Flatten Lists in Seconds: Python Tricks to Simplify Your Data Wrangling
Have you ever struggled with nested lists in Python? Maybe you’ve written tedious nested loops to extract data, only to end up with messy, hard-to-read code. If so, you’re not alone—nested lists are a common headache, especially when working with JSON responses, spreadsheet data, or multi-layered datasets.
The good news? Python has two clever one-liners that can flatten lists in seconds:
- The
sum()Hack:flat_list = sum(nested_list, []) - List Comprehension:
flat_list = [item for sublist in nested_list for item in sublist]
No more nested loops or complicated functions! Let’s break down how these methods work, when to use them, and which one might be best for your next project.
Why Flatten Lists? The Real-World Problem
Imagine you’re analyzing survey results stored as:
python
responses = [
["apple", "banana"],
["orange", "grape", "melon"],
["kiwi"]
]
To count all fruits, you’d need a single list like ["apple", "banana", "orange", ...]. Manually looping through sublists is time-consuming. That’s where flattening shines!
Method 1: The sum() Trick
At first glance, sum() seems like a math-only function. But in Python, you can use it to concatenate lists:
```python nested_list = [[1, 2], [3, 4], [5]] flat_list = sum(nested_list, [])
Result: [1, 2, 3, 4, 5]
```
How It Works:
- The sum() function starts with an initial value (here, an empty list []).
- It iteratively +’s each sublist to it, merging them into one.
Pros:
- Extremely short and readable.
- Great for small to medium-sized lists.
Cons:
- Slow for large lists (due to how + creates new lists each time).
- Feels "hacky"—may confuse beginners.
Method 2: List Comprehension
The classic Pythonic way:
python
nested_list = [[1, 2], [3, 4], [5]]
flat_list = [item for sublist in nested_list for item in sublist]
How It Works:
- The outer loop (for sublist in nested_list) grabs each sublist.
- The inner loop (for item in sublist) extracts individual items.
Pros:
- Faster for large lists (no intermediate list creations).
- Explicit and flexible—you can add conditions (e.g., if item > 3).
Cons:
- Slightly harder to read for beginners (nested order feels backward).
Performance Showdown
For tiny lists, both methods are negligible. But for 10,000+ items, list comprehension wins:
```python import timeit
nested_list = [[i] for i in range(10000)]
Time sum()
timeit.timeit(lambda: sum(nested_list, []), number=1000)
~2.3 seconds
Time list comprehension
timeit.timeit(lambda: [item for sublist in nested_list for item in sublist], number=1000)
~0.4 seconds
```
Verdict: Use sum() for quick scripts; switch to list comprehension for scalability.
Bonus: Other Ways to Flatten
itertools.chain(): Best for memory efficiency with huge datasets.
python from itertools import chain list(chain.from_iterable(nested_list))functools.reduce(): Functional programming style (but less readable).
Which Method Should You Use?
- For quick tasks:
sum(nested_list, [])(it’s cute and works!). - For production code: List comprehension (faster and more explicit).
- For gigantic data:
itertools.chain()(memory-friendly).
Final Tip: Save This Trick!
Next time you’re wrangling JSON API responses or cleaning Excel data, try flattening with these one-liners. No more nested loops—just clean, efficient Python!
Which method do you prefer? Let me know if you’ve got another favorite trick! 🚀
Call to Action:
- Try it now: Flatten a nested list in your current project and see the time saved!
- Share this with a teammate who’s still writing nested loops. 😉