How (Not) To Use Python’s Walrus Operator

In Python, if you wish to assign values to variables inside an expression, you should use the Walrus operator :=. Whereas x = 5 is a straightforward variable project, x := 5 is how you may use the Walrus operator.

This operator was launched in Python 3.8 and will help you write extra concise and probably extra readable code (in some instances). Nonetheless, utilizing it when not crucial or greater than crucial also can make code more durable to grasp.

On this tutorial, we’ll discover each the efficient and the not-so-effective makes use of of the Walrus operator with easy code examples. Let’s get began!

How and When Python’s Walrus Operator is Useful

Let’s begin by examples the place the walrus operator could make your code higher.

1. Extra Concise Loops

It is fairly frequent to have loop constructs the place you learn in an enter to course of inside the loop and the looping situation relies on the enter. In such instances, utilizing the walrus operator helps you retain your loops cleaner.

With out Walrus Operator

Think about the next instance:

information = enter("Enter your data: ")
whereas len(information) > 0:
    print("You entered:", information)
    information = enter("Enter your data: ")

If you run the above code, you’ll be repeatedly prompted to enter a price as long as you enter a non-empty string.

Word that there’s redundancy. You initially learn within the enter to the information variable. Inside the loop, you print out the entered worth and immediate the person for enter once more. The looping situation checks if the string is non-empty.

With Walrus Operator

You may take away the redundancy and rewrite the above model utilizing the walrus operator. To take action, you possibly can learn within the enter and verify if it’s a non-empty string—all inside the looping situation—utilizing the walrus operator like so:

whereas (information := enter("Enter your data: ")) != "":
    print("You entered:", information)

Now that is extra concise than the primary model.

2. Higher Listing Comprehensions

You’ll generally have perform calls inside record comprehensions. This may be inefficient if there are a number of costly perform calls. In these instances, rewriting utilizing the walrus operator could be useful.

With out Walrus Operator

Take the next instance the place there are two calls to the `compute_profit` perform within the record comprehension expression:

• To populate the brand new record with the revenue worth and
• To verify if the revenue worth is above a specified threshold.

# Operate to compute revenue
def compute_profit(gross sales, price):
	return gross sales - price

# With out Walrus Operator
sales_data = [(100, 70), (200, 150), (150, 100), (300, 200)]
income = [compute_profit(sales, cost) for sales, cost in sales_data if compute_profit(sales, cost) > 50]

With Walrus Operator

You may assign the return values from the perform name to the `revenue` variable and use that the populate the record like so:

# Operate to compute revenue
def compute_profit(gross sales, price):
	return gross sales - price

# With Walrus Operator
sales_data = [(100, 70), (200, 150), (150, 100), (300, 200)]
income = [profit for sales, cost in sales_data if (profit := compute_profit(sales, cost)) > 50]

This model is healthier if the filtering situation entails an costly perform name.

How To not Use Python’s Walrus Operator

Now that we’ve seen a couple of examples of how and when you should use Python’s walrus operator, let’s see a couple of anti-patterns.

1. Advanced Listing Comprehensions

We used the walrus operator inside a listing comprehension to keep away from repeated perform calls in a earlier instance. However overusing the walrus operator could be simply as unhealthy.

The next record comprehension is difficult to learn attributable to a number of nested situations and assignments.

# Operate to compute revenue
def compute_profit(gross sales, price):
    return gross sales - price

# Messy record comprehension with nested walrus operator
sales_data = [(100, 70), (200, 150), (150, 100), (300, 200)]
outcomes = [
	(sales, cost, profit, sales_ratio)
	for sales, cost in sales_data
	if (profit := compute_profit(sales, cost)) > 50
	if (sales_ratio := sales / cost) > 1.5
	if (profit_margin := (profit / sales)) > 0.2
]

As an train, you possibly can attempt breaking down the logic into separate steps—inside a daily loop and if conditional statements. This can make the code a lot simpler to learn and keep.

2. Nested Walrus Operators

Utilizing nested walrus operators can result in code that’s tough to each learn and keep. That is notably problematic when the logic entails a number of assignments and situations inside a single expression.

# Instance of nested walrus operators 
values = [5, 15, 25, 35, 45]
threshold = 20
outcomes = []

for worth in values:
    if (above_threshold := worth > threshold) and (incremented := (new_value := worth + 10) > 30):
        outcomes.append(new_value)

print(outcomes)

On this instance, the nested walrus operators make it obscure—requiring the reader to unpack a number of layers of logic inside a single line, lowering readability.

Wrapping Up

On this fast tutorial, we went over how and when to and when to not use Python’s walrus operator. You’ll find the code examples on GitHub.

If you happen to’re in search of frequent gotchas to keep away from when programming with Python, learn 5 Widespread Python Gotchas and Tips on how to Keep away from Them.

Preserve coding!

Bala Priya C is a developer and technical author from India. She likes working on the intersection of math, programming, information science, and content material creation. Her areas of curiosity and experience embody DevOps, information science, and pure language processing. She enjoys studying, writing, coding, and low! At present, she’s engaged on studying and sharing her data with the developer group by authoring tutorials, how-to guides, opinion items, and extra. Bala additionally creates partaking useful resource overviews and coding tutorials.