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Map vs List comprehension - Python

Last Updated : 12 Jul, 2025
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List comprehension and map() both transform iterables but differ in syntax and performance. List comprehension is concise as the logic is applied in one line while map() applies a function to each item and returns an iterator and offering better memory efficiency for large datasets.

List comprehension

List comprehension is a simple way to create new lists by applying transformations or filtering elements from an existing iterable. It is concise, readable, and ideal for simple operations.

Python 
# Doubling each number in `li`
li = [1, 2, 3, 4]
res = [x * 2 for x in li]
print(res)

Output
[2, 4, 6, 8]

Explanation: [x * 2 for x in numbers] iterates through each item in li. and x * 2 applies the transformation to each element.

map() in python

map() function applies a specified function to each element of an iterable and producing a map object. The result can be converted to a list, tuple or other data structures if needed.

Python 
# Doubling each number in `li`
li = [1, 2, 3, 4]
res = map(lambda x: x * 2, li)
print(list(res))

Output
[2, 4, 6, 8]

Explanation:

  • lambda x: x * 2 function that doubles the element.
  • map(lambda x: x * 2, numbers) applies the lambda function to each element in numbers.
  • list(result) converts the map object to a list.

Difference between Map and List Comprehension

Here are some key differences between map and list comprehension.

FeatureList ComprehensionMap Function
SyntaxConcise and readable for simple transformations.Requires a function or lambda as the first argument.
ReadabilityEasy to read and understand for simple logic.Can become complex with lambdas for simple tasks.
Output TypeDirectly produces a list.Returns an iterator (needs to be converted to a list).
PerformanceSlightly slower for pre-defined functions.Faster for pre-defined functions due to optimizations.
Memory EfficiencyCreates a list in memory directly.Returns an iterator, which is memory efficient.
Custom LogicBetter for adding conditions or custom logic.Limited to the function provided.
Use CaseSimple or custom transformations with conditions.Applying pre-defined functions or handling large datasets.

A

akritigoswami
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