10 Python Libraries Every Developer Should Know

In this article, we’ll go over Python libraries for tasks like logging, unit testing, data handling, and more — each with features that can simplify your application development.



10 Python Libraries Every Developer Should Know
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Are you a developer who enjoys coding in Python? If so, there are a few Python libraries you can add to your dev toolbox.

As a developer, you should be comfortable with debugging, logging, and unit testing. Besides, you’ll need to work with data sources, account for data validation, and build APIs.

In this article, we’ll go over Python libraries for tasks like logging, unit testing, data handling, and more — each with features that can simplify your application development. Let’s get started.

 

1. SQLAlchemy: For Database Interactions

 

SQLAlchemy is an SQL toolkit and Object Relational Mapper (ORM) for Python. You’ll use it often for database interaction in web and backend applications.

This provides a Pythonic way to interact with databases. It lets you manage database schema, perform complex queries, and handle transactions—all from within a Python script.

Key Features

  • Flexible ORM that maps database tables to Python objects
  • Works with most SQL dialects
  • Supports complex SQL queries and relationships

Learning Resources

 

2. Beautiful Soup: For Web Scraping

 

Beautiful Soup is a Python library for quick and easy web scraping that parses HTML and XML documents.

Beautiful Soup is the go-to library for extracting data from web pages. Great for tasks like data collection, automation, and building web crawlers.

Key Features

  • Simple parsing of HTML and XML documents
  • Easy-to-use syntax for navigating and searching HTML trees

Learning Resources

 

3. Pytest: For Unit Testing

 

Pytest is a popular testing framework for Python. It’s both simple and more flexible than the built-in unittest module.

It is used for writing, running, and organizing test cases in Python projects.

Key Features

  • Simple syntax that scales well for complex test suites
  • Supports parameterized testing, making it ideal for data-driven tests
  • Rich plugin ecosystem and built-in fixtures

Learning Resources

 

4. Pydantic: For Data Validation

 

Pydantic is a data validation library. It uses Python type hints to enforce data integrity in applications.

It is commonly used to validate and parse data from APIs or configuration files. Which ensures type-safety and consistency in applications.

Key Features

  • Type validation based on Python type hints
  • Useful for validating incoming API requests or configuration files
  • Integrates with FastAPI

Learning Resources

 

5. FastAPI: For Building APIs

 

FastAPI is a popular Python web framework for building APIs.

You can build fast, asynchronous web APIs with FastAPI. Besides, FastAPI supports data validation with Pydantic and auto-generates documentation for the API based on the OpenAPI specification.

Key Features

  • High-performance API creation with ASGI and async support
  • Automatic generation of interactive API documentation
  • Uses Pydantic for data validation

Learning Resources

 

6. IceCream: For Debugging

 

IceCream is a lightweight debugging tool that makes it easy to print and understand variables and expressions within your code.

IceCream is often used as a quick and helpful debugging tool—giving clear, readable output of expressions and their values as you code.

Key Features

  • Minimalistic syntax for quick debugging
  • Clear, human-readable output that shows variable values in context

Learning Resources

 

7. Loguru: For Advanced Logging

 

Loguru is a simple yet powerful logging library for Python, offering advanced features without complex setup.

This library is great for logging application events and errors, offering flexible and customizable logging for modern applications.

Key Features

  • Simple API that allows for easy setup and customization
  • Automatic log rotation and retention
  • Powerful formatting options and contextual logging.

Learning Resources

 

8. Watchdog: For Monitoring File System Events

 

Watchdog is a Python library for monitoring file system changes and triggering actions based on those changes.

It is used in automation scripts, for tasks like file synchronization, logging changes in directories, and automated deployments.

Key Features

  • Monitors file system events in real-time
  • Cross-platform support for different operating systems
  • Integrates well with automation workflows

Learning Resources

 

9. Pendulum: For Date and Time Handling

 

Pendulum is a user-friendly date and time library that simplifies date-time manipulation and makes handling time zones easier.

Pendulum is great for projects requiring date, time, and datetime objects—allowing for easy and intuitive date and time manipulation.

Key Features

  • Easy-to-use methods for manipulating dates and times
  • Time zone-aware and locale-friendly
  • Fully compatible with Python’s datetime module

Learning Resources

 

10. Pandas/Polars: For Data Analysis

 

Pandas and Polars are both Python libraries for data analysis. Learning these data analysis libraries can be useful even if you don't wish to switch to data analytics.

You can use either of them for data analysis. It’s sometimes easier to start with Pandas and move to Polars offering a faster, more efficient alternative for large datasets.

These libraries are essential for data analysis tasks, from cleaning and transforming data to aggregating and visualizing.

Key Features

  • Pandas: Tool for robust data manipulation and analysis, with support for complex operations on large datasets
  • Polars: Optimized for speed and memory efficiency, leveraging parallel processing and a powerful API

Learning Resources

 

Wrapping Up

 
That’s a wrap. I hope you found this article helpful.

Each of these Python libraries can streamline development across different areas—from database interactions to unit testing, building APIs, and more—making them useful in a developer’s toolkit.

If you’re interested in data science, you may find 10 Python Libraries Every Data Scientist Should Know helpful.
 
 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she's working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.