Python拥有丰富的标准库，提供了大量实用的功能。掌握这些标准库能够显著提高开发效率，避免重复造轮子。本章将介绍最常用和最重要的标准库模块。
collections模块提供了除内置容器外的专用容器数据类型。
from collections import Counter, defaultdict, OrderedDict, deque, namedtuple # Counter - 计数器，用于统计元素出现次数 text = "hello world hello python" counter = Counter(text.split()) print(counter) # Counter({'hello': 2, 'world': 1, 'python': 1}) print(counter.most_common(2)) # [('hello', 2), ('world', 1)] # 数学运算 c1 = Counter(a=3, b=1) c2 = Counter(a=1, b=2) print(c1 + c2) # Counter({'a': 4, 'b': 3}) print(c1 - c2) # Counter({'a': 2}) # defaultdict - 带有默认值的字典 default_dict = defaultdict(list) default_dict['fruits'].append('apple') default_dict['fruits'].append('banana') default_dict['colors'].append('red') print(dict(default_dict)) # {'fruits': ['apple', 'banana'], 'colors': ['red']} # 使用lambda设置默认值为0 int_dict = defaultdict(lambda: 0) int_dict['count'] += 1 print(int_dict['count']) # 1 print(int_dict['missing']) # 0（自动创建） # OrderedDict - 保持插入顺序的字典（Python 3.7+普通dict已实现） ordered = OrderedDict() ordered['first'] = 1 ordered['second'] = 2 ordered['third'] = 3 # 移动到末尾 ordered.move_to_end('first') print(list(ordered.keys())) # ['second', 'third', 'first'] # deque - 双端队列，两端操作都是O(1) dq = deque(maxlen=5) # 限制最大长度，自动丢弃旧元素 for i in range(10): dq.append(i) print(dq) # deque([5, 6, 7, 8, 9], maxlen=5) dq.appendleft(100) print(dq) # deque([100, 5, 6, 7, 8], maxlen=5) # namedtuple - 命名元组，可读性更强 Point = namedtuple('Point', ['x', 'y']) p = Point(10, 20) print(p.x, p.y) # 10 20 print(p[0], p[1]) # 10 20（仍支持索引访问） # 转换为字典 print(p._asdict()) # {'x': 10, 'y': 20} # 替换值创建新对象 p2 = p._replace(x=100) print(p2) # Point(x=100, y=20)
import heapq # 创建堆（使用列表） heap = [] heapq.heappush(heap, 3) heapq.heappush(heap, 1) heapq.heappush(heap, 2) print(heap) # [1, 3, 2] - 保持堆属性 # 弹出最小元素 print(heapq.heappop(heap)) # 1 print(heap) # [2, 3] # 将列表转换为堆 nums = [5, 3, 8, 1, 9, 2] heapq.heapify(nums) print(nums) # [1, 3, 2, 5, 9, 8] # 获取最大/最小的n个元素 numbers = [12, 3, 5, 7, 19, 1, 4] print(heapq.nlargest(3, numbers)) # [19, 12, 7] print(heapq.nsmallest(3, numbers)) # [1, 3, 4] # 合并多个有序列表 list1 = [1, 3, 5] list2 = [2, 4, 6] merged = heapq.merge(list1, list2) print(list(merged)) # [1, 2, 3, 4, 5, 6]
import bisect # 维护有序列表 sorted_list = [1, 3, 5, 7, 9] # 查找插入位置 pos = bisect.bisect_left(sorted_list, 4) # 2 pos = bisect.bisect_right(sorted_list, 5) # 3 # 插入元素保持有序 bisect.insort_left(sorted_list, 4) print(sorted_list) # [1, 3, 4, 5, 7, 9] bisect.insort_right(sorted_list, 5) print(sorted_list) # [1, 3, 4, 5, 5, 7, 9]
pathlib提供了面向对象的路径操作方式，推荐使用。
from pathlib import Path, PurePath # 创建路径对象 current = Path('.') home = Path.home() cwd = Path.cwd() # 构建路径 config_path = Path('/etc') / 'nginx' / 'nginx.conf' print(config_path) # /etc/nginx/nginx.conf # 路径属性 path = Path('/usr/local/bin/python') print(path.name) # python print(path.suffix) # '' print(path.suffixes) # [] print(path.stem) # python print(path.parent) # /usr/local/bin print(path.parents) # 祖先路径序列 print(path.parts) # ('/', 'usr', 'local', 'bin', 'python') print(path.anchor) # / # 路径查询 print(path.exists()) # 是否存在 print(path.is_file()) # 是否文件 print(path.is_dir()) # 是否目录 print(path.is_absolute()) # 是否绝对路径 # 文件操作 temp_file = Path('temp.txt') temp_file.write_text('Hello World', encoding='utf-8') content = temp_file.read_text(encoding='utf-8') temp_file.unlink() # 删除文件 # 目录操作 new_dir = Path('new_folder') new_dir.mkdir(exist_ok=True) new_dir.mkdir(parents=True, exist_ok=True) # 递归创建 new_dir.rmdir() # 删除空目录 # 遍历目录 for item in Path('.').iterdir(): print(item) # 递归查找 py_files = list(Path('.').rglob('*.py')) print(f'找到 {len(py_files)} 个Python文件') # 通配符匹配 for txt_file in Path('.').glob('*.txt'): print(txt_file) # 统计代码行数 def count_lines_of_code(path): total = 0 for py_file in Path(path).rglob('*.py'): try: lines = py_file.read_text(encoding='utf-8').splitlines() # 排除空行和注释 code_lines = [l for l in lines if l.strip() and not l.strip().startswith('#')] total += len(code_lines) except: pass return total
import shutil from pathlib import Path # 复制文件 shutil.copy('source.txt', 'dest.txt') # 复制文件 shutil.copy2('source.txt', 'dest.txt') # 保留元数据 shutil.copytree('src_dir', 'dst_dir') # 复制目录 # 移动和重命名 shutil.move('old_name.txt', 'new_name.txt') # 删除目录树 shutil.rmtree('directory_to_delete') # 归档操作 shutil.make_archive('backup', 'zip', 'source_dir') shutil.unpack_archive('backup.zip', 'extract_dir') # 磁盘使用 total, used, free = shutil.disk_usage('/') print(f'总空间: {total // 2**30} GB') print(f'已使用: {used // 2**30} GB') print(f'可用: {free // 2**30} GB') # 获取支持的压缩格式 print(shutil.get_archive_formats()) print(shutil.get_unpack_formats())
import pickle data = { 'name': '张三', 'age': 30, 'scores': [85, 90, 78], 'metadata': {'created': '2024-01-01'} } # 序列化到文件 with open('data.pkl', 'wb') as f: pickle.dump(data, f) # 从文件反序列化 with open('data.pkl', 'rb') as f: loaded_data = pickle.load(f) print(loaded_data) # 序列化为字节串 byte_data = pickle.dumps(data) print(f'序列化后大小: {len(byte_data)} 字节') # 从字节串反序列化 original = pickle.loads(byte_data) # 处理多个对象 with open('multi.pkl', 'wb') as f: pickle.dump(obj1, f) pickle.dump(obj2, f) pickle.dump(obj3, f) with open('multi.pkl', 'rb') as f: while True: try: obj = pickle.load(f) print(obj) except EOFError: break
警告：pickle不安全，不要加载不可信来源的数据。
import csv # 写入CSV with open('output.csv', 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f) # 写入表头 writer.writerow(['姓名', '年龄', '城市']) # 写入数据 writer.writerow(['张三', 25, '北京']) writer.writerow(['李四', 30, '上海']) # 写入多行 writer.writerows([ ['王五', 28, '广州'], ['赵六', 35, '深圳'] ]) # 读取CSV with open('output.csv', 'r', encoding='utf-8') as f: reader = csv.reader(f) for row in reader: print(row) # 使用DictReader/DictWriter with open('output.csv', 'r', encoding='utf-8') as f: reader = csv.DictReader(f) for row in reader: print(f"{row['姓名']} 住在 {row['城市']}") # DictWriter fieldnames = ['name', 'age', 'email'] with open('users.csv', 'w', newline='', encoding='utf-8') as f: writer = csv.DictWriter(f, fieldnames=fieldnames) writer.writeheader() writer.writerow({'name': 'Alice', 'age': 25, 'email': 'alice@example.com'}) # 自定义分隔符和引号 with open('data.tsv', 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f, delimiter='\\t', quotechar='"', quoting=csv.QUOTE_MINIMAL) writer.writerow(['Field 1', 'Field with, comma', 'Field with "quotes"'])
import configparser # 读取配置文件 config = configparser.ConfigParser() config.read('config.ini', encoding='utf-8') # 访问配置 print(config['DEFAULT']['ServerAliveInterval']) print(config.get('bitbucket.org', 'User')) print(config.getint('topsecret.server.com', 'Port')) print(config.getboolean('DEFAULT', 'Compression')) # 检查是否存在 if 'section_name' in config: print("Section exists") # 获取所有section print(config.sections()) # 创建/修改配置 config['Settings'] = { 'debug': 'true', 'log_level': 'INFO', 'max_connections': '100' } config['Database'] = {} config['Database']['host'] = 'localhost' config['Database']['port'] = '5432' # 写入文件 with open('new_config.ini', 'w', encoding='utf-8') as f: config.write(f) # 示例配置文件内容： """ [DEFAULT] ServerAliveInterval = 45 Compression = yes CompressionLevel = 9 [bitbucket.org] User = hg [topsecret.server.com] Port = 50022 ForwardX11 = no """
import re text = "The quick brown fox jumps over the lazy dog. Contact: john@example.com" # 搜索模式 match = re.search(r'fox', text) if match: print(f"找到: {match.group()} 在位置 {match.start()}-{match.end()}") # 查找所有匹配 emails = re.findall(r'\\b[\\w.-]+@[\\w.-]+\\.\\w+\\b', text) print(emails) # 替换 new_text = re.sub(r'dog', 'cat', text) # 分割 parts = re.split(r'\\s+', text) # 按空白分割 # 编译正则（多次使用时更高效） pattern = re.compile(r'\\b\\w{5}\\b') # 5个字母的单词 matches = pattern.findall(text) print(matches)
import re # 常用模式 patterns = { 'email': r'^[\\w.-]+@[\\w.-]+\\.\\w+$', 'phone': r'^1[3-9]\\d{9}$', # 中国手机号 'id_card': r'^\\d{17}[\\dXx]$', # 身份证号 'url': r'^https?://[^\\s/$.?#].[^\\s]*$', 'ipv4': r'^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$', 'date': r'^\\d{4}-(?:0[1-9]|1[0-2])-(?:0[1-9]|[12]\\d|3[01])$' } # 验证函数 def validate(pattern_name, value): pattern = patterns.get(pattern_name) if pattern: return bool(re.match(pattern, value)) return False # 测试 print(validate('email', 'test@example.com')) # True print(validate('phone', '13800138000')) # True print(validate('phone', '12345678901')) # False # 分组提取 text = "Name: John, Age: 30, City: New York" pattern = r'Name: (\\w+), Age: (\\d+), City: (\\w+)' match = re.search(pattern, text) if match: print(f"姓名: {match.group(1)}") print(f"年龄: {match.group(2)}") print(f"城市: {match.group(3)}") print(match.groups()) # ('John', '30', 'NewYork') # 命名分组 pattern = r'Name: (?P<name>\\w+), Age: (?P<age>\\d+)' match = re.search(pattern, text) if match: print(match.group('name')) print(match.groupdict()) # {'name': 'John', 'age': '30'}
import time # 当前时间戳 timestamp = time.time() print(f"当前时间戳: {timestamp}") # 时间戳转本地时间 local_time = time.localtime(timestamp) print(f"本地时间: {local_time}") print(f"格式化: {time.strftime('%Y-%m-%d %H:%M:%S', local_time)}") # UTC时间 utc_time = time.gmtime() print(f"UTC时间: {time.strftime('%Y-%m-%d %H:%M:%S', utc_time)}") # 字符串转时间结构 parsed = time.strptime('2024-01-15 14:30:00', '%Y-%m-%d %H:%M:%S') # 睡眠 print("开始") time.sleep(2) # 暂停2秒 print("2秒后") # 性能计时 start = time.perf_counter() # ... 执行代码 end = time.perf_counter() print(f"耗时: {end - start:.6f} 秒")
from datetime import datetime, date, time, timedelta from datetime import timezone # 当前日期时间 now = datetime.now() today = date.today() # 创建特定日期时间 dt = datetime(2024, 1, 15, 14, 30, 0) t = time(14, 30, 0) d = date(2024, 1, 15) # 格式化 print(now.strftime('%Y年%m月%d日 %H:%M:%S')) print(now.isoformat()) # 解析 parsed = datetime.strptime('2024-01-15 14:30:00', '%Y-%m-%d %H:%M:%S') # 时间差 delta = timedelta(days=5, hours=3, minutes=30) future = now + delta past = now - delta diff = future - now print(f"相差天数: {diff.days}") print(f"总秒数: {diff.total_seconds()}") # 时区处理（Python 3.9+） from zoneinfo import ZoneInfo utc = ZoneInfo('UTC') beijing = ZoneInfo('Asia/Shanghai') new_york = ZoneInfo('America/New_York') # 添加时区信息 dt_utc = datetime.now(utc) dt_bj = dt_utc.astimezone(beijing) dt_ny = dt_utc.astimezone(new_york) print(f"UTC: {dt_utc}") print(f"北京: {dt_bj}") print(f"纽约: {dt_ny}") # 所有可用时区 import zoneinfo print(sorted(zoneinfo.available_timezones())[:10])
from urllib import request, parse, error # 简单GET请求 url = 'https://api.github.com' with request.urlopen(url) as response: data = response.read() print(f"状态码: {response.status}") print(f"内容类型: {response.headers['Content-Type']}") print(data.decode('utf-8')[:500]) # 带参数的GET params = parse.urlencode({'q': 'python', 'page': 1}) url = f'https://httpbin.org/get?{params}' # POST请求 data = parse.urlencode({'name': 'test', 'value': '123'}).encode() req = request.Request('https://httpbin.org/post', data=data, method='POST') req.add_header('User-Agent', 'Python urllib') req.add_header('Content-Type', 'application/x-www-form-urlencoded') with request.urlopen(req) as response: print(response.read().decode()) # 处理异常 try: with request.urlopen('https://httpbin.org/status/404') as response: pass except error.HTTPError as e: print(f"HTTP错误: {e.code} {e.reason}") except error.URLError as e: print(f"URL错误: {e.reason}")
# 命令行运行: python -m http.server 8000 # 或编程方式 from http.server import HTTPServer, BaseHTTPRequestHandler class SimpleHandler(BaseHTTPRequestHandler): def do_GET(self): self.send_response(200) self.send_header('Content-type', 'text/html; charset=utf-8') self.end_headers() response = f""" <html> <body> <h1>Hello from Python!</h1> <p>路径: {self.path}</p> </body> </html> """ self.wfile.write(response.encode()) def do_POST(self): content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length) self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() import json response = {'received': post_data.decode()} self.wfile.write(json.dumps(response).encode()) if __name__ == '__main__': server = HTTPServer(('localhost', 8000), SimpleHandler) print('服务器运行在 http://localhost:8000') server.serve_forever()
import itertools # 无限迭代器 counter = itertools.count(start=10, step=2) print(list(next(counter) for _ in range(5))) # [10, 12, 14, 16, 18] cycle = itertools.cycle(['A', 'B', 'C']) print(list(next(cycle) for _ in range(7))) # ['A', 'B', 'C', 'A', 'B', 'C', 'A'] repeat = itertools.repeat('X', 5) print(list(repeat)) # ['X', 'X', 'X', 'X', 'X'] # 组合迭代器 items = ['A', 'B', 'C'] # 笛卡尔积 print(list(itertools.product(items, repeat=2))) # [('A', 'A'), ('A', 'B'), ('A', 'C'), ('B', 'A'), ...] # 排列 print(list(itertools.permutations(items, 2))) # [('A', 'B'), ('A', 'C'), ('B', 'A'), ('B', 'C'), ...] # 组合 print(list(itertools.combinations(items, 2))) # [('A', 'B'), ('A', 'C'), ('B', 'C')] # 可重复组合 print(list(itertools.combinations_with_replacement(items, 2))) # [('A', 'A'), ('A', 'B'), ('A', 'C'), ('B', 'B'), ...] # 分组 data = [('A', 1), ('A', 2), ('B', 3), ('B', 4), ('C', 5)] for key, group in itertools.groupby(data, key=lambda x: x[0]): print(f"{key}: {list(group)}") # 链式迭代 list1 = [1, 2, 3] list2 = ['a', 'b', 'c'] list3 = [True, False] print(list(itertools.chain(list1, list2, list3))) # [1, 2, 3, 'a', 'b', 'c', True, False] # 压缩 names = ['Alice', 'Bob', 'Charlie'] ages = [25, 30] print(list(itertools.zip_longest(names, ages, fillvalue='Unknown'))) # [('Alice', 25), ('Bob', 30), ('Charlie', 'Unknown')]
from functools import lru_cache, partial, reduce, wraps # lru_cache - 函数结果缓存 @lru_cache(maxsize=128) def fibonacci(n): if n < 2: return n return fibonacci(n - 1) + fibonacci(n - 2) # 比较有无缓存的性能 import time start = time.time() print(fibonacci(35)) print(f"首次调用: {time.time() - start:.4f}秒") start = time.time() print(fibonacci(35)) print(f"缓存调用: {time.time() - start:.6f}秒") # partial - 部分参数绑定 from operator import mul double = partial(mul, 2) print(double(5)) # 10 def greet(greeting, name): return f"{greeting}, {name}!" say_hello = partial(greet, "Hello") print(say_hello("Alice")) # Hello, Alice! # reduce from operator import add numbers = [1, 2, 3, 4, 5] total = reduce(add, numbers) # 等同于 sum(numbers) product = reduce(mul, numbers) # 120 # 自定义reduce操作 max_value = reduce(lambda x, y: x if x > y else y, numbers) # wraps - 保留装饰器函数的元信息 def my_decorator(func): @wraps(func) def wrapper(*args, **kwargs): """Wrapper docstring""" print("Before") result = func(*args, **kwargs) print("After") return result return wrapper @my_decorator def example(): """Example function""" pass print(example.__name__) # example（使用@wraps） print(example.__doc__) # Example function
import random # 设置随机种子（可重复） random.seed(42) # 随机整数 print(random.randint(1, 100)) # 1-100之间 print(random.randrange(0, 100, 2)) # 0-98之间的偶数 # 随机浮点数 print(random.random()) # 0.0 - 1.0 print(random.uniform(1.5, 10.5)) # 1.5 - 10.5 # 序列操作 items = ['apple', 'banana', 'cherry', 'date'] print(random.choice(items)) # 随机选择一项 print(random.choices(items, k=3)) # 随机选择3项（可重复） print(random.sample(items, k=2)) # 随机选择2项（不重复） random.shuffle(items) # 原地打乱 print(items) # 加权随机 weights = [10, 1, 1, 1] # apple被选中的概率更高 print(random.choices(items, weights=weights, k=5)) # 高斯分布 print(random.gauss(mu=0, sigma=1)) # 均值0，标准差1
import statistics data = [2, 4, 4, 4, 5, 5, 7, 9] print(f"均值: {statistics.mean(data)}") # 5.0 print(f"中位数: {statistics.median(data)}") # 4.5 print(f"众数: {statistics.mode(data)}") # 4 print(f"方差: {statistics.variance(data)}") # 4.571... print(f"标准差: {statistics.stdev(data)}") # 2.138... # 其他统计量 print(f"几何平均: {statistics.geometric_mean([2, 8])}") # 4.0 print(f"调和平均: {statistics.harmonic_mean([2, 4, 8])}") # 3.428...
from contextlib import contextmanager, suppress, redirect_stdout import io # 自定义上下文管理器（装饰器方式） @contextmanager def managed_resource(name): print(f"获取资源: {name}") resource = {"name": name, "data": []} try: yield resource finally: print(f"释放资源: {name}") resource.clear() # 使用 with managed_resource("database") as db: db["data"].append("记录1") print(f"使用资源: {db}") # suppress - 忽略指定异常 with suppress(FileNotFoundError): with open('nonexistent.txt') as f: content = f.read() print("继续执行") # redirect_stdout output = io.StringIO() with redirect_stdout(output): print("这行输出被捕获") print("另一行") captured = output.getvalue() print(f"捕获的内容: {captured}") # ExitStack - 管理多个上下文 from contextlib import ExitStack with ExitStack() as stack: files = [stack.enter_context(open(fname)) for fname in ['a.txt', 'b.txt']] # 所有文件会在退出时自动关闭
习题1：日志分析器
编写一个日志分析工具，功能包括：
习题2：配置文件管理器
实现一个配置管理类，支持：
习题3：正则表达式工具
创建正则表达式工具函数：
习题4：文件批处理工具
使用pathlib和shutil实现：
习题5：时间工具类
实现TimeUtil类，提供以下方法：
本章介绍了Python标准库中最实用的模块：
熟练掌握这些标准库模块，能够解决大部分日常编程任务，避免引入不必要的第三方依赖。