The Secret Life of Python: The Infinite Copy

The rain had stopped, leaving the library in a heavy, quiet silence. The only sound was the whirring of the cooling fans on Timothy’s laptop. They were spinning at maximum speed.

Timothy stared at his screen, drumming his fingers on the oak table. "It’s still running," he sighed.

Margaret looked up from her ledger. "What is?"

"I'm just building a CSV report," Timothy said defensively. "I have a list of one hundred thousand log entries. I’m just looping through them and combining them into one long string to save to a file. It should be instant, but it’s been running for two minutes."

Margaret stood and walked behind his chair. "Show me the loop."

Timothy pointed to the code block that seemed innocent enough:

# Timothy's Slow Code
log_entries = ["Entry 1", "Entry 2", "Entry 3", ...] # Imagine 100,000 items
final_report = ""

for entry in log_entries:
    final_report += entry + ","

Margaret adjusted her glasses. "Ah," she said softly. "You are falling victim to the Quadratic Copy."

The Cost of Immutability

"I don't see the problem," Timothy said. "I'm just adding to the string. += is standard, isn't it?"

"Remember the statue?" Margaret asked, pointing to the Roman bust in the corner. "Remember how we proved that strings are immutable? That you cannot change them, only create new ones?"

"Yes..."

"Think about what you are asking the computer to do inside that loop," Margaret said. She grabbed a marker and drew a diagram on the whiteboard.

Iteration 1:

• Python takes "Entry 1".
• It creates a new string object.

Iteration 2:

• Python takes the new string.
• It adds "Entry 2".
• It creates a brand new string object containing both.
• It destroys the first one.

Iteration 100,000:

• Python has to copy the entire massive report it has built so far, add one tiny entry, and create a new massive object.

"You aren't just adding a brick to a wall," Margaret explained. "You are building a wall. Then, to add one brick, you are tearing the whole wall down and rebuilding it one brick wider. Then you tear that down and rebuild it again. You are doing this one hundred thousand times."

Timothy looked horrified. "That sounds... exhausting."

"It is," Margaret agreed. "It is an immense waste of memory and time. You are copying millions of characters just to add a few."

The "Join" Solution

"So how do I stop rebuilding the wall?"

"You collect all the bricks first," Margaret said. "And then you hire a mason to build the wall exactly once."

She leaned over and deleted his loop. In its place, she typed a single, elegant line.

# Margaret's Optimized Code
final_report = ",".join(log_entries)

"That's it?" Timothy asked.

"That is the .join() method," Margaret said. "It calculates the total size needed for the final string upfront. Then, it allocates the memory once and slots everything in."

She pointed to the screen. "And look at your original loop. You were adding a trailing comma after every single item, which you would have to strip off later. The .join() method is smarter—it places the separator only between the items, so the result is perfect."

The Speed Test

Timothy looked skeptical that such a small change could matter that much. "Prove it," he said.

"Gladly," Margaret replied. She quickly wrote a script using Python’s timeit library to race the two methods against each other.

import timeit

setup_code = """
items = [str(i) for i in range(100000)]
"""

# The "Wall Rebuilder" (Timothy's Loop)
stmt_loop = """
result = ""
for item in items:
    result += item + ","
"""

# The "Master Mason" (Margaret's Join)
stmt_join = """
result = ",".join(items)
"""

print("Loop Time:", timeit.timeit(stmt_loop, setup=setup_code, number=10))
print("Join Time:", timeit.timeit(stmt_join, setup=setup_code, number=10))

She hit run. The results appeared instantly.

Loop Time: 3.452 seconds
Join Time: 0.041 seconds

Timothy stared at the numbers, the conceptual "wall" analogy suddenly feeling very, very heavy.

"The loop took 3 seconds," he murmured. "The join took... 40 milliseconds? That’s nearly 100 times faster."

"And the gap widens the more data you have," Margaret added. "With a million items, your loop might take hours. The join would take seconds."

Margaret’s Cheat Sheet

Margaret returned to her desk and scribbled a new entry in her notebook.

• Avoid += in Loops: Because strings are immutable, += forces Python to copy the entire string history for every single iteration.
• Use .join(): This method allocates memory once. It is the efficient, Pythonic way to combine an iterable (like a list) of strings.
• Smart Separators: .join() only puts the separator between items, avoiding the "trailing comma" bug common in loops.
• The Syntax: separator.join(iterable).
• Correct: ", ".join(["A", "B", "C"]) → "A, B, C"
• Correct: "".join(["A", "B", "C"]) → "ABC" (Empty string separator)

"So," Timothy said, rewriting his report script. "I'm not building 100,000 statues anymore."

"No," Margaret smiled, watching the fans on his laptop slow down to a whisper. "You are building one masterpiece, and you are doing it on the first try."

---

In the next episode, Margaret and Timothy will face "The Truth About Nothing"—investigating why some things in Python are True, while others are just... Empty.

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Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.

Comments

[shemith mohanan]

This is beautifully written 👌
The storytelling makes a classic Python performance pitfall instantly intuitive — the “rebuilding the wall” analogy is perfect. Clear explanation, real benchmarks, and a practical takeaway (.join() over +=) all in one smooth narrative. This is exactly the kind of article that sticks with readers long after they finish it.

[Aaron Rose]

Thanks shemith. Cheers ❤️🌹