Keyword arguments in Python - Time & Space Complexity

Time Complexity: Keyword arguments

O(n)

Understanding Time Complexity

Let's explore how using keyword arguments affects the time it takes for a function to run.

We want to know if naming arguments changes how long the program works as input grows.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

def greet(name, greeting="Hello", punctuation="!"):
    print(f"{greeting}, {name}{punctuation}")

for person in ["Alice", "Bob", "Charlie"]:
    greet(name=person, punctuation=".", greeting="Hi")

This code defines a function with keyword arguments and calls it multiple times with named parameters.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The for-loop calls the greet function once per person.
• How many times: The loop runs 3 times, once for each name in the list.

How Execution Grows With Input

Explain the growth pattern intuitively.

Input Size (n)	Approx. Operations
10	10 calls to greet
100	100 calls to greet
1000	1000 calls to greet

Pattern observation: The number of function calls grows directly with the number of names.

Final Time Complexity

Time Complexity: O(n)

This means the time to run grows in a straight line as the number of inputs increases.

Common Mistake

[X] Wrong: "Using keyword arguments makes the function slower in a way that changes the overall time complexity."

[OK] Correct: Keyword arguments only affect how arguments are matched inside the function, which is very fast and does not change how the total work grows with input size.

Interview Connect

Understanding how function calls scale helps you explain your code clearly and reason about performance in real projects.

Self-Check

"What if the greet function had a loop inside that printed multiple times? How would the time complexity change?"