List Comprehensions in Python

Python list comprehension provides a concise way of writing code, which allows you to simultaneously calculate the value of an expression and assign it to a variable. Using the walrus operator (:=), we can optimize the code:

square_cubes = [res if (res := n**2) % 9 == 0 or res % 4 == 0 else n**3 for n in range(1, 11)]
print(square_cubes)  # 输出: [1, 4, 9, 16, 125, 36, 343, 64, 81, 100]

Here, we store the res variables to store the calculation results n**2 and reuse them in subsequent code to avoid repeated calculations.

List comprehension of nested loops

List comprehension supports nested loops, and there is no limit on the number of loops. But it should be noted that the loop sequence must be consistent with the original code. You can also add optional for conditions after each for cycle. The list comprehension structure of nested if loops is as follows: for

[ <表达式> for <元素a> in <可迭代对象a> (可选 if <条件a>)
              for <元素b> in <可迭代对象b> (可选 if <条件b>)
              for <元素c> in <可迭代对象c> (可选 if <条件c>)
              ... ]

The following example demonstrates a list comprehension of nested loops for generating multiplication tables:

multiplications = []
for i in range(1, 4):
    for n in range(1, 11):
        multiplications.append(i*n)
print(multiplications) # 输出: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30]

Convert it to a list comprehension:

multiplications = [i*n for i in range(1,4) for n in range(1,11)]
print(multiplications) # 输出: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30]

List comprehension can also be used to flatten nested lists:

matrix = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12],
]
flatten = [n for row in matrix for n in row]
print(flatten) # 输出: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

Nested list comprehension

Nested list comprehension is different from the list comprehension of nested loops. The former is a deduced internal nested derivation, while the latter is a loop internal nested loop. For example, matrix transpose:

Use normal loop:

matrix = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12],
]
transpose = []
for i in range(4):
    temp = []
    for row in matrix:
        temp.append(row[i])
    transpose.append(temp)
print(transpose) # 输出: [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

Use nested list comprehension:

matrix = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9, 10, 11, 12],
]
transpose = [[row[n] for row in matrix] for n in range(4)]
print(transpose) # 输出: [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

Set and dictionary derivation

The concept of list comprehension also applies to set and dictionary comprehensions. Dictionary is used to store key-value pairs:

squares_cubes = {n: n**2 if n%2 == 0 else n**3 for n in range(1,11)}
print(squares_cubes) # 输出: {1: 1, 2: 4, 3: 27, 4: 16, 5: 125, 6: 36, 7: 343, 8: 64, 9: 729, 10: 100}

Set derivation is used to create unordered sets:

import random
non_multiples = {n for n in random.sample(range(0, 1001), 20) if n not in range(0, 1001, 9)}
print(non_multiples) # 输出 (示例): {3, 165, 807, 574, 745, 266, 616, 44, 12, 910, 336, 145, 755, 179, 25, 796, 926}

Summary

This article introduces Python list comprehensions and their applications in code optimization, including nested loops, nested derivations, and collection and dictionary derivations. It should be noted that for complex nested loops, in order to improve code readability, the list comprehension can be split into multiple lines. It is recommended to choose the appropriate method according to the actual situation, taking into account code efficiency and readability.

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