5 Best Ways to Split a List of Strings into Sublists in Python

Python developers often need to manipulate lists for various tasks. A common requirement might be to divide a single list of strings into several sublists based on specific criteria. For example, given a list of names, you might want to create sublists where each sublist includes names starting with the same letter. This article shows several ways to accomplish this in Python, catering to different use cases and preferences.

Method 1: Using List Comprehension

In this approach, list comprehension is used to iterate over the list and partition it into sublists based on a common characteristic or a condition. This is a quintessential Pythonic way to split lists and can be easily customized.

Here’s an example:

names = ["Alice", "Bob", "Charlie", "Anna", "Albert"]
sublists = [[name for name in names if name.startswith(char)] for char in "ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
sublists = [sublist for sublist in sublists if sublist]  # Remove empty sublists

The output:

[['Alice', 'Anna', 'Albert'], ['Bob'], ['Charlie']]

This snippet creates a sublist for each letter in the alphabet and filters out the names that start with the corresponding letter. Then, it removes any empty sublists from the result. It’s a clean and expressive one-liner that showcases Python’s list comprehension power.

Method 2: Using GroupBy from itertools

Python’s itertools library has a function called groupby() that can group items in a list. When combined with sorting, it can be used to efficiently split a list into sublists based on a key function.

Here’s an example:

from itertools import groupby
names = ["Alice", "Bob", "Charlie", "Anna", "Albert"]
# Ensure the list is sorted based on the criterion
names.sort(key=lambda name: name[0])
sublists = [list(group) for key, group in groupby(names, key=lambda name: name[0])]

The output:

[['Alice', 'Anna', 'Albert'], ['Bob'], ['Charlie']]

After sorting the names by their first letter, we use groupby() to create sublists of names that start with the same letter. Remember, groupby() only works properly if the list is sorted by the grouping criterion.

Method 3: Using a Dictionary

Another common approach for splitting a list into sublists is to use a dictionary to map a key to a list of strings. This method is particularly useful if you want to access sublists based on their keys later on.

Here’s an example:

names = ["Alice", "Bob", "Charlie", "Anna", "Albert"]
sublists = {}
for name in names:
    key = name[0]  # Use the first character as the key
    if key not in sublists:
        sublists[key] = []
    sublists[key].append(name)

sublists = list(sublists.values())

The output:

[['Alice', 'Anna', 'Albert'], ['Bob'], ['Charlie']]

Here, we iterate through all names, use the first character as a key, and append the name to the corresponding value in the dictionary. Finally, we extract the sublists as the dictionary’s values. It’s not as succinct as list comprehension but offers easier readability for some.

Method 4: Using a For Loop and Conditional Statements

If you prefer not to use Python-specific features like comprehensions or library functions, a more traditional approach with a for loop and conditionals can also be employed.

Here’s an example:

names = ["Alice", "Bob", "Charlie", "Anna", "Albert"]
sublists = [[]]

for name in names:
    inserted = False
    for sublist in sublists:
        if name[0] == sublist[0][0]:
            sublist.append(name)
            inserted = True
            break
    if not inserted:
        sublists.append([name])

sublists = [sublist for sublist in sublists if sublist]  # Remove any empty sublists

The output:

[['Alice', 'Anna', 'Albert'], ['Bob'], ['Charlie']]

This straightforward technique iterates through each name and either appends it to an existing sublist or creates a new sublist for the name if it does not fit into any existing sublists. Then, it removes any leftover empty sublists.

Bonus One-Liner Method 5: Using a Lambda Function with Reduce

The reduce function from the functools module can be used with a lambda function to efficiently process elements in a list and aggregate them into sublists. This is more advanced and may not be as self-explanatory as other methods but can be very efficient.

Here’s an example:

from functools import reduce
names = ["Alice", "Bob", "Charlie", "Anna", "Albert"]

sublists = reduce(lambda acc, name: acc[:-1] + [acc[-1] + [name]] if acc[-1] and name[0] == acc[-1][0][0] else acc + [[name]], names, [[]])

The output:

[['Alice', 'Anna', 'Albert'], ['Bob'], ['Charlie']]

This complex one-liner uses reduce() to accumulate names into sublists. The lambda checks whether the last character of the last added name matches the first character of the current name. If so, it appends the name to the last sublist; otherwise, it starts a new sublist.

Summary/Discussion

• Method 1: List Comprehension. Highly Pythonic and concise. Might be less readable for Python newcomers or for very complicated conditions.
• Method 2: Using groupby() from itertools. Efficient for ordered data. Requires data to be pre-sorted which could add overhead.
• Method 3: Using a Dictionary. Useful with key-based access later on. Easier to understand at the expense of being verbose.
• Method 4: Traditional for Loops and Conditionals. Straightforward and language-agnostic logic. Typically less efficient and more lines of code.
• Bonus Method 5: One-liner with Reduce. Compact and potentially the most efficient. Not very readable and can be confusing for those unfamiliar with reduce().