Upgrade Your Python Toolbox for 2025: Discover the Essential Libraries You’re Missing Out On
This article was originally published here: https://medium.com/p/3616b07b6121
Python’s powerful, but your tools can make you a coding god or a frustrated mess. Don’t be that developer stuck using outdated tools while the rest of the world is speeding ahead
Many developers are still heavily reliant on libraries like Pandas, Requests, and BeautifulSoup, but these aren’t always the most efficient solutions for modern development needs. In this article, we’ll explore some of the top emerging Python libraries for 2025 that will supercharge your development process and help you stay ahead of the curve.
A) Outdated Libraries and Better Replacements
1. Ditch OS For File Operations: Use pathlib
The os module is often cumbersome for file and path handling due to issues like platform-specific path separators and verbose syntax. pathlib simplifies this with intuitive object-oriented methods like for joining paths, .exists(), and .is_file() for checks, making cross-platform compatibility seamless. With its cleaner syntax and built-in features, pathlib eliminates the need for manual adjustments, becoming the go-to solution for modern Python developers
Example:
from pathlib import Path
# Creating a file
file = Path("example.txt")
file.write_text("Hello, Pathlib!")
# Reading the file
print(file.read_text())
# Checking existence
if file.exists():
print("File exists")
Why Switch?
pathlib just makes life easier. It’s more intuitive than os, with its object-oriented approach to working with files and paths. You won’t have to worry about platform-specific issues (like \ vs /) because pathlib handles all that for you. Plus, the syntax is cleaner and more readable.
It’s not a game-changer for tiny projects, but for anything serious, it’s definitely the way forward
2. Replace Requests with httpx: A Modern HTTP Client for Asynchronous and Synchronous Requests
HTTPS has emerged as a powerful alternative to requests, especially in 2025. Unlike requests, HTTPX also offers HTTP/2 support, which can dramatically reduce latency and improve request handling by allowing multiplexed connections. httpx—a modern alternative that supports async operations without sacrificing the simplicity and familiarity of the Requests API.
Example:
import httpx
import asyncio
# asyncio is used to enable asynchronous programming,
# and it's integral to httpx for non-blocking HTTP requests.
# With httpx, you can use async/await syntax to run multiple HTTP requests concurrently.
# Demonstrating Asynchronous Requests with httpx
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}")
# Run the asynchronous request
asyncio.run(async_get_data())
# Asynchronous HTTP/2 Request with httpx
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}")
# Run the HTTP/2 request
asyncio.run(async_http2_request())
# Connection Pooling with httpx Client
def connection_pooling_example():
with httpx.Client(keep_alive=True) as client:
url = "https://jsonplaceholder.typicode.com/posts/1"
# Multiple requests using connection pooling
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}")
# Run the connection pooling example
connection_pooling_example()
Why Use httpx?
If you’re working on applications that demand high concurrency, such as web scraping or microservices, HTTPX’s support for asynchronous operations offers significant performance improvements.
Another key benefit of HTTPX is its connection pooling by default for every host, which reduces latency and resource consumption
Essentially, if you're working with a lot of I/O, httpx will save you a lot of headaches.
3. **Move Beyond Pandas: Use Polars
Pandasis perfect for small to mid-sized datasets, but when you throw larger datasets at it, the memory usage and performance starts to suffer.
Issues like slow memory consumption, inefficient column-based operations, and difficulties with data transformations such as .fillna() and .loc are common problems for many developers using Pandas
Polars is a modern, memory-efficient, and multi-threaded data processing Rust-based backend library that provides a faster alternative to Pandas for large datasets. Unlike Pandas, Polars supports parallel processing, which speeds up data manipulation tasks
Example:
import polars as pl
# Create a Polars DataFrame from a dictionary with sample data
data = pl.DataFrame({
"name": ["Alice", "Bob", "Charlie", "David"],
"age": [25, 30, 35, 40],
"salary": [50000, 60000, 70000, 80000]
})
print("Original DataFrame:")
print(data)
# Output:
# shape: (4, 3)
# ┌─────────┬─────┬────────┐
# │ name ┆ age ┆ salary │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 │
# ╞═════════╪═════╪════════╡
# │ Alice ┆ 25 ┆ 50000 │
# │ Bob ┆ 30 ┆ 60000 │
# │ Charlie ┆ 35 ┆ 70000 │
# │ David ┆ 40 ┆ 80000 │
# └─────────┴─────┴────────┘
# This shows the original DataFrame with 4 rows and 3 columns: "name", "age", "salary"
# Perform lazy evaluation to prepare for filtering, sorting, and selecting data
result = (
data.lazy() # Converts the DataFrame to a lazy query, operations will not be executed yet
.filter(pl.col("age") > 30) # Filter rows where age > 30 (Charlie, David)
.sort("salary", reverse=True) # Sort by salary in descending order (David first, Charlie second)
.select(["name", "salary"]) # Select only "name" and "salary" columns
.collect() # Trigger computation and get the result
)
print("\nFiltered, Sorted, and Selected Data:")
print(result)
# Output:
# Filtered, Sorted, and Selected Data:
# shape: (2, 2)
# ┌─────────┬────────┐
# │ name ┆ salary │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════════╪════════╡
# │ David ┆ 80000 │
# │ Charlie ┆ 70000 │
# └─────────┴────────┘
# This output shows that only two rows ("Charlie" and "David") are left after filtering by age > 30.
# The rows are sorted by salary in descending order, with "David" (salary 80000) appearing first, followed by "Charlie" (salary 70000).
Why Polars?
So, if you’re dealing with large-scale data processing, need parallel execution, or want a memory-efficient solution, Polars is the superior choice for modern data science and analytics..Pandas might be your first love, but Polars is the one who can handle the heavy lifting.
4. Upgrade Your Testing Game: Replace unittest with pytest
unittest? Sure, it works, but come on, it’s 2025. You ain’t pulling any bitches with that. It’s like trying to impress someone with a flip phone when everyone’s rocking iPhones. Yeah, it works, but it’s a total hassle. pytest: it’s the cool, modern testing framework that makes writing and reading tests way easier.
Wait, What’s unittest?
For the uninitiated, unittest is Python's built-in testing framework, but it often feels outdated with verbose syntax and repetitive boilerplate. With, you get powerful features like flexible fixture management, automatic test discovery, and built-in parameterization (using @pytest.mark.parametrize) to easily run the same test with different inputs. It also supports parallel test execution via pytest-xdist, which boosts performance for large test suites.
Example:
# test_sample.py
# Importing pytest library for testing
import pytest
# Simple function to add two numbers
def add(x, y):
return x + y
# Test function to check if add(x, y) returns the correct result
def test_add(): # Note: function started with "test_"
# Checking if 2 + 3 equals 5
assert add(2, 3) == 5 # If the add function works, this should pass
# No output is printed because pytest handles the test results automatically
# Expected output after running pytest:
# ===================== test session starts =====================
# collected 1 item
#
# test_sample.py . [100%]
#
# ===================== 1 passed in 0.03 seconds =====================
Why test_?
By using the test_ prefix, you make it clear to pytest that these functions are supposed to be tests. It’s part of pytest's convention to discover and run the correct functions without any additional configuration.
In short, pytest replaces the clunky setup of unittest and makes testing more efficient, flexible, and scalable.
Libraries That Deserve More Attention in 2025
1. BeeWare for Cross-Platform Python App Development
BeeWare is an emerging Python framework that deserves more attention, especially in 2025. It allows Python developers to write native apps across multiple platforms (desktop, mobile, web) using the same codebase. Unlike traditional desktop frameworks like PyQt or Tkinter, BeeWare goes further by enabling deployment on Android, iOS, and even WebAssembly. One key feature of BeeWare is its cross-platform nature, so you can write an app once and run it everywhere.
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Getting Started with BeeWare
Before running the examples, make sure you have BeeWare installed. Follow the official installation guide to set up your environment.
Once installed, you can start building cross-platform apps with BeeWare! Here is an example:
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Example:
# Install BeeWare dependencies
# Follow this link to install: https://docs.beeware.org/en/latest/tutorial/tutorial-0.html#install-dependencies
# Example BeeWare Code: Simple App with Toga
import toga
from toga.style import Pack
from toga.style.pack import COLUMN, ROW
class MyApp(toga.App):
def startup(self):
# Create a simple button and label
self.main_window = toga.MainWindow(self.name)
self.label = toga.Label('Hello, BeeWare!',
style=Pack(padding=10))
self.button = toga.Button('Click Me!',
on_press=self.on_button_press,
style=Pack(padding=10))
# Create a Box to hold the button and label
self.box = toga.Box(children=[self.label, self.button],
style=Pack(direction=COLUMN, padding=10))
self.main_window.content = self.box
self.main_window.show()
def on_button_press(self, widget):
self.label.text = 'Button Pressed!'
# Run the app
def main():
return MyApp('My BeeWare App', 'org.beeware.helloworld')
if __name__ == '__main__':
main().main_loop()
This is what BeeWare provides. Tools to help you write Python code with a rich, native user interface; and the libraries and support code necessary to get that code running on iOS, Android, macOS, Linux, Windows, tvOS, and more.
2. pydantic for Data Validation
Validating data can be a chore. pydanticis a Python library that validates and parses data based on type hints. If you’re dealing with messy or untrusted data—like API responses, user inputs, or configs—Pydantic ensures your data is clean, structured, and error-free.
from pydantic import BaseModel, ValidationError, Field
from typing import List, Optional
# Define a data model using Pydantic
class User(BaseModel):
id: int # Automatically validates that id must be an integer
name: str # Validates that name must be a string
age: Optional[int] = None # Optional field with default value of None
email: str = Field(..., regex=r'^\S+@\S+\.\S+$') # Ensures email follows proper format
tags: List[str] = [] # Ensures 'tags' is a list of strings, default is an empty list
# Example usage
try:
# Creating a valid user instance
user = User(id=1, name="John Doe", age=30, email="john.doe@example.com", tags=["developer", "writer"])
print(user) # Output: id=1 name='John Doe' age=30 email='john.doe@example.com' tags=['developer', 'writer']
except ValidationError as e:
# Handles validation errors
print(e) # This block won't execute for valid data
# Demonstrating validation
try:
# Attempting to create a user with invalid email
invalid_user = User(id="abc", name="Invalid User", email="not-an-email")
except ValidationError as e:
print(e)
"""
Output:
1 validation error for User
id
value is not a valid integer (type=type_error.integer)
email
string does not match regex "^\\S+@\\S+\\.\\S+$" (type=value_error.str.regex; pattern=^\\S+@\\S+\\.\\S+$)
"""
# Demonstrating default values
user_without_age = User(id=2, name="Jane Doe", email="jane.doe@example.com")
print(user_without_age)
# Output: id=2 name='Jane Doe' age=None email='jane.doe@example.com' tags=[]
Why Use pydantic?
It automatically validates and converts data types without extensive custom configurations. Unlike Marshmallow, which requires explicit schema definitions, Pydantic integrates seamlessly with frameworks like FastAPI and offers better performance due to its Cython optimization, making it a go-to choice for APIs and data-driven projects.
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, ensures reproducible builds, and simplifies publishing to PyPI. It improves transparency in dependency graphs, making it a powerful alternative to older tools like pip and setuptools.
Example:
# Create a new Poetry project
poetry new my_project
# Navigate into the project directory
cd my_project
# Add a dependency to the project
poetry add requests
# Add a dev dependency (e.g., pytest for testing)
poetry add pytest --group dev
# View installed dependencies
poetry show
# Install all dependencies listed in pyproject.toml
poetry install
# Remove a dependency
poetry remove requests
# Run the virtual environment shell
poetry shell
# Run a script inside the Poetry environment
poetry run python my_script.py
# Update dependencies to their latest versions
poetry update
# Publish the package to PyPI
poetry publish --build
Learn more about poetry here . It’s a great tool
Why Use poetry?
It stands out for its ease of use when adding, updating, or removing dependencies and its seamless publishing workflow to PyPI
4. fastapi for Modern APIs
FastApi is a high-performance Python framework commonly used as an alternative to Flask or Django REST Framework for building APIs. It is ideal for both small projects and large-scale applications due to its scalability and efficiency. 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
Example Integrating FastAPI and Pydantic:
from fastapi import FastAPI, Depends, HTTPException, Query
from pydantic import BaseModel, Field
from typing import Optional
app = FastAPI()
# Dependency Injection Example
def fake_db():
return {"db": "Connected to database"}
# Data Validation with Pydantic
class Item(BaseModel):
name: str = Field(..., example="Laptop", description="Name of the item")
price: float = Field(..., gt=0, example=999.99, description="Price must be greater than 0")
in_stock: bool = Field(default=True, example=True)
# API Endpoints
@app.post("/items/", tags=["Items"])
async def create_item(
item: Item,
db=Depends(fake_db),
discount: Optional[float] = Query(0.0, ge=0, le=1, description="Discount between 0 and 1"),
):
if not db.get("db"):
raise HTTPException(status_code=500, detail="Database not connected")
final_price = item.price * (1 - discount)
return {
"item": item,
"final_price": final_price,
"db_status": db["db"],
}
# Async API Example
@app.get("/async-process/", tags=["Async"])
async def async_task():
import asyncio
await asyncio.sleep(2) # Simulate a long-running task
return {"message": "This endpoint is async!"}
# Automatic Interactive API Documentation
@app.get("/docs/", tags=["Docs"])
async def docs_info():
return {"info": "Visit /docs for Swagger or /redoc for ReDoc documentation!"}
Why Use fastapi?
Its asynchronous capabilities, powered by Starlette and Pydantic, allow it to handle high-concurrency use cases, making it popular for modern microservices and API-first designs.
5. asyncpg for Database Operations
asyncpg is a highly performant, asynchronous PostgreSQL database driver for Python. Want fast queries in your Python app? Boom, no more slow, blocking calls that make your app crawl like you’re still running on dial-up internet. Unlike traditional synchronous libraries like psycopg2, asyncpg uses asynchronous programming to optimize database operations without blocking other tasks in the application, especially useful in modern web frameworks like FastAPI, Tornado, or Sanic.
Example:
import asyncpg
import asyncio
# Connect to the PostgreSQL database
async def connect_db():
conn = await asyncpg.connect(user='your_user', password='your_password', database='your_db', host='localhost')
return conn
# Create a table in the database
async def create_table():
conn = await connect_db()
await conn.execute('''
CREATE TABLE IF NOT EXISTS users(
id SERIAL PRIMARY KEY,
name TEXT,
age INT
)
''')
print("Table created successfully")
await conn.close()
# Insert data into the table
async def insert_user(name: str, age: int):
conn = await connect_db()
await conn.execute('INSERT INTO users(name, age) VALUES($1, $2)', name, age)
print(f"User {name} inserted")
await conn.close()
# Fetch all users from the table
async def fetch_users():
conn = await connect_db()
users = await conn.fetch('SELECT * FROM users')
for user in users:
print(f"ID: {user['id']}, Name: {user['name']}, Age: {user['age']}")
await conn.close()
# Main function to demonstrate usage
async def main():
await create_table()
await insert_user('Alice', 30)
await fetch_users()
# Run the main function
asyncio.run(main())
Why Use asyncpg?
To wrap it up, asyncpg is a fast, asynchronous PostgreSQL driver for Python, offering non-blocking database operations. Unlike traditional libraries like psycopg2, it helps your app run more efficiently by preventing slow database queries from holding up other tasks. It’s perfect for high-performance web frameworks like FastAPI.
- duckdb for In-Memory Analytics DuckDB is a fast, in-memory database designed for running complex analytical queries efficiently. Unlike traditional databases like PostgreSQL, DuckDB works directly with data in memory, allowing you to quickly process large datasets without needing external servers.
With features like vectorized query execution, support for formats like Parquet, Arrow, and Pandas, and efficient handling of JSON and semi-structured data, DuckDB is a top choice for analytics. It outshines tools like Spark by eliminating complex setups and offering unparalleled performance for SQL analytics and large-scale data queries, making it the go-to solution for developers and data engineers today
Example:
import duckdb
# Create a DuckDB in-memory database and a table to store some personal data
conn = duckdb.connect()
# Create a table to store user details
conn.execute("""
CREATE TABLE users (
id INTEGER,
name VARCHAR,
medium_handle VARCHAR
);
""")
# Insert a record with Harshvardhan's details
conn.execute("""
INSERT INTO users VALUES (1, 'Harshvardhan', '@decodebuzzing');
""")
# Querying the database to fetch user details
result = conn.execute("SELECT * FROM users;").fetchall()
# Print the results
print(result) # Output: [(1, 'Harshvardhan', '@decodebuzzing')]
# Perform a query to search by name
result_by_name = conn.execute("SELECT * FROM users WHERE name = 'Harshvardhan';").fetchall()
# Print the search result
print(result_by_name) # Output: [(1, 'Harshvardhan', '@decodebuzzing')]
# Demonstrating SELECT with a WHERE clause and LIMIT
limited_result = conn.execute("SELECT * FROM users LIMIT 1;").fetchall()
print(limited_result) # Output: [(1, 'Harshvardhan', '@decodebuzzing')]
# Demonstrating aggregation (count the number of users)
count_users = conn.execute("SELECT COUNT(*) FROM users;").fetchone()
print(count_users) # Output: (1,)
# Join example: Joining two dummy tables for more complex queries (imagine having another table like posts)
conn.execute("""
CREATE TABLE posts (
post_id INTEGER,
user_id INTEGER,
content VARCHAR
);
""")
conn.execute("""
INSERT INTO posts VALUES (1, 1, 'Exploring DuckDB with Python!');
""")
# Perform a JOIN to get posts by the user
joined_result = conn.execute("""
SELECT users.name, posts.content
FROM users
JOIN posts ON users.id = posts.user_id;
""").fetchall()
# Print the JOIN result
print(joined_result) # Output: [('Harshvardhan', 'Exploring DuckDB with Python!')]
# Closing the connection
conn.close()
DuckDB is easy to install and deploy. It has zero external dependencies and runs in-process in its host application or as a single binary.
Final Thoughts
In 2025, it’s time to rethink your Python toolbox. While classics like Requests and Pandas have their place, emerging libraries like httpx, Polars, rich, and duckdb can streamline your workflows.
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