快速开始
本指南将帮助您快速上手 pyutils 库的主要功能。
基本导入
pyutils 采用模块化设计，您可以按需导入所需的功能：
# 导入特定模块
from pyutils import array, string, math
# 或者导入整个库
import pyutils
# 导入特定函数
from pyutils.string import camel_case, snake_case
from pyutils.array import chunk, unique
数组操作
pyutils 提供了丰富的数组和列表操作工具：
from pyutils import array
# 将数组分块
data = [1, 2, 3, 4, 5, 6, 7, 8]
chunks = array.chunk(data, 3)
print(chunks) # [[1, 2, 3], [4, 5, 6], [7, 8]]
# 去重
numbers = [1, 2, 2, 3, 3, 3, 4]
unique_numbers = array.unique(numbers)
print(unique_numbers) # [1, 2, 3, 4]
# 随机打乱
shuffled = array.shuffle([1, 2, 3, 4, 5])
print(shuffled) # [3, 1, 5, 2, 4] (随机结果)
# 数组差集
diff = array.diff([1, 2, 3, 4], [2, 3])
print(diff) # [1, 4]
字符串处理
强大的字符串处理和格式化工具：
from pyutils import string
# 命名风格转换
snake_str = "hello_world_example"
camel_str = string.camel_case(snake_str)
print(camel_str) # "helloWorldExample"
# 反向转换
back_to_snake = string.snake_case(camel_str)
print(back_to_snake) # "hello_world_example"
# URL 友好的 slug
title = "Hello World! 这是一个测试 123"
slug = string.slugify(title)
print(slug) # "hello-world-123"
# 模糊匹配
similarity = string.fuzzy_match("hello", "helo")
print(similarity) # 0.8 (相似度)
# 生成 UUID
uuid = string.generate_uuid()
print(uuid) # "550e8400-e29b-41d4-a716-446655440000"
数学计算
实用的数学函数和统计工具：
from pyutils import math
# 数值限制
value = math.clamp(150, 0, 100) # 限制在 0-100 之间
print(value) # 100
# 线性插值
result = math.lerp(0, 100, 0.5) # 0 到 100 的中点
print(result) # 50.0
# 数值归一化
normalized = math.normalize(75, 0, 100) # 将 75 归一化到 0-1
print(normalized) # 0.75
# 角度转换
radians = math.deg_to_rad(180)
print(radians) # 3.141592653589793
degrees = math.rad_to_deg(3.14159)
print(degrees) # 179.99954498897746
对象操作
对象检查和操作工具：
from pyutils import object as obj_utils
# 深拷贝
original = {"a": [1, 2, 3], "b": {"c": 4}}
copied = obj_utils.deep_copy(original)
# 对象合并
obj1 = {"a": 1, "b": 2}
obj2 = {"b": 3, "c": 4}
merged = obj_utils.merge(obj1, obj2)
print(merged) # {"a": 1, "b": 3, "c": 4}
# 获取嵌套属性
data = {"user": {"profile": {"name": "Alice"}}}
name = obj_utils.get_nested(data, "user.profile.name")
print(name) # "Alice"
# 检查对象类型
is_dict = obj_utils.is_dict({"key": "value"})
print(is_dict) # True
函数式编程
函数式编程工具和装饰器：
from pyutils import function
# 函数组合
def add_one(x):
return x + 1
def multiply_two(x):
return x * 2
composed = function.compose(multiply_two, add_one)
result = composed(5) # (5 + 1) * 2
print(result) # 12
# 偏函数应用
def greet(greeting, name):
return f"{greeting}, {name}!"
say_hello = function.partial(greet, "Hello")
message = say_hello("Alice")
print(message) # "Hello, Alice!"
# 缓存装饰器
@function.memoize
def fibonacci(n):
if n < 2:
return n
return fibonacci(n-1) + fibonacci(n-2)
print(fibonacci(10)) # 55 (缓存加速)
异步编程
异步编程辅助工具：
import asyncio
from pyutils import async_utils
# 异步任务批量执行
async def fetch_data(url):
# 模拟异步请求
await asyncio.sleep(0.1)
return f"Data from {url}"
async def main():
urls = ["url1", "url2", "url3"]
tasks = [fetch_data(url) for url in urls]
# 并发执行所有任务
results = await async_utils.gather_with_concurrency(tasks, limit=2)
print(results)
# 超时控制
try:
result = await async_utils.timeout(fetch_data("slow_url"), 0.05)
except asyncio.TimeoutError:
print("请求超时")
# 运行异步代码
asyncio.run(main())
实际应用示例
数据处理管道
from pyutils import array, string, math
# 处理用户数据
users = [
{"name": "john_doe", "age": 25, "score": 85},
{"name": "jane_smith", "age": 30, "score": 92},
{"name": "bob_wilson", "age": 35, "score": 78},
]
# 数据转换和处理
processed_users = []
for user in users:
processed_user = {
"displayName": string.camel_case(user["name"]),
"age": user["age"],
"normalizedScore": math.normalize(user["score"], 0, 100),
"grade": "A" if user["score"] >= 90 else "B" if user["score"] >= 80 else "C"
}
processed_users.append(processed_user)
# 按分数分组
score_groups = array.chunk(sorted(processed_users, key=lambda x: x["normalizedScore"]), 2)
print(score_groups)
配置管理
from pyutils import object as obj_utils, string
# 默认配置
default_config = {
"database": {
"host": "localhost",
"port": 5432,
"name": "myapp"
},
"cache": {
"enabled": True,
"ttl": 3600
}
}
# 用户配置
user_config = {
"database": {
"host": "prod-server",
"password": "secret"
},
"debug": True
}
# 合并配置
final_config = obj_utils.deep_merge(default_config, user_config)
# 获取配置值
db_host = obj_utils.get_nested(final_config, "database.host")
print(db_host) # "prod-server"
下一步
现在您已经了解了 pyutils 的基本用法，可以：
如果您有任何问题或建议，欢迎在 GitHub 上创建 issue 或提交 pull request！