Python Language Tutorial => List comprehensions

Introduction

List comprehensions in Python are concise, syntactic constructs. They can be utilized to generate lists from other lists by applying functions to each element in the list. The following section explains and demonstrates the use of these expressions.

Syntax

[x + 1 for x in (1, 2, 3)] # list comprehension, gives [2, 3, 4]
(x + 1 for x in (1, 2, 3)) # generator expression, will yield 2, then 3, then 4
[x for x in (1, 2, 3) if x % 2 == 0] # list comprehension with filter, gives [2]
[x + 1 if x % 2 == 0 else x for x in (1, 2, 3)] # list comprehension with ternary
[x + 1 if x % 2 == 0 else x for x in range(-3,4) if x > 0] # list comprehension with ternary and filtering
{x for x in (1, 2, 2, 3)} # set comprehension, gives {1, 2, 3}
{k: v for k, v in [('a', 1), ('b', 2)]} # dict comprehension, gives {'a': 1, 'b': 2} (python 2.7+ and 3.0+ only)
[x + y for x in [1, 2] for y in [10, 20]] # Nested loops, gives [11, 21, 12, 22]
[x + y for x in [1, 2, 3] if x > 2 for y in [3, 4, 5]] # Condition checked at 1st for loop
[x + y for x in [1, 2, 3] for y in [3, 4, 5] if x > 2] # Condition checked at 2nd for loop
[x for x in xrange(10) if x % 2 == 0] # Condition checked if looped numbers are odd numbers

Remarks

Comprehensions are syntactical constructs which define data structures or expressions unique to a particular language. Proper use of comprehensions reinterpret these into easily-understood expressions. As expressions, they can be used:

in the right hand side of assignments
as arguments to function calls
in the body of a lambda function
as standalone statements. (For example: [print(x) for x in range(10)])

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