': 100, 'orange': 80, 'banana': 120}

>>> inventory["orange"] = 140
>>> inventory
{'apple': 100, 'orange': 140, 'banana': 120}

In this example, you update the current inventory of oranges in your store using an assignment. The left operand is the existing dictionary key, and the right operand is the desired new value.

While you can’t add new values to a list by assignment, dictionaries do allow you to add new key-value pairs using the assignment operator. In the example below, you add a lemon key to inventory:

>>> inventory["lemon"] = 100
>>> inventory
{'apple': 100, 'orange': 140, 'banana': 120, 'lemon': 100}

In this example, you successfully add a new key-value pair to your inventory with 100 units. This addition is possible because dictionaries don’t have consecutive indices but unique keys, which are safe to add by assignment.

Doing Parallel Assignments

The assignment statement does more than assign the result of a single expression to a single variable. It can also cope nicely with assigning multiple values to multiple variables simultaneously in what’s known as a parallel assignment.

Here’s the general syntax for parallel assignments in Python:

variable_1, variable_2, ..., variable_n = value_1, value_2, ..., value_n

variable_1, variable_2, ..., variable_n = exp_1, exp_2, ..., exp_n

(variable_1, variable_2, ..., variable_n) = exp_1, exp_2, ..., exp_n

[variable_1, variable_2, ..., variable_n] = exp_1, exp_2, ..., exp_n

Note that the left side of the statement can be either a tuple or a list of variables. Remember that to create a tuple, you just need a series of comma-separated elements. In this case, these elements must be variables.

The right side of the statement must be a sequence or iterable of values or expressions. In any case, the number of elements in the right operand must match the number of variables on the left. Otherwise, you’ll get a ValueError exception.

In the following example, you compute the two solutions of a quadratic equation using a parallel assignment:

>>> from math import sqrt

>>> a, b, c = 2.0, -1.0, -4.0

>>> x1, x2 = (
...     (-b - sqrt(b**2 - 4 * a * c)) / (2 * a),
...     (-b + sqrt(b**2 - 4 * a * c)) / (2 * a),
... )

>>> f"{x1=}, {x2=}"
'x1=-1.1861406616345072, x2=1.6861406616345072'

In this example, you first import sqrt() from the math module. Then you initialize the equation’s coefficients in a parallel assignment.

The equation’s solution is computed in another parallel assignment. The left operand contains a tuple of two variables, x1 and x2. The right operand consists of a tuple of expressions that compute the solutions for the equation. Note how each result is assigned to each variable by position.

A classical use case of parallel assignment is to swap values between variables:

>>> previous_value = 42
>>> next_value = 43

>>> next_value, previous_value = previous_value, next_value

>>> previous_value
43
>>> next_value
42

The highlighted line does the magic and swaps the values of previous_value and next_value at the same time. Note that in a programming language that doesn’t support this kind of assignment, you’d have to use a temporary variable to produce the same effect:

>>> previous_value = 42
>>> next_value = 43

>>> temp = previous_value
>>> previous_value = next_value
>>> next_value = temp

>>> previous_value
43
>>> next_value
42

In this example, instead of using parallel assignment to swap values between variables, you use a new variable to temporarily store the value of previous_value to avoid losing its reference.

For a concrete example of when you’d need to swap values between variables, say you’re learning how to implement the bubble sort algorithm, and you come up with the following function:

>>> def bubble_sort_list(a_list):
...     n = len(a_list)
...     for i in range(n):
...         is_sorted = True
...         for j in range(n - i - 1):
...             if a_list[j] > a_list[j + 1]:
...                 a_list[j], a_list[j + 1] = a_list[j + 1], a_list[j]
...                 is_sorted = False
...         if is_sorted:
...             break
...     return a_list
...

>>> bubble_sort_list([1, 3, 2, 4, 7, 6, 3, 8, 9, 1])
[1, 1, 2, 3, 3, 4, 6, 7, 8, 9]

In the highlighted line, you use a parallel assignment to swap values in place if the current value is less than the next value in the input list. To dive deeper into the bubble sort algorithm and into sorting algorithms in general, check out Sorting Algorithms in Python.

Unpacking Iterables

You can use assignment statements for iterable unpacking in Python. Unpacking an iterable means assigning its values to a series of variables one by one. The iterable must be the right operand in the assignment, while the variables must be the left operand.

Like in parallel assignments, the variables must come as a tuple or list. The number of variables must match the number of values in the iterable. Alternatively, you can use the unpacking operator (*) to grab several values in a variable if the number of variables doesn’t match the iterable length.

Here’s the general syntax for iterable unpacking in Python:

variable_1, variable_2, ..., variable_n  = n_length_iterable

(variable_1, variable_2, ..., variable_n) = n_length_iterable

[variable_1, variable_2, ..., variable_n] = n_length_iterable

variable, *bag_variable, ..., variable_n = unknown_length_iterable

Iterable unpacking is a powerful feature that you can use all around your code. It can help you write more readable and concise code. For example, you may find yourself doing something like this:

>>> numbers = [1, 2, 3, 4]

>>> one = numbers[0]
>>> two = numbers[1]
>>> three = numbers[2]
>>> four = numbers[3]

>>> one
1
>>> two
2
>>> three
3
>>> four
4

Whenever you do something like this in your code, go ahead and replace it with a more readable iterable unpacking using a single and elegant assignment, like in the following code snippet:

>>> numbers = [1, 2, 3, 4]

>>> one, two, three, four = numbers

>>> one
1
>>> two
2
>>> three
3
>>> four
4

The numbers list on the right side contains four values. The assignment operator unpacks these values into the four variables on the left side of the statement. The values in numbers get assigned to variables in the same order that they appear in the iterable. The assignment is done by position.

The above example shows the most common form of iterable unpacking in Python. The main condition for the example to work is that the number of variables matches the number of values in the iterable.

What if you don’t know the iterable length upfront? Will the unpacking work? It’ll work if you use the * operator to pack several values into one of your target variables.

For example, say that you want to unpack the first and second values in numbers into two different variables. Additionally, you would like to pack the rest of the values in a single variable conveniently called rest. In this case, you can use the unpacking operator like in the following code:

>>> first, second, *rest = numbers
>>> first
1
>>> second
2
>>> rest
[3, 4]

In this example, first and second hold the first and second values in numbers, respectively. These values are assigned by position. The * operator packs all the remaining values in the input iterable into rest.

The unpacking operator (*) can appear at any position in your series of target variables. However, you can only use one instance of the operator:

>>> *head, last = numbers
>>> head
[1, 2, 3]
>>> last
4

>>> head, *body, tail = numbers
>>> head
1
>>> body
[2, 3]
>>> tail
4

>>> *head, *rest = numbers
    ...
SyntaxError: multiple starred expressions in assignment

The iterable unpacking operator works in any position in your list of variables. Note that you can only use one unpacking operator per assignment. Using more than one unpacking operator isn’t allowed and raises a SyntaxError.

Dropping away unwanted values from the iterable is a common use case for the iterable unpacking operator. Consider the following example:

>>> *_, useful = numbers
>>> useful
4
>>> _
[1, 2, 3]

In Python, if you want to signal that a variable won’t be used, then you use an underscore (_) as the variable’s name. In this example, useful holds the only value that you need to use from the input iterable. The _ variable is a placeholder that guarantees that the unpacking works correctly. You won’t use the values that end up in this disposable variable.

>>> useful = numbers[-1]
>>> useful
4

The pattern used in the above example comes in handy when you have a function that returns multiple values, and you only need a few of these values in your code. The os.walk() function may provide a good example of this situation.

This function allows you to iterate over the content of a directory recursively. The function returns a generator object that yields three-item tuples. Each tuple contains the following items:

• The path to the current directory as a string
• The names of all the immediate subdirectories as a list of strings
• The names of all the files in the current directory as a list of strings

Now say that you want to iterate over your home directory and list only the files. You can do something like this:

>>> import os

>>> for content in os.walk("/path/to/your/home"):
...     *_, filenames = content
...     print(filenames)
...

This code will issue a long output depending on the current content of your home directory. Note that you need to provide a string with the path to your user folder for the example to work. The _ placeholder variable will hold the unwanted data.

In contrast, the filenames variable will hold the list of files in the current directory, which is the data that you need. The code will print the list of filenames. Go ahead and give it a try!

Providing Default Argument Values

The assignment operator also comes in handy when you need to provide default argument values in your functions and methods. Default argument values allow you to define functions that take arguments with sensible defaults. These defaults allow you to call the function with specific values or to simply rely on the defaults.

As an example, consider the following function:

>>> def greet(name="World"):
...     print(f"Hello, {name}!")
...

This function takes one argument, called name. This argument has a sensible default value that’ll be used when you call the function without arguments. To provide this sensible default value, you use an assignment.

Here’s how the function works:

>>> greet()
Hello, World!

>>> greet("Pythonista")
Hello, Pythonista!

If you don’t provide a name during the call to greet(), then the function uses the default value provided in the definition. If you provide a name, then the function uses it instead of the default one.

Up to this point, you’ve learned a lot about the Python assignment operator and how to use it for writing different types of assignment statements. In the following sections, you’ll dive into a great feature of assignment statements in Python. You’ll learn about augmented assignments.

Augmented Mathematical Assignment Operators

An equation like x = x + b doesn’t make sense in math. But in programming, a statement like x = x + b is perfectly valid and can be extremely useful. It adds b to x and reassigns the result back to x.

As you already learned, Python provides an operator to shorten x = x + b. Yes, the += operator allows you to write x += b instead. Python also offers augmented assignment operators for most math operators. Here’s a summary:

The Example column provides generic examples of how to use the operators in actual code. Note that x must be previously defined for the operators to work correctly. On the other hand, y can be either a concrete value or an expression that returns a value.

>>> import numpy as np

>>> x = np.ones(3)
>>> x
array([1., 1., 1.])

>>> m = np.eye(3)
>>> m
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

>>> x @ m
array([1., 1., 1.])

>>> x @= m
Traceback (most recent call last):
    ...
TypeError: In-place matrix multiplication is not (yet) supported.
    Use 'a = a @ b' instead of 'a @= b'.

To illustrate how augmented assignment operators work, say that you need to create a function that takes an iterable of numeric values and returns their sum. You can write this function like in the code below:

>>> def sum_all(numbers):
...    total = 0
...    for number in numbers:
...        total += number  # Augmented addition
...    return total
...

>>> sum_all([1, 2, 3, 4])
10

In this function, you first initialize total to 0. In each iteration, the loop adds a new number to total using the augmented addition operator (+=). When the loop terminates, total holds the sum of all the input numbers. Variables like total are known as accumulators. The += operator is typically used to update accumulators.

Another interesting example of using an augmented assignment is when you need to implement a countdown while loop to reverse an iterable. In this case, you can use the -= operator:

>>> def custom_reversed(sequence):
...     index = len(sequence) - 1
...     while index >= 0:
...         yield sequence[index]
...         index -= 1
...

>>> list(custom_reversed("12345"))
['5', '4', '3', '2', '1']

In this example, custom_reversed() is a generator function because it uses yield. Calling the function creates an iterator that yields items from the input iterable in reverse order. To decrement the control variable, index, you use an augmented subtraction statement that subtracts 1 from the variable in every iteration.

Finally, counters are a special type of accumulators that allow you to count objects. Here’s an example of a letter counter:

>>> word = "mississippi"
>>> counter = {}

>>> for letter in word:
...     if letter not in counter:
...         counter[letter] = 0
...     counter[letter] += 1
...

>>> counter
{'m': 1, 'i': 4, 's': 4, 'p': 2}

To create this counter, you use a Python dictionary. The keys store the letters. The values store the counts. Again, to increment the counter, you use an augmented addition.

Counters are so common in programming that Python provides a tool specially designed to facilitate the task of counting. Check out Python’s Counter: The Pythonic Way to Count Objects for a complete guide on how to use this tool.

Augmented Assignments for Concatenation and Repetition

The += and *= augmented assignment operators also work with sequences, such as lists, tuples, and strings. The += operator performs augmented concatenations, while the *= operator performs augmented repetition.

These operators behave differently with mutable and immutable data types:

Note that the augmented concatenation operator operates on two sequences, while the augmented repetition operator works on a sequence and an integer number.

Consider the following examples and pay attention to the result of calling the id() function:

>>> # Mutable target
>>> list_1 = [1, 2, 3]
>>> id(list_1)
4323479104

>>> list_2 = [4, 5]
>>> list_2
[4, 5]

>>> list_1 += list_2  # Concatenation...
>>> list_1
[1, 2, 3, 4, 5]
>>> id(list_1)
4323479104

>>> # Immutable target
>>> tuple_1 = (1, 2, 3)
>>> id(tuple_1)
4387336896

>>> tuple_2 = (4, 5)
>>> tuple_2
(4, 5)

>>> tuple_1 += tuple_2  # Concatenation...
>>> tuple_1
(1, 2, 3, 4, 5)
>>> id(tuple_1)
4387485104

Mutable sequences like lists support the += augmented assignment operator through the .__iadd__() method, which performs an in-place addition. This method mutates the underlying list, appending new values to its end.

Immutable sequences, such as tuples and strings, don’t provide an .__iadd__() method. Therefore, augmented concatenations fall back to the .__add__() method, which doesn’t modify the sequence in place but returns a new sequence.

There’s another difference between mutable and immutable sequences when you use them in an augmented concatenation. Consider the following examples:

>>> a_list = [1, 2, 3]
>>> a_list += (4, 5)
>>> a_list
[1, 2, 3, 4, 5]

>>> a_list += "67"
>>> a_list
[1, 2, 3, 4, 5, '6', '7']

>>> a_tuple = (1, 2, 3)
>>> a_tuple += [4, 5]
Traceback (most recent call last):
    ...
TypeError: can only concatenate tuple (not "list") to tuple

>>> a_string = "123"
>>> a_string += ("4", "5")
Traceback (most recent call last):
    ...
TypeError: can only concatenate str (not "tuple") to str

With mutable sequences, the data to be concatenated can come as a list, tuple, string, or any other iterable. In contrast, with immutable sequences, the data can only come as objects of the same type. You can concatenate tuples to tuples and strings to strings, for example.

Again, the augmented repetition operator works with a sequence on the left side of the operator and an integer on the right side. This integer value represents the number of repetitions to get in the resulting sequence:

>>> # Mutable target
>>> a_list = [1, 2, 3]
>>> id(a_list)
4395563840

>>> n = 2
>>> a_list *= n  # Repetition...
>>> a_list
[1, 2, 3, 1, 2, 3]
>>> id(a_list)
4395563840

>>> a_list[0] is a_list[3]
True

>>> # Immutable target
>>> a_tuple = (1, 2, 3)
>>> id(a_tuple)
4393522944

>>> n = 2
>>> a_tuple *= n  # Repetition...
>>> a_tuple
(1, 2, 3, 1, 2, 3)
>>> id(a_tuple)
4394199328

>>> a_tuple[0] is a_tuple[3]
True

When the *= operator operates on a mutable sequence, it falls back to the .__imul__() method, which performs the operation in place, modifying the underlying sequence. In contrast, if *= operates on an immutable sequence, then .__mul__() is called, returning a new sequence of the same type.

Note that a_list[0] is a_list[3] returns True. This is because the *= operator doesn’t make a copy of the repeated data. It only reflects the data. This behavior can be a source of issues when you use the operator with mutable values.

For example, say that you want to create a list of lists to represent a matrix, and you need to initialize the list with n empty lists, like in the following code:

>>> n = 3
>>> matrix = [[]]
>>> matrix *= n
>>> matrix
[[], [], []]

In this example, you use the *= operator to populate matrix with three empty lists. Now check out what happens when you try to populate the first sublist in matrix:

>>> matrix[0].append(1)
>>> matrix[0].append(2)
>>> matrix[0].append(3)
>>> matrix
[[1, 2, 3], [1, 2, 3], [1, 2, 3]]

The appended values are reflected in the three sublists. This happens because the *= operator doesn’t make copies of the data that you want to repeat. It only reflects the data. Therefore, every sublist in matrix points to the same object and memory address.

If you ever need to initialize a list with a bunch of empty sublists, then use a list comprehension:

>>> n = 3
>>> matrix = [[] for _ in range(n)]
>>> matrix
[[], [], []]

>>> matrix[0].append(1)
>>> matrix[0].append(2)
>>> matrix[0].append(3)
>>> matrix
[[1, 2, 3], [], []]

This time, when you populate the first sublist of matrix, your changes aren’t propagated to the other sublists. This is because all the sublists are different objects that live in different memory addresses.

Augmented Bitwise Assignment Operators

Bitwise operators also have their augmented versions. The logic behind them is similar to that of the math operators. The following table summarizes the augmented bitwise operators that Python provides:

The augmented bitwise assignment operators perform the intended operation by taking the current value of the left operand as a starting point for the computation. Consider the following example, which uses the & and &= operators:

>>> number_1 = 123
>>> bin(number_1)
'0b1111011'

>>> number_2 = 456
>>> bin(number_2)
'0b111001000'

>>> # Bitwise AND
>>> #     0b1111011 (int 123)
>>> # & 0b111001000 (int 456)
>>> # -------------
>>> #     0b1001000 (int 72)

>>> number_1 & number_2
72
>>> bin(number_1 & number_2)
'0b1001000'

>>> number_1 &= number_2
>>> number_1
72
>>> bin(number_1)
'0b1001000'

Programmers who work with high-level languages like Python rarely use bitwise operations in day-to-day coding. However, these types of operations can be useful in some situations.

For example, say that you’re implementing a Unix-style permission system for your users to access a given resource. In this case, you can use the characters "r" for reading, "w" for writing, and "x" for execution permissions, respectively. However, using bit-based permissions could be more memory efficient:

>>> r = 0b100
>>> w = 0b010
>>> x = 0b001

>>> # Assign permissions to users with |
>>> admin = r | w | x
>>> bin(admin)
'0b111'

>>> # Assign permissions to users with |=
>>> user = r
>>> user |= w
>>> bin(user)
'0b110'

>>> # Check permission with & and bool()
>>> bool(r & user)
True
>>> bool(x & user)
False

You can assign permissions to your users with the OR bitwise operator or the augmented OR bitwise operator. Finally, you can use the bitwise AND operator to check if a user has a certain permission, as you did in the final two examples.

You’ve learned a lot about augmented assignment operators and statements in this and the previous sections. These operators apply to math, concatenation, repetition, and bitwise operations. Now you’re ready to look at other assignment variants that you can use in your code or find in other developers’ code.

Annotated Assignment Statements

PEP 526 introduced a dedicated syntax for variable annotation back in Python 3.6. The syntax consists of the variable name followed by a colon (:) and the variable type:

>>> counter: int
>>> name: str
>>> fruits: list[str]

Even though these statements declare three variables with their corresponding data types, the variables aren’t actually created or initialized. So, for example, you can’t use any of these variables in an augmented assignment statement:

>>> counter += 1
Traceback (most recent call last):
    ...
NameError: name 'counter' is not defined

If you try to use one of the previously declared variables in an augmented assignment, then you get a NameError because the annotation syntax doesn’t define the variable. To actually define it, you need to use an assignment.

The good news is that you can use the variable annotation syntax in an assignment statement with the = operator:

>>> counter: int = 0
>>> counter += 1
>>> counter += 1
>>> counter
2

The first statement in this example is what you can call an annotated assignment statement in Python. You may ask yourself why you should use type annotations in this type of assignment if everybody can see that counter holds an integer number. You’re right. In this example, the variable type is unambiguous.

However, imagine what would happen if you found a variable initialization like the following:

>>> class User:
...     # Class implementation...
...     pass
...

>>> users = []

What would be the data type of each user in users? If the initialization of users is far away from the definition of the User class, then there’s no quick way to answer this question. To clarify this ambiguity, you can provide the appropriate type hint for users:

>>> class User:
...     # Class implementation...
...     pass
...

>>> users: list[User] = []

Now you’re clearly communicating that users will hold a list of User instances. Using type hints in assignment statements that initialize variables to empty collection data types—such as lists, tuples, or dictionaries—allows you to provide more context about how your code works. This practice will make your code more explicit and less error-prone.

Assignment Expressions With the Walrus Operator

Up to this point, you’ve learned that regular assignment statements with the = operator don’t have a return value. They just create or update variables. Therefore, you can’t use a regular assignment to assign a value to a variable within the context of an expression.

Python 3.8 changed this by introducing a new type of assignment statement through PEP 572. This new statement is known as an assignment expression or named expression.

Unlike regular assignments, assignment expressions have a return value, which is why they’re called expressions in the first place. This return value is automatically assigned to a variable. To write an assignment expression, you must use the walrus operator (:=), which was named for its resemblance to the eyes and tusks of a walrus lying on its side.

The general syntax of an assignment statement is as follows:

(variable := expression)

This expression looks like a regular assignment. However, instead of using the assignment operator (=), it uses the walrus operator (:=). For the expression to work correctly, the enclosing parentheses are required in most use cases. However, there are certain situations in which these parentheses are superfluous. Either way, they won’t hurt you.

Assignment expressions come in handy when you want to reuse the result of an expression or part of an expression without using a dedicated assignment to grab this value beforehand.

A particularly handy use case for assignment expressions is when you need to grab the result of an expression used in the context of a conditional statement. For example, say that you need to write a function to compute the mean of a sample of numeric values. Without the walrus operator, you could do something like this:

>>> def mean(sample):
...     n = len(sample)
...     if n == 0:
...         raise ValueError("input data required")
...     return sum(sample) / n
...

>>> mean([1, 2, 3, 4, 5])
3.0

In this example, the sample size (n) is a value that you need to reuse in two different computations. First, you need to check whether the sample has data points or not. Then you need to use the sample size to compute the mean. To be able to reuse n, you wrote a dedicated assignment statement at the beginning of your function to grab the sample size.

You can avoid this extra step by combining it with the first use of the target value, len(sample), using an assignment expression like the following:

>>> def mean(sample):
...     if (n := len(sample)) == 0:
...         raise ValueError("input data required")
...     return sum(sample) / n
...

>>> mean([1, 2, 3, 4, 5])
3.0

The assignment expression introduced in the conditional computes the sample size and assigns it to n. This way, you guarantee that you have a reference to the sample size to use in further computations.

Because the assignment expression returns the sample size anyway, the conditional can check whether that size equals 0 or not and then take a certain course of action depending on the result of this check. The return statement computes the sample’s mean and sends the result back to the function caller.

Managed Attribute Assignments

Python provides a few tools that allow you to fine-tune the operations behind the assignment of attributes. The attributes that run implicit operations on assignments are commonly referred to as managed attributes.

Properties are the most commonly used tool for providing managed attributes in your classes. However, you can also use descriptors and, in some cases, the .__setitem__() special method.

To understand what fine-tuning the operation behind an assignment means, say that you need a Point class that only allows numeric values for its coordinates, x and y. To write this class, you must set up a validation mechanism to reject non-numeric values. You can use properties to attach the validation functionality on top of x and y.

Here’s how you can write your class:

# point.py

class Point:

    @property
    def x(self):
        return self._x

    @x.setter
    def x(self, value):
        try:
            self._x = float(value)
        except ValueError:
            raise ValueError('"x" must be a number') from None

    @property
    def y(self):
        return self._y

    @y.setter
    def y(self, value):
        try:
            self._y = float(value)
        except ValueError:
            raise ValueError('"y" must be a number') from None

In Point, you use properties for the .x and .y coordinates. Each property has a getter and a setter method. The getter method returns the attribute at hand. The setter method runs the input validation using a try … except block and the built-in float() function. Then the method assigns the result to the actual attribute.

Here’s how your class works in practice:

>>> from point import Point

>>> point_1 = Point()
>>> point_1.x = 1
>>> point_1.y = 2
>>> point_1.x, point_1.y
1.0 2.0

>>> point_2 = Point()
>>> point_2.x = "one"
Traceback (most recent call last):
    ...
ValueError: "x" must be a number

When you use a property-based attribute as the left operand in an assignment statement, Python automatically calls the property’s setter method, running any computation from it.

Because both .x and .y are properties, the input validation runs whenever you assign a value to either attribute. In the first example, the input values are valid numbers and the validation passes. In the final example, "one" isn’t a valid numeric value, so the validation fails.

If you look at your Point class, you’ll note that it follows a repetitive pattern, with the getter and setter methods looking quite similar. To avoid this repetition, you can use a descriptor instead of a property.

A descriptor is a class that implements the descriptor protocol, which consists of four special methods:

1. .__get__() runs when you access the attribute represented by the descriptor.
2. .__set__() runs when you use the attribute in an assignment statement.
3. .__delete__() runs when you use the attribute in a del statement.
4. .__set_name__() sets the attribute’s name, creating a name-aware attribute.

Here’s how your code may look if you use a descriptor to represent the coordinates of your Point class:

# point.py

class Coordinate:
    def __set_name__(self, owner, name):
        self._name = name

    def __get__(self, instance, owner):
        return instance.__dict__[self._name]

    def __set__(self, instance, value):
        try:
            instance.__dict__[self._name] = float(value)
        except ValueError:
            raise ValueError(f'"{self._name}" must be a number') from None

class Point:
    x = Coordinate()
    y = Coordinate()

You’ve removed repetitive code by defining Coordinate as a descriptor that manages the input validation in a single place. Go ahead and run the following code to try out the new implementation of Point:

>>> from point import Point

>>> point_1 = Point()
>>> point_1.x = 1
>>> point_1.y = 2
>>> point_1.x, point_1.y
1.0 2.0

>>> point_2 = Point()
>>> point_2.x = "one"
Traceback (most recent call last):
    ...
ValueError: "x" must be a number

Great! The class works as expected. Thanks to the Coordinate descriptor, you now have a more concise and non-repetitive version of your original code.

Another way to fine-tune the operations behind an assignment statement is to provide a custom implementation of .__setitem__() in your class. You’ll use this method in classes representing mutable data collections, such as custom list-like or dictionary-like classes.

As an example, say that you need to create a dictionary-like class that stores its keys in lowercase letters:

>>> from collections import UserDict

>>> class LowerCasedDict(UserDict):
...     def __setitem__(self, key, value):
...         key = key.lower()
...         super().__setitem__(key, value)
...

>>> numbers = LowerCasedDict()
>>> numbers["ONE"] = 1
>>> numbers["Two"] = 2
>>> numbers["Three"] = 3

>>> numbers
{'one': 1, 'two': 2, 'three': 3}

In this example, you create a dictionary-like class by subclassing UserDict from collections. Your class implements a .__setitem__() method, which takes key and value as arguments. The method uses str.lower() to convert key into lowercase letters before storing it in the underlying dictionary.

Python implicitly calls .__setitem__() every time you use a key as the left operand in an assignment statement. This behavior allows you to tweak how you process the assignment of keys in your custom dictionary.

Define or Call a Function

When you define a function, the def keyword implicitly assigns a function object to your function’s name. Here’s an example:

>>> def greet(name):
...    print(id(name))
...    print(f"Hello, {name}!")
...

>>> greet
<function greet at 0x105e9bb00>

>>> id(greet)
4394171136

>>> fellow = "Pythonista"
>>> greet(fellow)
4381781552
Hello, Pythonista!

>>> id(fellow)
4381781552

From this point on, the name greet refers to a function object that lives at a given memory address in your computer. You can call the function using its name and a pair of parentheses with appropriate arguments. This way, you can reuse greet() wherever you need it.

If you call your greet() function with fellow as an argument, then Python implicitly assigns the input argument value to the name parameter on the function’s definition. The parameter will hold a reference to the input arguments.

Work With Classes

When you define a class with the class keyword, you’re assigning a specific name to a class object. You can later use this name to create instances of that class. Consider the following example:

>>> class User:
...     def __init__(self, name, job):
...         self.name = name
...         self.job = job
...

>>> User
<class '__main__.User'>
>>> id(User)
5035278528

>>> john = User("John Doe", "Python Dev")
>>> john.name
'John Doe'
>>> john.job
'Python Dev'

In this example, the name User holds a reference to a class object, which was defined in __main__.User. Like with a function, when you call the class’s constructor with the appropriate arguments to create an instance, Python assigns the arguments to the parameters defined in the class initializer.

Another example of implicit assignments is the current instance of a class, which in Python is called self by convention. This name implicitly gets a reference to the current object whenever you instantiate a class. Thanks to this implicit assignment, you can access .name and .job from within the class without getting a NameError in your code.

Import Modules and Objects

Import statements are another variant of implicit assignments in Python. Through an import statement, you assign a name to a module object, class, function, or any other imported object. This name is then created in your current namespace so that you can access it later in your code:

>>> dir()
['__builtins__', '__doc__', ..., '__spec__', 'help']

>>> import sys
>>> dir()
['__builtins__', '__doc__', ..., '__spec__', 'help', 'sys']

>>> sys
<module 'sys' (built-in)>

In this example, you import the sys module object from the standard library and assign it to the sys name, which is now available in your namespace, as you can conclude from the second call to the built-in dir() function.

Use a Decorator

You also run an implicit assignment when you use a decorator in your code. The decorator syntax is just a shortcut for a formal assignment like the following:

func = decorator(func)

Here, you call decorator() with a function object as an argument. This call will typically add functionality on top of the existing function, func(), and return a function object, which is then reassigned to the func name.

The decorator syntax is syntactic sugar for replacing the previous assignment, which you can now write as follows:

@decorator
def func():
    # Implementation here...
    pass

Even though this new code looks pretty different from the above assignment, the code implicitly runs the same steps.

Access the Control Variable in a for Loop or a Comprehension

Another situation in which Python automatically runs an implicit assignment is when you use a for loop or a comprehension. In both cases, you can have one or more control variables that you then use in the loop or comprehension body:

>>> for control_variable in range(5):
...     print(f"{control_variable=} {id(control_variable)=}")
...
control_variable=0 id(control_variable)=4376944840
control_variable=1 id(control_variable)=4376944872
control_variable=2 id(control_variable)=4376944904
control_variable=3 id(control_variable)=4376944936
control_variable=4 id(control_variable)=4376944968

The memory address of control_variable changes on each iteration of the loop. This is because Python internally reassigns a new value from the loop iterable to the loop control variable on each cycle.

The same behavior appears in comprehensions:

>>> [
...     f"{control_variable=} {id(control_variable)=}"
...     for control_variable in range(5)
... ]
[
    'control_variable=0 id(control_variable)=4376944840',
    'control_variable=1 id(control_variable)=4376944872',
    'control_variable=2 id(control_variable)=4376944904',
    'control_variable=3 id(control_variable)=4376944936',
    'control_variable=4 id(control_variable)=4376944968'
]

In the end, comprehensions work like for loops but use a more concise syntax. This comprehension creates a new list of strings that mimic the output from the previous example.

Use the as Keyword

The as keyword in with statements, except clauses, and import statements is another example of an implicit assignment in Python. This time, the assignment isn’t completely implicit because the as keyword provides an explicit way to define the target variable.

In a with statement, the target variable that follows the as keyword will hold a reference to the context manager that you’re working with. As an example, say that you have a hello.txt file with the following content:

Hello, Pythonista!
Welcome to Real Python!

You want to open this file and print each of its lines on your screen. In this case, you can use the with statement to open the file using the built-in open() function.

In the example below, you accomplish this. You also add some calls to print() that display information about the target variable defined by the as keyword:

>>> with open("hello.txt", mode="r") as hello:
...     print(f"File object: {hello}")
...     print(f"Memory address: {id(hello)}")
...     print("File content:")
...     for line in hello:
...        print("> ", line)
...
File object: <_io.TextIOWrapper name='hello.txt' mode='r' encoding='UTF-8'>
Memory address: 4372378896
File content:
>  Hello, Pythonista!
>  Welcome to Real Python!

This with statement uses the open() function to open hello.txt. The open() function is a context manager that returns a text file object represented by an io.TextIOWrapper instance.

Since you’ve defined a hello target variable with the as keyword, now that variable holds a reference to the file object itself. You confirm this by printing the object and its memory address. Finally, the for loop iterates over the lines and prints this content to the screen.

When it comes to using the as keyword in the context of an except clause, the target variable will contain an exception object if any exception occurs:

>>> try:
...     5 / 0
... except ZeroDivisionError as error:
...     print(f"Exception: {error}")
...     print(f"Memory address: {id(error)}")
...
Exception: division by zero
Memory address: 4382188704

>>> error
Traceback (most recent call last):
    ...
NameError: name 'error' is not defined

In this example, you run a division that raises a ZeroDivisionError. The as keyword assigns the raised exception to error. Note that when you print the exception object, you get only the message because exceptions have a custom .__str__() method that supports this behavior.

There’s a final detail to remember when using the as specifier in a try … except block like the one in the above example. Once you leave the except block, the target variable goes out of scope, and you can’t use it anymore.

Finally, Python’s import statements also support the as keyword. In this context, you can use as to import objects with a different name:

>>> import numpy as np
>>> import pandas as pd

>>> dir()
['__builtins__', '__doc__', ..., 'help', 'np', 'pd']

In these examples, you use the as keyword to import the numpy package with the np name and pandas with the name pd. If you call dir(), then you’ll realize that np and pd are now in your namespace. However, the numpy and pandas names are not.

Using the as keyword in your imports comes in handy when you want to use shorter names for your objects or when you need to use different objects that originally had the same name in your code. It’s also useful when you want to make your imported names non-public using a leading underscore, like in import sys as _sys.

Access the _ Special Variable in an Interactive Session

The final implicit assignment that you’ll learn about in this tutorial only occurs when you’re using Python in an interactive session. Every time you run a statement that returns a value, the interpreter stores the result in a special variable denoted by a single underscore character (_).

You can access this special variable as you’d access any other variable:

>>> # Expressions
>>> 5 < 7
True
>>> _
True
>>> 12 + 30
42
>>> _
42

>>> # Function calls
>>> sum([1, 2, 3, 4])
10
>>> _
10
>>> print("Hello, Pythonista!")
Hello, Pythonista!
>>> _
10

>>> # Assignments
>>> counter = 0
>>> _
10

>>> # Variable accesses
>>> counter
0
>>> _
0

These examples cover several situations in which Python internally uses the _ variable. The first two examples evaluate expressions. Expressions always have a return value, which is automatically assigned to the _ variable every time.

When it comes to function calls, note that if your function returns a fruitful value, then _ will hold it. In contrast, if your function returns None, then the _ variable will remain untouched.

The next example consists of a regular assignment statement. As you already know, regular assignments don’t return any value, so the _ variable isn’t updated after these statements run. Finally, note that accessing a variable in an interactive session returns the value stored in the target variable. This value is then assigned to the _ variable.

Note that since _ is a regular variable, you can use it in other expressions:

>>> numbers = [1, 2, 3, 4]

>>> len(numbers)
4

>>> sum(numbers) / _
2.5

In this example, you first create a list of values. Then you call len() to get the number of values in the list. Python automatically stores this value in the _ variable. Finally, you use _ to compute the mean of your list of values.

Now that you’ve learned about some of the implicit assignments that Python runs under the hood, it’s time to dig into a final assignment-related topic. In the following few sections, you’ll learn about some illegal and dangerous assignments that you should be aware of and avoid in your code.

Keywords

You can’t use Python keywords as variable names in assignment statements. This kind of assignment is explicitly forbidden. If you try to use a keyword as a variable name in an assignment, then you get a SyntaxError:

>>> class = "Economy"
  File "<stdin>", line 1
    class = "Economy"
          ^
SyntaxError: invalid syntax

>>> global = 42
  File "<input>", line 1
    global = 42
           ^
SyntaxError: invalid syntax

Whenever you try to use a keyword as the left operand in an assignment statement, you get a SyntaxError. Keywords are an intrinsic part of the language and can’t be overridden.

If you ever feel the need to name one of your variables using a Python keyword, then you can append an underscore to the name of your variable:

>>> class_ = "Economy"
>>> class_
'Economy'

>>> global_ = 42
>>> global_
42

In this example, you’re using the desired name for your variables. Because you added a final underscore to the names, Python doesn’t recognize them as keywords, so it doesn’t raise an error.

>>> booking_class = "Economy"
>>> booking_class
'Economy'

You’ll also find that you can use only a few keywords as part of the right operand in an assignment statement. Those keywords will generally define simple statements that return a value or object. These include lambda, and, or, not, True, False, None, in, and is. You can also use the for keyword when it’s part of a comprehension and the if keyword when it’s used as part of a ternary operator.

In an assignment, you can never use a compound statement as the right operand. Compound statements are those that require an indented block, such as for and while loops, conditionals, with statements, try … except blocks, and class or function definitions.

Built-in Objects

Sometimes, you need to name variables, but the desired or ideal name is already taken and used as a built-in name. If this is your case, think harder and find another name. Don’t shadow the built-in.

Shadowing built-in names can cause hard-to-identify problems in your code. A common example of this issue is using list or dict to name user-defined variables. In this case, you override the corresponding built-in names, which won’t work as expected if you use them later in your code.

Consider the following example:

>>> list = [1, 2, 3, 4]

>>> squares = list(map(lambda x: x ** 2, [1, 2, 3, 4]))
Traceback (most recent call last):
    ...
TypeError: 'list' object is not callable

The exception in this example may sound surprising. How come you can’t use list() to build a list from a call to map() that returns a generator of square numbers?

By using the name list to identify your list of numbers, you shadowed the built-in list name. Now that name points to a list object rather than the built-in class. List objects aren’t callable, so your code no longer works.

In Python, you’ll have nothing that warns against using built-in, standard-library, or even relevant third-party names to identify your own variables. Therefore, you should keep an eye out for this practice. It can be a source of hard-to-debug errors.

Named Constants

In programming, a constant refers to a name associated with a value that never changes during a program’s execution. Unlike other programming languages, Python doesn’t have a dedicated syntax for defining constants. This fact implies that Python doesn’t have constants in the strict sense of the word.

Python only has variables. If you need a constant in Python, then you’ll have to define a variable and guarantee that it won’t change during your code’s execution. To do that, you must avoid using that variable as the left operand in an assignment statement.

To tell other Python programmers that a given variable should be treated as a constant, you must write your variable’s name in capital letters with underscores separating the words. This naming convention has been adopted by the Python community and is a recommendation that you’ll find in the Constants section of PEP 8.

In the following examples, you define some constants in Python:

>>> PI = 3.14
>>> MAX_SPEED = 300
>>> WIDTH = 20
>>> BASE_URL = "https://api.example.com"

The problem with these constants is that they’re actually variables. Nothing prevents you from changing their value during your code’s execution. So, at any time, you can do something like the following:

>>> PI = 3.141592653589793
>>> MAX_SPEED = 1_000

These assignments modify the value of two of your original constants. Python doesn’t complain about these changes, which can cause issues later in your code. As a Python developer, you must guarantee that named constants in your code remain constant.

The only way to do that is never to use named constants in an assignment statement other than the constant definition.