Python字典推导式的常见应用场景，让你的代码更简洁高效

{key_expression: value_expression for item in iterable if condition}

# 基础用法
students = ['张三', '李四', '王五', '赵六']
student_dict = {i: name for i, name in enumerate(students, start=2024001)}
print(student_dict)
# 输出：{2024001: '张三', 2024002: '李四', 2024003: '王五', 2024004: '赵六'}

# 也可以从两个平行列表创建
names = ['张三', '李四', '王五']
scores = [85, 92, 78]
score_dict = {name: score for name, score in zip(names, scores)}
print(score_dict)
# 输出：{'张三': 85, '李四': 92, '王五': 78}

# 温度转换：将摄氏度转换为华氏度
celsius_temps = {'北京': 20, '上海': 25, '广州': 30, '哈尔滨': 15}
fahrenheit_temps = {city: temp * 9/5 + 32 for city, temp in celsius_temps.items()}
print(fahrenheit_temps)
# 输出：{'北京': 68.0, '上海': 77.0, '广州': 86.0, '哈尔滨': 59.0}

# 字符串格式化
products = {'苹果': 8.5, '香蕉': 3.5, '橙子': 6.0}
formatted_prices = {name: f'¥{price:.2f}' for name, price in products.items()}
print(formatted_prices)
# 输出：{'苹果': '¥8.50', '香蕉': '¥3.50', '橙子': '¥6.00'}

# 筛选成绩优秀的学生（分数 >= 90）
scores = {'张三': 85, '李四': 92, '王五': 78, '赵六': 95, '钱七': 88}
top_students = {name: score for name, score in scores.items() if score >= 90}
print(top_students)
# 输出：{'李四': 92, '赵六': 95}

# 筛选特定前缀的键
files = {'image1.jpg': 1024, 'doc1.pdf': 512, 'image2.png': 2048, 'data.txt': 128}
image_files = {name: size for name, size in files.items() if name.startswith('image')}
print(image_files)
# 输出：{'image1.jpg': 1024, 'image2.png': 2048}

# 基础键值交换
original = {'a': 1, 'b': 2, 'c': 3}
reversed_dict = {value: key for key, value in original.items()}
print(reversed_dict)
# 输出：{1: 'a', 2: 'b', 3: 'c'}

# 处理重复值的情况
original_with_dups = {'a': 1, 'b': 2, 'c': 2, 'd': 1}
# 使用列表收集重复的键
reversed_with_lists = {}
for key, value in original_with_dups.items():
    if value not in reversed_with_lists:
        reversed_with_lists[value] = [key]
    else:
        reversed_with_lists[value].append(key)
print(reversed_with_lists)
# 输出：{1: ['a', 'd'], 2: ['b', 'c']}

# 二维列表转字典
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
row_dicts = {f'row_{i}': {f'col_{j}': val for j, val in enumerate(row)} 
             for i, row in enumerate(matrix)}
print(row_dicts)
# 输出：{'row_0': {'col_0': 1, 'col_1': 2, 'col_2': 3}, 
#       'row_1': {'col_0': 4, 'col_1': 5, 'col_2': 6}, 
#       'row_2': {'col_0': 7, 'col_1': 8, 'col_2': 9}}

# 统计字符串中字符出现的次数
text = "hello world"
char_count = {char: text.count(char) for char in set(text)}
print(char_count)
# 输出：{'w': 1, 'r': 1, 'd': 1, 'e': 1, ' ': 1, 'l': 3, 'h': 1, 'o': 2}

# 对不同条件应用不同处理
numbers = [1, 2, 3, 4, 5, 6]
num_type = {n: '偶数' if n % 2 == 0 else '奇数' for n in numbers}
print(num_type)
# 输出：{1: '奇数', 2: '偶数', 3: '奇数', 4: '偶数', 5: '奇数', 6: '偶数'}

# 更复杂的条件判断
grades = {'张三': 85, '李四': 92, '王五': 60, '赵六': 45, '钱七': 75}
grade_level = {name: '优秀' if score >= 90 else '良好' if score >= 80 
               else '及格' if score >= 60 else '不及格' 
               for name, score in grades.items()}
print(grade_level)
# 输出：{'张三': '良好', '李四': '优秀', '王五': '及格', '赵六': '不及格', '钱七': '良好'}

# 读取CSV格式的数据（模拟）
data = [
    "张三,25,北京",
    "李四,30,上海",
    "王五,22,广州"
]

# 解析并创建字典
parsed_data = {line.split(',')[0]: {
    'age': int(line.split(',')[1]),
    'city': line.split(',')[2]
} for line in data}
print(parsed_data)
# 输出：{'张三': {'age': 25, 'city': '北京'}, 
#       '李四': {'age': 30, 'city': '上海'}, 
#       '王五': {'age': 22, 'city': '广州'}}

# 去除空值或None
raw_dict = {'a': 1, 'b': None, 'c': 0, 'd': '', 'e': 5}
cleaned_dict = {k: v for k, v in raw_dict.items() if v not in (None, '', 0)}
print(cleaned_dict)
# 输出：{'a': 1, 'e': 5}

import timeit

# 使用字典推导式
def dict_comprehension():
    return {i: i**2 for i in range(1000)}

# 使用传统for循环
def traditional_loop():
    result = {}
    for i in range(1000):
        result[i] = i**2
    return result

# 测试性能
comp_time = timeit.timeit(dict_comprehension, number=10000)
loop_time = timeit.timeit(traditional_loop, number=10000)

print(f"字典推导式耗时: {comp_time:.4f}秒")
print(f"传统循环耗时: {loop_time:.4f}秒")
print(f"推导式快 {loop_time/comp_time:.2f} 倍")