The Secret Life of Python: The Phantom Copy

Source: Dev.to

Why = doesn’t actually copy your data in Python.

Timothy stared at his screen, his face pale. “Margaret? I think I just accidentally deleted half the database.”

Margaret wheeled her chair over immediately, her voice calm. “Don’t panic. Tell me exactly what happened.”

“I was testing a script to clean up our user list,” Timothy explained. “I wanted to test it safely, so I made a copy of the list first. I thought if I messed up the copy, the original would be safe.”

He showed her the code:

# Timothy's Safety Plan

# The original list of critical users
users = ["Alice", "Bob", "Charlie", "Dave"]

# Create a "backup" copy to test on
test_group = users

# Timothy deletes 'Alice' from the test group
test_group.remove("Alice")

# Check the results
print(f"Test Group: {test_group}")
print(f"Original Users: {users}")

Output

Test Group: ['Bob', 'Charlie', 'Dave']
Original Users: ['Bob', 'Charlie', 'Dave']

Timothy slumped. “See? I removed Alice from the test_group, but she disappeared from the users list too! How is that possible? I touched the backup, not the original!”

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The Address, Not the House

Margaret studied the code. “This is one of the most common misunderstandings in Python. It comes down to how Python handles memory.”

She asked, “When you wrote test_group = users, what did you think that command did?”

Timothy replied, “I thought it created a new list. I thought it took all the names from users and copied them into a new variable named test_group.”

“Python takes a shortcut for efficiency,” Margaret explained. “Copying data takes time and memory, so instead of copying the house, Python just copies the address.”

She drew a simple diagram: the variable users points to a box containing the list of names. When you assign test_group = users, you’re giving the second variable the same address; both names refer to the exact same object.

“So users and test_group are just two different names for the exact same object?”
“Exactly,” Margaret smiled. “It’s like sharing a document via a link—any change you make is seen by everyone with that link.”

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Breaking the Link

Timothy asked, “So how do I actually make a copy? I want a separate box.”

“We have to be explicit,” Margaret said. “Tell Python to take the data and build a new list.”

She showed him the .copy() method:

# Margaret's Fix: Explicit Copying

users = ["Alice", "Bob", "Charlie", "Dave"]

# .copy() creates a brand new list with the same data
test_group = users.copy()

test_group.remove("Alice")

print(f"Test Group: {test_group}")
print(f"Original Users: {users}")

Output

Test Group: ['Bob', 'Charlie', 'Dave']
Original Users: ['Alice', 'Bob', 'Charlie', 'Dave']

Timothy breathed a sigh of relief. “Alice is safe.”

Margaret added a warning: .copy() makes a shallow copy. If the list contains other mutable objects (e.g., inner lists), those inner objects are still shared. For a simple list of names, a shallow copy is sufficient.

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Margaret’s Cheat Sheet

• The Trap: Assuming new_list = old_list creates a copy.
• The Reality: Assignment (=) creates a reference (a nickname), not a copy. Both variables point to the same object.
• Why: Python does this to save memory and improve speed.

The Fix

• Shallow Copy: new_list = old_list.copy() (standard way).
• Slicing: new_list = old_list[:] (older, but common).

The Check

You can verify whether two variables reference the same object:

id(a) == id(b)   # True if a and b are the same object

Timothy made a note in his editor. “I’ll never assume = means ‘copy’ again.”

“It’s a rite of passage,” Margaret assured him. “Every Python developer learns this lesson the hard way. Better to learn it on a test script than on the production database.”