Python 序列遍历与推导式

列表推导式

通过简明语法创建新列表。

numbers = [1, 2, 3, 4, 5]
squared = [num * num for num in numbers]
print(squared)

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flattened = [num for row in matrix for num in row]
print(flattened)

字典遍历

访问字典的键值对。

user_data = {
    "name": "sumingcheng",
    "age": 25,
    "city": "Beijing"
}

def print_user_info(data):
    for key, value in data.items():
        print(f"{key} {value}")

def get_filtered_items(data, exclude_keys):
    return {k: v for k, v in data.items() if k not in exclude_keys}

序列索引遍历

使用 enumerate 获取序列的索引和值。

def process_items_with_index(items):
    for index, item in enumerate(items):
        print(f"处理第{index + 1}个项目 {item}")

tasks = ["任务1", "任务2", "任务3"]
process_items_with_index(tasks)

多序列并行遍历

使用 zip 同时遍历多个序列。

def combine_lists(names, ages, cities):
    user_profiles = []
    for name, age, city in zip(names, ages, cities):
        profile = {
            "name": name,
            "age": age,
            "city": city
        }
        user_profiles.append(profile)
    return user_profiles

序列排序遍历

按特定顺序遍历序列。

def process_sorted_data(data, reverse=False):
    for item in sorted(data, reverse=reverse):
        yield item

numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
for num in process_sorted_data(numbers):
    print(num)

条件筛选遍历

根据条件筛选序列元素。

def filter_and_transform(numbers):
    return [num * 2 for num in numbers if num > 0]

def group_by_condition(items):
    result = {"valid": [], "invalid": []}
    for item in items:
        if isinstance(item, (int, float)):
            result["valid"].append(item)
        else:
            result["invalid"].append(item)
    return result

矩阵操作

处理二维数组。

def transpose_matrix(matrix):
    return [[row[i] for row in matrix]
            for i in range(len(matrix[0]))]

def matrix_operation(matrix):
    rows = len(matrix)
    cols = len(matrix[0])

    result = [[0 for _ in range(cols)]
              for _ in range(rows)]

    for i in range(rows):
        for j in range(cols):
            result[i][j] = matrix[i][j] * 2

    return result

生成器表达式

使用生成器处理大型序列。

def process_large_dataset(data):
    processed = (item * 2 for item in data)
    return sum(processed)

def chunk_processor(items, size):
    return (items[i:i + size]
            for i in range(0, len(items), size))