5 Best Ways to Flatten a Grouped List in Python

💡 Problem Formulation: Python developers often encounter datasets where information is segmented into grouped lists, such as [[1, 2], [3, 4, 5], [6]]. The goal is to flatten these lists into a single list, like [1, 2, 3, 4, 5, 6], while maintaining the order of elements. This article demonstrates five effective methods for achieving this grouped flattening.

Method 1: Using a Nested Loop

This traditional approach employs a for-loop within another for-loop to iterate through each sublist and extract its elements. It’s highly readable and doesn’t require importing additional libraries.

Here’s an example:

result = []
grouped_list = [[1, 2], [3, 4, 5], [6]]
for sublist in grouped_list:
    for item in sublist:
        result.append(item)

The output will be [1, 2, 3, 4, 5, 6].

This code snippet first initializes an empty list called result. It then iterates over sublists in the grouped_list and appends each element it encounters into the result, effectively flattening the structure.

Method 2: Using List Comprehension

List comprehension offers a concise way to create lists in Python. This method compresses the nested loop from the first method into a single line of code that is still quite readable, which is especially handy for small data transformation tasks.

Here’s an example:

grouped_list = [[1, 2], [3, 4, 5], [6]]
flattened_list = [item for sublist in grouped_list for item in sublist]

The output will be [1, 2, 3, 4, 5, 6].

The code snippet achieves flattening through a single line that uses list comprehension. The expression iterates over each sublist in the grouped_list and then iterates over each item within those sublists, creating a new flat list.

Method 3: Using the itertools.chain() Function

The itertools module in Python provides a chain() function that is specifically designed to handle iterable flattening. It is efficient and can handle large datasets without consuming excessive memory.

Here’s an example:

from itertools import chain

grouped_list = [[1, 2], [3, 4, 5], [6]]
flattened_list = list(chain(*grouped_list))

The output will be [1, 2, 3, 4, 5, 6].

This code snippet uses chain() from the itertools module, unpacking the grouped_list with the * operator right inside the function call. Resulting iterators are then converted to a list, giving a flattened list.

Method 4: Using itertools.chain.from_iterable()

The chain.from_iterable() method from the itertools module is an alternative to chain() that takes an iterable of iterables. This is ideal when you already have a list of lists and want a clean, readable way to flatten it.

Here’s an example:

from itertools import chain

grouped_list = [[1, 2], [3, 4, 5], [6]]
flattened_list = list(chain.from_iterable(grouped_list))

The output will be [1, 2, 3, 4, 5, 6].

This snippet uses chain.from_iterable(), which takes the grouped_list directly as an argument, avoiding the unpacking operator. This can be more readable and avoids an extra step compared to the simple chain() method.

Bonus One-Liner Method 5: Using sum() with a generator expression

If you want to impress with a one-liner, you can use Python’s sum() function combined with a generator expression, which will concatenate lists inside the grouped_list. This method is less common and might be less intuitive for some developers.

Here’s an example:

grouped_list = [[1, 2], [3, 4, 5], [6]]
flattened_list = sum(grouped_list, [])

The output will be [1, 2, 3, 4, 5, 6].

The code snippet effectively uses the sum() function, which, while typically used for numeric addition, can concatenate lists when provided with an empty list as the start value. Be cautious, as this may not be the most efficient method for very large lists.

Summary/Discussion

• Method 1: Nested Loops. Intuitive and explicit. Can be slow for very large lists.
• Method 2: List Comprehension. Compact and Pythonic. Can still be inefficient with memory for significant data volume.
• Method 3: itertools.chain(). Highly efficient and memory-friendly. Requires understanding of iterators and the itertools module.
• Method 4: itertools.chain.from_iterable(). Clean syntax and efficient. Same benefits and considerations as Method 3.
• Bonus Method 5: Using sum(). Neat one-liner. Generally less efficient, particularly with large datasets.