推导式与生成器表达式的重要性
Python的推导式（Comprehensions）和生成器表达式是Python语言的重要特性，它们提供了一种简洁、高效的方式来创建数据结构。掌握这些概念对于编写Pythonic代码、提高代码可读性和性能至关重要。
列表推导式
1. 基本列表推导式
from datetime import datetime
def list_comprehension_basics():
"""列表推导式基础"""
print("=== 列表推导式基础 ===")
# 基本语法: [expression for item in iterable]
squares = [x**2 for x in range(10)]
print(f"平方数: {squares}")
# 字符串处理
words = ['hello', 'world', 'python', 'programming']
upper_words = [word.upper() for word in words]
print(f"大写单词: {upper_words}")
# 数学运算
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
doubled = [x * 2 for x in numbers]
print(f"双倍数字: {doubled}")
# 日期处理
dates = ['2018-11-20', '2018-11-21', '2018-11-22']
formatted_dates = [datetime.strptime(date, '%Y-%m-%d').strftime('%Y年%m月%d日') for date in dates]
print(f"格式化日期: {formatted_dates}")
list_comprehension_basics()
2. 带条件的列表推导式
def conditional_list_comprehension():
"""带条件的列表推导式"""
print("=== 带条件的列表推导式 ===")
# 基本条件过滤
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = [x for x in numbers if x % 2 == 0]
print(f"偶数: {even_numbers}")
# 复杂条件
scores = [85, 92, 78, 96, 88, 73, 91, 87]
high_scores = [score for score in scores if score >= 90]
print(f"高分: {high_scores}")
# 字符串条件
words = ['apple', 'banana', 'cherry', 'date', 'elderberry']
long_words = [word for word in words if len(word) > 5]
print(f"长单词: {long_words}")
# 多重条件
students = [
{'name': '张三', 'age': 20, 'grade': 85},
{'name': '李四', 'age': 19, 'grade': 92},
{'name': '王五', 'age': 21, 'grade': 78},
{'name': '赵六', 'age': 20, 'grade': 96},
]
excellent_students = [s['name'] for s in students if s['age'] >= 20 and s['grade'] >= 85]
print(f"优秀学生: {excellent_students}")
# 条件表达式
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
categorized = ['偶数' if x % 2 == 0 else '奇数' for x in numbers]
print(f"数字分类: {categorized}")
conditional_list_comprehension()
字典推导式
1. 基本字典推导式
def dict_comprehension_basics():
"""字典推导式基础"""
print("=== 字典推导式基础 ===")
# 基本语法: {key: value for item in iterable}
squares_dict = {x: x**2 for x in range(5)}
print(f"平方字典: {squares_dict}")
# 字符串处理
words = ['apple', 'banana', 'cherry']
word_lengths = {word: len(word) for word in words}
print(f"单词长度: {word_lengths}")
# 从列表创建字典
numbers = [1, 2, 3, 4, 5]
number_types = {num: '偶数' if num % 2 == 0 else '奇数' for num in numbers}
print(f"数字类型: {number_types}")
# 日期处理
dates = ['2018-11-20', '2018-11-21', '2018-11-22']
date_dict = {i: date for i, date in enumerate(dates, 1)}
print(f"日期字典: {date_dict}")
dict_comprehension_basics()
集合推导式
1. 基本集合推导式
def set_comprehension_basics():
"""集合推导式基础"""
print("=== 集合推导式基础 ===")
# 基本语法: {expression for item in iterable}
squares_set = {x**2 for x in range(10)}
print(f"平方数集合: {squares_set}")
# 字符串处理
words = ['hello', 'world', 'python', 'programming', 'hello']
unique_lengths = {len(word) for word in words}
print(f"唯一长度: {unique_lengths}")
# 数学运算
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = {x for x in numbers if x % 2 == 0}
print(f"偶数集合: {even_numbers}")
# 字符处理
text = "hello world python programming"
unique_chars = {char for char in text if char.isalpha()}
print(f"唯一字母: {unique_chars}")
set_comprehension_basics()
生成器表达式
1. 基本生成器表达式
def generator_expression_basics():
"""生成器表达式基础"""
print("=== 生成器表达式基础 ===")
# 基本语法: (expression for item in iterable)
squares_gen = (x**2 for x in range(10))
print(f"生成器对象: {squares_gen}")
print(f"生成器内容: {list(squares_gen)}")
# 字符串处理
words = ['hello', 'world', 'python', 'programming']
upper_gen = (word.upper() for word in words)
print(f"大写生成器: {list(upper_gen)}")
# 数学运算
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
doubled_gen = (x * 2 for x in numbers)
print(f"双倍生成器: {list(doubled_gen)}")
# 日期处理
dates = ['2018-11-20', '2018-11-21', '2018-11-22']
formatted_gen = (datetime.strptime(date, '%Y-%m-%d').strftime('%Y年%m月%d日') for date in dates)
print(f"格式化日期生成器: {list(formatted_gen)}")
generator_expression_basics()
2. 生成器表达式节省内存
def memory_efficient_example():
"""内存效率示例"""
print("=== 内存效率示例 ===")
# 列表推导式 - 占用大量内存
import sys
# 创建大列表
large_list = [x**2 for x in range(1000000)]
print(f"列表内存占用: {sys.getsizeof(large_list)} 字节")
# 生成器表达式 - 内存友好
large_gen = (x**2 for x in range(1000000))
print(f"生成器内存占用: {sys.getsizeof(large_gen)} 字节")
# 惰性求值
total = sum(x**2 for x in range(1000000))
print(f"平方和: {total}")
# 条件生成器
even_squares_gen = (x**2 for x in range(1000000) if x % 2 == 0)
count = sum(1 for _ in even_squares_gen)
print(f"偶数平方数量: {count}")
memory_efficient_example()
实际应用案例
1. 数据处理
def data_processing_example():
"""数据处理示例"""
print("=== 数据处理示例 ===")
# 模拟数据
raw_data = [
{'name': '张三', 'age': 25, 'salary': 8000, 'department': 'IT'},
{'name': '李四', 'age': 30, 'salary': 12000, 'department': 'IT'},
{'name': '王五', 'age': 28, 'salary': 9000, 'department': 'HR'},
{'name': '赵六', 'age': 35, 'salary': 15000, 'department': 'IT'},
{'name': '钱七', 'age': 27, 'salary': 7000, 'department': 'HR'},
]
# 数据过滤和转换
it_employees = [emp for emp in raw_data if emp['department'] == 'IT']
print(f"IT部门员工: {it_employees}")
# 高薪员工
high_salary = {emp['name']: emp['salary'] for emp in raw_data if emp['salary'] >= 10000}
print(f"高薪员工: {high_salary}")
# 年龄统计
age_groups = {
'young': [emp['name'] for emp in raw_data if emp['age'] < 30],
'middle': [emp['name'] for emp in raw_data if 30 <= emp['age'] < 35],
'senior': [emp['name'] for emp in raw_data if emp['age'] >= 35]
}
print(f"年龄分组: {age_groups}")
# 部门统计
dept_stats = {
dept: len([emp for emp in raw_data if emp['department'] == dept])
for dept in {emp['department'] for emp in raw_data}
}
print(f"部门统计: {dept_stats}")
data_processing_example()
可读性建议
1. 最佳实践
def best_practices():
"""最佳实践示例"""
print("=== 最佳实践 ===")
# 好的实践：条件前置
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
good_practice = [x**2 for x in numbers if x % 2 == 0]
print(f"好的实践: {good_practice}")
# 避免：过于复杂的推导式
# 复杂逻辑应该拆分成函数
def is_prime(n):
if n < 2:
return False
return all(n % i != 0 for i in range(2, int(n**0.5) + 1))
primes = [x for x in range(2, 50) if is_prime(x)]
print(f"质数: {primes}")
# 嵌套推导式：不超过2层
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flattened = [item for row in matrix for item in row]
print(f"展平矩阵: {flattened}")
# 复杂嵌套应该拆分
def process_matrix(matrix):
return [item for row in matrix for item in row if item > 5]
result = process_matrix(matrix)
print(f"处理结果: {result}")
best_practices()
总结
掌握Python推导式和生成器表达式是编写高效代码的关键：
- 列表推导式：理解基本语法、条件过滤、嵌套结构
- 字典推导式：掌握键值对创建、条件过滤、复杂转换
- 集合推导式：了解去重、条件过滤、集合运算
- 生成器表达式：掌握内存效率、惰性求值、大数据处理
- 实际应用：在数据处理、文件操作等场景中的应用
- 最佳实践：遵循推导式使用的最佳实践
通过系统学习这些概念，你将能够编写出更加Pythonic、高效的代码，提高开发效率和代码质量。
转载请注明：周志洋的博客 » Python基础知识-推导式与生成器表达式
