Why You Should Rethink Your Python Toolbox in 5

Upgrade your 2025 Python toolbox: explore the important library you missed

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https://www.php.cn/link/00bd13095d06c20b11a2993ca419d16b

Python is powerful, but your tools can make you a programming god and make you fall into trouble. Do not become developers who are still using outdated tools, and other parts of the world are developing rapidly.

Many developers still seriously rely on Pandas, Requests, and BeautifulSoup, but these are not the most effective solutions for modern development demand. In this article, we will explore some top new Python libraries in 2025. These libraries will enhance your development process and help you maintain a leading position.

A) Old library and better alternative

Give up OS for file operation: Use Pathlib

1. OS module is usually cumbersome in terms of files and path processing, because it has problems such as path segments and lengthy grammar such as paths specific to the platform. Pathlib simplifies this through the intuitive object -oriented method (such as ,
and

) used to check the path, which simplifies this point, thereby achieving seamless cross -platform compatibility. With its simpler grammar and built -in function, Pathlib eliminates the needs of manual adjustment and became the first choice solution for modern Python developers.

/ Example: .exists() .is_file()

Why do you want to switch? Pathlib makes life easier. It is more intuitive than OS, using object -oriented methods to process files and paths. You don't have to worry about issues specific to the platform (such as

and

from pathlib import Path

# 创建文件
file = Path("example.txt")
file.write_text("Hello, Pathlib!")

# 读取文件
print(file.read_text())

# 检查是否存在
if file.exists():
    print("File exists")

), because Pathlib will deal with you for you. In addition, grammar is more concise and easy to read.

For small projects, it is not a tool for changing the rules of the game, but for any large project, it is definitely a direction.

/ Replace Requests with httpx: modern HTTP client used for asynchronous and synchronized requests

HTTPX has become a powerful alternative to Requests, especially in 2025. Unlike Requests, HTTPX also provides HTTP/2 support, which can significantly reduce delays and improve request processing by allowing multi -road reuse connections. HTTPX is a modern alternative that supports asynchronous operations without sacrificing the simplicity and familiarity of the Requests API.

<示> Example:
<要> Why use HTTPX?

<<>

If you are developing applications that require high and combined nature (such as network capture or microservices), HTTPX's support for asynchronous operations can significantly improve performance.

Another key advantage of HTTPX is its default connection pool for each host, which reduces delay and resource consumption.

import httpx
import asyncio

# asyncio用于启用异步编程，对于httpx的非阻塞HTTP请求来说是不可或缺的。
# 使用httpx，你可以使用async/await语法同时运行多个HTTP请求。


# 演示使用httpx的异步请求
async def async_get_data():
    async with httpx.AsyncClient() as client:
        response = await client.get('https://jsonplaceholder.typicode.com/posts/1')
        if response.status_code == 200:
            print("Async Response:", response.json())
        else:
            print(f"Error: {response.status_code}")

# 运行异步请求
asyncio.run(async_get_data())

# 使用httpx的异步HTTP/2请求
async def async_http2_request():
    async with httpx.AsyncClient(http2=True) as client:
        response = await client.get('https://http2.golang.org/reqinfo')
        if response.status_code == 200:
            print("HTTP/2 Response:", response.text)
        else:
            print(f"Error: {response.status_code}")

# 运行HTTP/2请求
asyncio.run(async_http2_request())

# 使用httpx客户端进行连接池
def connection_pooling_example():
    with httpx.Client(keep_alive=True) as client:
        url = "https://jsonplaceholder.typicode.com/posts/1"
        # 使用连接池进行多次请求
        for _ in range(5):
            response = client.get(url)
            if response.status_code == 200:
                print("Response Content:", response.text)
            else:
                print(f"Error: {response.status_code}")

# 运行连接池示例
connection_pooling_example()

Basically, if you handle a lot of I/O, HTTPX will save you a lot of trouble.

3. <越> Beyond pandas: Use Polars

Pandas is very suitable for small to medium -sized data sets, but when you add a larger data set to it, memory use and performance will begin to decrease.

Such as the difficulty of memory consumption, low operation efficiency, and data conversion (such as

and .fillna()), it is a problem that many developers who use Pandas often encounter. .loc

Polars is a modern, high -efficiency, multi -threaded data processing library, which provides a faster alternative to large data sets than Pandas. Unlike Pandas, Polars supports parallel processing, which accelerates the speed of data operation tasks.

<示> Example:

<要> Why use Polars?

from pathlib import Path

# 创建文件
file = Path("example.txt")
file.write_text("Hello, Pathlib!")

# 读取文件
print(file.read_text())

# 检查是否存在
if file.exists():
    print("File exists")

<<>

Therefore, if you are dealing with large -scale data processing, you need to perform parallel execution or want an efficient solution for memory, then Polars is the best choice for modern data science and analysis. Pandas may be your first love, but Polars is the person who can handle heavy work.

<级> Upgrade your test game: Use pytest to replace Unittest

4. Unittest? Of course, it is effective, but please, now it is 2025. Using it can not attract anyone. This is like trying to attract someone with a flip phone when everyone is using the iPhone. Yes, it is effective, but this is completely a trouble. Pytest: This is a cool modern test framework, which makes it easier for writing and reading tests. What is the unittest?
<<>

For unfamiliar people, UnitTest is the built -in test framework of Python, but it often makes people feel outdated, the grammar is lengthy, and the model code is repeated. With Pytest, you can get powerful functions, such as flexible FIXTURE management, automatic test discovery and built -in parameterization (using

) to easily use different inputs to run the same test. It also supports parallel test execution through Pytest-XDIST, which improves the performance of large test kits.

<示> Example:

<要> Why use test_? @pytest.mark.parametrize <<>

By using <缀> prefix, you can clearly indicate to Pytest that these functions should be testing. This is part of Pytest's agreement, which can be found and run correctly without any additional configuration.

In short, Pytest replaced Unittest's clumsy settings, making the test more efficient, more flexible and scalable.

import httpx
import asyncio

# asyncio用于启用异步编程，对于httpx的非阻塞HTTP请求来说是不可或缺的。
# 使用httpx，你可以使用async/await语法同时运行多个HTTP请求。


# 演示使用httpx的异步请求
async def async_get_data():
    async with httpx.AsyncClient() as client:
        response = await client.get('https://jsonplaceholder.typicode.com/posts/1')
        if response.status_code == 200:
            print("Async Response:", response.json())
        else:
            print(f"Error: {response.status_code}")

# 运行异步请求
asyncio.run(async_get_data())

# 使用httpx的异步HTTP/2请求
async def async_http2_request():
    async with httpx.AsyncClient(http2=True) as client:
        response = await client.get('https://http2.golang.org/reqinfo')
        if response.status_code == 200:
            print("HTTP/2 Response:", response.text)
        else:
            print(f"Error: {response.status_code}")

# 运行HTTP/2请求
asyncio.run(async_http2_request())

# 使用httpx客户端进行连接池
def connection_pooling_example():
    with httpx.Client(keep_alive=True) as client:
        url = "https://jsonplaceholder.typicode.com/posts/1"
        # 使用连接池进行多次请求
        for _ in range(5):
            response = client.get(url)
            if response.status_code == 200:
                print("Response Content:", response.text)
            else:
                print(f"Error: {response.status_code}")

# 运行连接池示例
connection_pooling_example()

The library that deserves more attention in 2025

1. BeeWare for cross-platform Python application development

BeeWare is an emerging Python framework that deserves more attention, especially in 2025. It allows Python developers to write native applications on multiple platforms (desktop, mobile, web) using the same code base. Unlike traditional desktop frameworks such as PyQt or Tkinter, BeeWare goes a step further and supports deployment on Android, iOS and even WebAssembly. A key feature of BeeWare is its cross-platform nature, so you can write an application once and run it anywhere.

2. pydantic for data validation

Validating data can be a chore. pydantic is a Python library that validates and parses data based on type hints. If you're dealing with messy or unreliable data (such as API responses, user input, or configuration), Pydantic can ensure your data is clean, structured, and error-free.

3. Poetry for packaging and dependency management

Poetry is a modern Python tool for easy dependency management and project packaging.

It uses pyproject.toml and poetry.lock for version control, ensuring reproducible builds and simplifying publishing to PyPI. It improves the transparency of dependency graphs, making it a more powerful alternative to older tools like pip and setuptools.

4. FastAPI for modern APIs

FastAPI is a high-performance Python framework often used as an alternative to Flask or Django REST Framework for building APIs. Due to its scalability and efficiency, it is ideal for both small projects and large applications. FastAPI stands out because it integrates Python's type hints for data validation and automatically generates OpenAPI documentation, making development faster and less error-prone.

5. asyncpg is used for database operations

asyncpg is a high-performance asynchronous PostgreSQL database driver for Python. Want fast queries in your Python application? Okay, no more slow blocking calls making your application as slow as if you were still using dial-up. Unlike traditional synchronization libraries such as psycopg2, asyncpg uses asynchronous programming to optimize database operations without blocking other tasks in the application, which is especially useful in modern web frameworks such as FastAPI, Tornado or Sanic.

6. DuckDB for in-memory analytics

DuckDB is a fast, in-memory database designed to run complex analytical queries efficiently. Unlike traditional databases such as PostgreSQL, DuckDB uses data directly in memory, allowing you to quickly process large data sets without the need for an external server.

Final Thoughts

In 2025, it’s time to rethink your Python toolbox. While classic libraries like Requests and Pandas have their place, emerging libraries like httpx, Polars, rich, and duckdb can streamline your workflow.

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