When working with lists in Python, it might seem logical to remove items from a list as you loop through it. However, this often leads to confusing results , skipped items, incomplete deletions, or even bugs that are hard to track down.

Let’s look at why this happens, how to avoid it, and what the best practices are for safely modifying lists during iteration.

Description of the Problem

You’re trying to remove items from a list while looping over it, but the result is not what you expected. For example:

items = [1, 2, 3, 4, 5]
for item in items: if item % 2 == 0: items.remove(item)
print(items)
  • Expected output: [1, 3, 5]
  • Actual output: [1, 3, 5] — So far, so good.

Now try this with a longer list:

items = [0, 1, 2, 3, 4, 5, 6]
for item in items: if item % 2 == 0: items.remove(item)
print(items)

Output: [1, 3, 5] — But wait, it skipped over some numbers!

Why This Happens

Modifying a list in-place while iterating over it changes the list’s length and shifts its indexes, which confuses the iterator.

Here’s what happens step-by-step in the second example:

  1. Iteration starts with index 0 → item = 0 → removed.
  2. The list shifts left: [1, 2, 3, 4, 5, 6]
  3. Next iteration → index 1 → item = 2 (skipped 1!)
  4. 2 is removed → list becomes [1, 3, 4, 5, 6]
  5. Iteration continues, skipping more elements…

This happens because the loop’s internal counter doesn’t know the list has changed.

How to Solve This Issue?

Step 1: Avoid In-Place Modification During Iteration

Instead of removing from the original list, create a new list:

items = [0, 1, 2, 3, 4, 5, 6]
new_items = [item for item in items if item % 2 != 0]
print(new_items) # [1, 3, 5]

This approach is clean, readable, and avoids side effects.

Step 2: Iterate Over a Copy of the List

If you need to modify the original list, iterate over a copy of it:

for item in items[:]: # Slice creates a shallow copy if item % 2 == 0: items.remove(item)
print(items) # Now works as expected

This way, the loop is not affected by changes to the original list.

Step 3: Use filter() When Applicable

For simple filtering, Python’s built-in filter() function works well:

items = list(filter(lambda x: x % 2 != 0, items))

Step 4: Use del Carefully with Index-Based Loops

Sometimes, using indexes is safer if you need to delete items:

i = 0
while i < len(items): if items[i] % 2 == 0: del items[i] else: i += 1

But this is more error-prone and should be avoided unless necessary.

Quick Recap

Modifying a list during iteration is dangerous because it changes the list’s structure while the loop is running — leading to skipped elements or logic errors.

Best Practices:

  • Don’t modify lists while looping over them directly
  • Use list comprehensions or filter() to build new lists
  • If you must change the original list, iterate over a copy (items[:])
  • Always check edge cases when dealing with in-place mutations

Conclusion

Changing a list while you’re looping through it is a common source of bugs in Python. It may seem like it works at first, but it can quickly cause unexpected behavior in more complex cases. To write safer, more predictable code, stick to cleaner methods like creating a new list or looping over a copy. For larger projects or critical data handling, it may be helpful to hire Python engineers who know how to avoid these pitfalls and write efficient, bug-free logic.