7 Python Libraries You're Not Using But Should Be in 2025

7 Python Libraries You’re Not Using But Should Be in 2025

2025 年你应该使用但尚未使用的 7 个 Python 库

Python’s ecosystem grows every year, and 2025 has brought some incredible libraries that can dramatically simplify your workflow. While everyone knows about requests, pandas, and numpy, let me share 7 lesser-known gems that have genuinely changed how I write code. Python 的生态系统每年都在增长，2025 年带来了一些令人惊叹的库，可以极大地简化你的工作流程。虽然每个人都知道 requests、pandas 和 numpy，但请允许我分享 7 个鲜为人知的瑰宝，它们确实改变了我编写代码的方式。

1. 🐍 pydantic-core + pydantic v2 — Data Validation on Steroids

1. 🐍 pydantic-core + pydantic v2 — 强力数据验证

Most developers use Pydantic, but Pydantic v2 is a completely different beast. Written in Rust via pydantic-core, it’s 5-50x faster than v1. 大多数开发者都在使用 Pydantic，但 Pydantic v2 是一个完全不同的“野兽”。它通过 pydantic-core 使用 Rust 编写，速度比 v1 版本快 5 到 50 倍。

from pydantic import BaseModel, field_validator
from datetime import date

class Event(BaseModel):
    name: str
    date: date
    attendees: int

    @field_validator("name")
    @classmethod
    def name_must_not_be_empty(cls, v):
        if not v.strip():
            raise ValueError("Event name cannot be empty")
        return v.strip()

event = Event(name=" DevConf 2025 ", date="2025-09-15", attendees=500)
print(event.name) # "DevConf 2025" — automatically stripped

Why it matters: Fast validation + automatic type coercion = fewer bugs, less boilerplate code. 为什么重要： 快速验证 + 自动类型转换 = 更少的 Bug，更少的样板代码。

2. 🌐 httpx — The Modern HTTP Client That Replaces requests

2. 🌐 httpx — 取代 requests 的现代 HTTP 客户端

If you’re still using requests for everything, httpx is the upgrade you need. 如果你还在用 requests 处理一切，httpx 就是你需要的升级版。

import httpx
import asyncio

async def fetch_api():
    async with httpx.AsyncClient() as client:
        resp = await client.get("https://api.github.com/users/octocat")
        return resp.json()

# Works in sync too!
resp = httpx.get("https://api.github.com/users/octocat")
print(resp.json()["name"])

Why switch? Async support built-in, HTTP/2 support, timeout defaults that actually make sense, and a nearly identical API to requests. 为什么要切换？ 内置异步支持、HTTP/2 支持、合理的默认超时设置，以及与 requests 几乎相同的 API。

3. 🎨 rich — Make Your CLI Apps Beautiful

3. 🎨 rich — 让你的 CLI 应用变得美观

Stop printing plain text. rich gives you colors, tables, progress bars, and syntax highlighting — all in the terminal. 别再只打印纯文本了。rich 可以为你提供颜色、表格、进度条和语法高亮——所有这些都在终端中实现。

from rich.console import Console
from rich.table import Table
from rich.progress import track

console = Console()

# Beautiful tables
table = Table(title="Server Status")
table.add_column("Server", style="cyan")
table.add_column("Status", style="green")
table.add_column("Uptime", justify="right")

table.add_row("web-01", "🟢 Running", "99.9%")
table.add_row("db-01", "🟢 Running", "99.7%")
table.add_row("cache-01", "🟡 Warning", "95.2%")

console.print(table)

# Progress bars with track()
for task in track(range(100), description="Processing..."):
    pass # do work

Your scripts will look professional with almost zero extra effort. 你的脚本将看起来非常专业，且几乎不需要额外的工作量。

4. 📝 typer — Build CLI Apps in Minutes, Not Hours

4. 📝 typer — 在几分钟内构建 CLI 应用

import typer

app = typer.Typer()

@app.command()
def hello(name: str, count: int = 1):
    """Say hello multiple times."""
    for _ in range(count):
        typer.echo(f"Hello {name}!")

@app.command()
def goodbye(name: str):
    typer.echo(f"Goodbye {name}! 👋")

if __name__ == "__main__":
    app()

Auto-generated help, type validation, and completion scripts. It’s Click but actually pleasant to use. 自动生成的帮助文档、类型验证和补全脚本。它就像 Click，但用起来更令人愉悦。

5. 🔍 watchdog — Monitor File System Changes

5. 🔍 watchdog — 监控文件系统变更

Perfect for build tools, auto-reloaders, and sync scripts. 非常适合构建工具、自动重载器和同步脚本。

from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
import time

class MyHandler(FileSystemEventHandler):
    def on_modified(self, event):
        if event.src_path.endswith(".py"):
            print(f"🔥 Changed: {event.src_path}")

observer = Observer()
observer.schedule(MyHandler(), path=".", recursive=True)
observer.start()

try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    observer.stop()
observer.join()

Build your own hot-reload system in 20 lines. 用 20 行代码构建你自己的热重载系统。

6. 📊 polars — The DataFrame Library That’s Eating Pandas’ Lunch

6. 📊 polars — 正在蚕食 Pandas 市场的 DataFrame 库

If you work with data, polars is the modern alternative to pandas that’s significantly faster and uses less memory. 如果你从事数据工作，polars 是 pandas 的现代替代品，它速度显著更快且占用内存更少。

import polars as pl

df = pl.scan_csv("large_dataset.csv") # Lazy evaluation!

result = (
    df.filter(pl.col("age") > 25)
    .group_by("department")
    .agg([
        pl.col("salary").mean().alias("avg_salary"),
        pl.col("id").count().alias("count")
    ])
    .sort("avg_salary", descending=True)
    .collect() # Execute the query
)
print(result)

Lazy evaluation, multi-threaded execution, and a cleaner API. For datasets over 100K rows, the speed difference is dramatic. 惰性求值、多线程执行以及更简洁的 API。对于超过 10 万行的数据集，速度差异非常巨大。

7. ⚡ orjson — The Fastest JSON Library for Python

7. ⚡ orjson — Python 最快的 JSON 库

Drop-in replacement for the standard json module that’s 2-3x faster. 标准 json 模块的直接替代品，速度快 2-3 倍。

import orjson

# Serialize
data = {"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}
serialized = orjson.dumps(data, option=orjson.OPT_INDENT_2)

# Deserialize
parsed = orjson.loads(serialized)

Also handles datetime, UUID, and numpy types natively — no custom encoders needed. 还能原生处理 datetime、UUID 和 numpy 类型——无需自定义编码器。

Bonus: Quick Comparison Table

附录：快速对比表

Library	Replaces	Speed Gain	Key Feature
库	替代	速度提升	核心功能
pydantic v2	pydantic v1	5-50x	Rust-powered validation
httpx	requests	Async native	HTTP/2 support
rich	print()	N/A	Beautiful terminal output
typer	click/argparse	N/A	Type hints → CLI
watchdog	polling loops	N/A	Cross-platform file events
polars	pandas	5-20x	Lazy + multi-threaded
orjson	json	2-3x	Native datetime support

Conclusion

结论

The Python ecosystem in 2025 is all about performance and developer experience. Libraries like polars and pydantic-core leverage Rust under the hood for speed, while typer and rich make your tools feel polished and professional. 2025 年的 Python 生态系统完全围绕性能和开发者体验展开。像 polars 和 pydantic-core 这样的库在底层利用 Rust 来提升速度，而 typer 和 rich 则让你的工具看起来精致且专业。

Which of these are you already using? Which one are you going to try first? Drop a comment below — I’d love to hear what’s in your toolbox! 你已经在用其中的哪些库了？你打算先尝试哪一个？在下方留言吧——我很想知道你的工具箱里都有什么！