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.
# 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.
# 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.
| Feature | List Comprehension | Map Function |
|---|---|---|
| Syntax | Concise and readable for simple transformations. | Requires a function or lambda as the first argument. |
| Readability | Easy to read and understand for simple logic. | Can become complex with lambdas for simple tasks. |
| Output Type | Directly produces a list. | Returns an iterator (needs to be converted to a list). |
| Performance | Slightly slower for pre-defined functions. | Faster for pre-defined functions due to optimizations. |
| Memory Efficiency | Creates a list in memory directly. | Returns an iterator, which is memory efficient. |
| Custom Logic | Better for adding conditions or custom logic. | Limited to the function provided. |
| Use Case | Simple or custom transformations with conditions. | Applying pre-defined functions or handling large datasets. |