The State of Python in 2025
It has been interesting to see how Python evolved over the past 10 years. Trends have come and gone. Millions of libraries minted.
What's hot in Python seems to often mirror big technology trends. For example, with recent advancements in AI, controlling AI models via Python is now common. Amazon's Boto3 is one of the most downloaded libraries for cloud computing in Python. Where tech wants to go, Python's popular culture tends to follow.
In any given year, there might not be new foundational libraries introduced. Or maybe they do get introduced, but it takes time for them to go mainstream.
Here are some popular Python libraries and the year they were introduced:
- FastAPI: 2018
- uv: 2023
- ruff: 2023
- Django: 2005
- Flask: 2010
- pandas: 2008
- requests: 2011
- Google's Gemini AI model
Anecdotally from murmurs online, FastAPI seems to be picking up steam in 2025. It was introduced back in 2018. This is a great example of how it is extremely rare for a library to burst out of nowhere and immediately shake up the status quo.
It takes a library a long time to reach critical mass in popularity and to attain legendary status library like heavyweights numpy, pandas, Django and FastAPI have. These libraries are well respected by Pythonistas and for good reason. They provide much needed functionality in an intuitive Python interface.
There should only be one obvious way to do it. Yet, it doesn't stop Python developers from repeatedly reinventing solved problems. However, sometimes they do improve upon the existing incumbents in a space. Requests is the obvious example when the urllib HTTP library was already in the standard library.
While lots of future Python staple libraries could be launching this year, it is rare for one to make a splash seemingly immediately as black did in 2018 and ruff did in 2023.
In conclusion, the state of Python is healthy in 2025. Every year, new libraries continually rise to the top of Pythonistas' minds. Advancements in technology are moving forward and so follows this programming language. But it's not just the cutting edge where we see improvements. The needs of developers are the same as 10 years ago. We need to build apps and websites. We need to automate data transfer between systems. We need to make HTTP requests to APIs. Integrate Rust to speed up the Python code. We need to make things. We need to save time. Fortunately, Python is usually there to help us do it all. With advancements in AI, we don't need to write the code anymore. What matters is the idea and the execution.
Enough About Now. Predictions for the Future?
New library trends will continue to follow tech advancements:
- The ongoing AI trend. Useful libraries will extend or connect to APIs or LLM models in novel ways or connect to services previously unable to access AI.
- Autonomous driving. Advances in controlling your car autonomously via Python.
- Robotics. Controlling robotic hardware via Python will become more available.
- Python will get faster. With every new release, Python's performance is improved. This trend will continue.
- Businesses that are lucky enough to tap into AI-fueled profits will thrive. They'll employ Python developers to maintain models.