Creating objects in Python - Performance & Efficiency

Time Complexity: Creating objects

O(n)

Understanding Time Complexity

When we create objects in Python, it takes some time to set up each one.

We want to know how the time needed grows as we make more objects.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

class Item:
    def __init__(self, value):
        self.value = value

items = []
n = 10  # Example value for n
for i in range(n):
    items.append(Item(i))
    

This code creates a list of n objects, each holding a value.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Creating a new object inside the loop.
• How many times: Exactly n times, once per loop cycle.

How Execution Grows With Input

Each new object takes a little time, so total time grows as we add more.

Input Size (n)	Approx. Operations
10	About 10 object creations
100	About 100 object creations
1000	About 1000 object creations

Pattern observation: The time grows directly with the number of objects created.

Final Time Complexity

Time Complexity: O(n)

This means if you double the number of objects, the time to create them roughly doubles too.

Common Mistake

[X] Wrong: "Creating many objects happens instantly, so time doesn't grow with more objects."

[OK] Correct: Each object needs some setup time, so more objects mean more total time.

Interview Connect

Understanding how object creation time grows helps you write efficient code and explain your choices clearly.

Self-Check

"What if we created objects inside a nested loop instead? How would the time complexity change?"