How can I use list comprehensions to process a nested list?

python

list-comprehensions

nested-lists

data-processing

byAlex Kataev·Sep 29, 2024

Use a list comprehension to work with a nested list by iterating over sublists and their items and applying required operations:

# The good old flatten-a-nested-list trick. Nothing up my sleeves!
flat_list = [item for sublist in nested_list for item in sublist]

Apply functions or condition checks directly:

# Squirtle I choose you, 'function'! Use Transform!
processed = [function(item) for sublist in nested_list for item in sublist]

# If I had a nickel for every 'item_condition' that was true...
filtered = [item for sublist in nested_list for item in sublist if item_condition(item)]

Here are your handy-dandy tools:

Double the for loops, double the fun! Use two for loops in one comprehension for nested iterations.
Optional inline if for playing favorites with your processing.

A good understanding of list comprehensions can boost both efficiency and readability of your code. Replacing nested loops with comprehensions for tasks like flattening a list or transforming its elements can make your code lean, mean, and serpentine in its elegance.

Comprehensions, assemble!

Maintaining sublist structure

If you want to apply a function to each item but keep the structure of your sublists, you can do this:

# It's morphing time! Apply 'transformation' to each item within a sublist.
transformed = [[transformation(item) for item in sublist] for sublist in nested_list]

Putting sublists under the microscope

You can use conditions to pick and choose entire sublists to involve in your scheming:

# Some sublists are more equal than the others, based on the 'sublist_condition'.
conditional_nested_list = [
    [transformation(item) for item in sublist] 
    for sublist in nested_list if sublist_condition(sublist)
]

Mix and match with combinations

Create combinations of elements from different sublists:

# Why not both? I say, Porque no los dos?
combinations = [(x, y) for x in nested_list[0] for y in nested_list[1]]

Sanity checks and cautious whispers

Efficiency: List comprehensions are often faster than for-loop counterparts. They're like the Flash of Python operations.
Readability: Remember, with great power comes great responsibility. Don't go crazy with complexity.
Memory: These guys are resident in memory. For larger lists, you may want to consider generator expressions that are more energy-efficient.

Knowing these nuances can save you from many an ambush.

Trade tricks and practical spells

Data Processing

Data scientists, unite! Nested list comprehensions can do wonders during the preprocessing phase:

# You, a regular coder: min-max normalization. 
# Me, an intellectual: Z-score normalization!
standardized_data = [
    [(x - mean(sublist)) / std(sublist) for x in sublist] 
    for sublist in data
]

Matrix operations

Transpose operations without having to import numpy or pandas:

# Transposing in 5, 4, 3, matrix...
transposed_matrix = [
   [row[i] for row in matrix] 
   for i in range(len(matrix[0]))
]

Flat list from irregular nested lists

Life ain't always fair. Neither are lists:

# Irregular lists, your reign of terror ends here!
flat_list = [
    item 
    for sublist in nested_list 
    for item in (sublist if isinstance(sublist, list) else [sublist])
]