This is a guest post from Michael Kennedy, the founder of Talk Python and a PSF Fellow.Welcome to the highlights, trends, and key actions from the eighth annual Python Developers Survey. This survey is conducted as a collaborative effort between the Python Software Foundation and JetBrains PyCharm team.My name is Michael Kennedy, and Ive analyzed the more than 30,000 responses to the survey and pulled out the most significant trends and predictions, and identified various actions that you can take to improve your Python career.I am in a unique position as the host of the Talk Python to Me podcast. Every week for the past 10 years, Ive interviewed the people behind some of the most important libraries and language trends in the Python ecosystem. In this article, my goal is to use that larger community experience to understand the results of this important yearly survey.If your job or products and services depend on Python, or developers more broadly, youll want to read this article. It provides a lot of insight that is difficult to gain from other sources.Key Python trends in 2025Lets dive into the most important trends based on the Python survey results.As you explore these insights, having the right tools for your projects can make all the difference. Try PyCharm for free and stay equipped with everything you need for data science, ML/AI workflows, and web development in one powerful Python IDE. Try PyCharm for free Python people use PythonLets begin by talking about how central Python is for people who use it. Python people use Python primarily. That might sound like an obvious tautology. However, developers use many languages that are not their primary language. For example, web developers might use Python, C#, or Java primarily, but they also use CSS, HTML, and even JavaScript.On the other hand, developers who work primarily with Node.js or Deno also use JavaScript, but not as their primary language.The survey shows that 86% of respondents use Python as their main language for writing computer programs, building applications, creating APIs, and more.We are mostly brand-new programmersFor those of us who have been programming for a long time I include myself in this category, having written code for almost 30 years now its easy to imagine that most people in the industry have a decent amount of experience. Its a perfectly reasonable assumption. You go to conferences and talk with folks who have been doing programming for 10 or 20 years. You look at your colleagues, and many of them have been using Python and programming for a long time.But that is not how the broader Python ecosystem looks.Exactly 50% of respondents have less than two years of professional coding experience! And 39% have less than two years of experience with Python (even in hobbyist or educational settings).This result reaffirms that Python is a great language for those early in their career. The simple (but not simplistic) syntax and approachability really speak to newer programmers as well as seasoned ones. Many of us love programming and Python and are happy to share it with our newer community members.However, it suggests that we consider these demographics when we create content for the community. If you create a tutorial or video demonstration, dont skimp on the steps to help people get started. For example, dont just tell them to install the package. Tell them that they need to create a virtual environment, and show them how to do so and how to activate it. Guide them on installing the package into that virtual environment.If youre a tool vendor such as JetBrains, youll certainly want to keep in mind that many of your users will be quite new to programming and to Python itself. That doesnt mean you should ignore advanced features or dumb down your products, but dont make it hard for beginners to adopt them either.Data science is now over half of all PythonThis year, 51% of all surveyed Python developers are involved in data exploration and processing, with pandas and NumPy being the tools most commonly used for this.Many of us in the Python pundit space have talked about Python as being divided into thirds: One-third web development, one-third data science and pure science, and one-third as a catch-all bin.We need to rethink that positioning now that one of those thirds is overwhelmingly the most significant portion of Python.This is also in the context of not only a massive boom in the interest in data and AI right now, but a corresponding explosion in the development of tools to work with in this space. There are data processing tools like Polars, new ways of working with notebooks like Marimo, and a huge number of user friendly packages for working with LLMs, vision models, and agents (e.g. Transformers, Diffusers, smolagents, LangChain/LangGraph, LlamaIndex).Pythons center of gravity has indeed tilted further toward data/AI.Most still use older Python versions despite benefits of newer releasesThe survey shows a distribution across the latest and older versions of the Python runtime. Many of us (15%) are running on the very latest released version of Python, but more likely than not, were using a version a year old or older (83%).The survey also indicates that many of us are using Docker and containers to execute our code, which makes this 83% or higher number even more surprising. With containers, just pick the latest version of Python in the container. Since everything is isolated, you dont need to worry about its interactions with the rest of the system, for example, Linuxs system Python. We should expect containerization to provide more flexibility and ease our transition towards the latest version of Python.So why havent people updated to the latest version of Python? The survey results give two primary reasons.The version Im using meets all my needs (53%)I havent had the time to update (25%)The 83% of developers running on older versions of Python may be missing out on much more than they realize. Its not just that they are missing some language features, such as the except keyword, or a minor improvement to the standard library, such as tomllib. Python 3.11, 3.12, and 3.13 all include major performance benefits, and the upcoming 3.14 will include even more.Whats amazing is you get these benefits without changing your code. You simply choose a newer runtime, and your code runs faster. CPython has been extremely good at backward compatibility. Theres rarely significant effort involved in upgrading. Lets look at some numbers.48% of people are currently using Python 3.11. Upgrading to 3.13 will make their code run ~11% faster end to end while using ~10-15% less memory.If they are one of the 27% still on 3.10 or older, their code gets a whopping ~42% speed increase (with no code changes), and memory use can drop by ~20-30%!So maybe theyll still come back to Well, its fast enough for us. We dont have that much traffic, etc.. But if they are like most medium to large businesses, this is an incredible waste of cloud compute expense (which also maps to environmental harm via spent energy).Research shows some estimates for cloud compute (specifically computationally based):Mid-market / medium businessTotal annual AWS bill (median): ~ $2.3 million per year (vendr.com)EC2 (compute-instance) share (~ 5070 % of that bill): $1.151.6 million per year (cloudlaya.com)Large enterpriseTotal annual AWS bill: ~ $2436 million per year (i.e. $23 million per month) (reddit.com)EC2 share (~ 5070 %): $1225 million per year (cloudlaya.com)If we assume theyre running Python 3.10, thats potentially $420,000 and $5.6M in savings, respectively (computed as 30% of the EC2 cost).If your company realizes you are burning an extra $0.4M-$5M a year because you havent gotten around to spending the day it takes to upgrade, thatll be a tough conversation.Finances and environment aside, its really great to be able to embrace the latest language features and be in lock-step with the core devs significant work. Make upgrading a priority, folks.Python web devs resurgenceFor the past few years, weve heard that the significance of web development within the Python space is decreasing. Two powerful forces could be at play here: 1) As more data science and AI-focused people come to Python, the relatively static number of web devs represents a lower percentage, and 2) The web continues to be frontend-focused, and until Python in the browser becomes a working reality, web developers are likely to prefer JavaScript.Looking at the numbers from 20212023, the trend is clearly downward 45% 43% 42%. But this year, the web is back! Respondents reported that 46% of them are using Python for web development in 2024. To bolster this hypothesis further, we saw web secondary languages jump correspondingly, with HTML/CSS usage up 15%, JavaScript usage up 14%, and SQLs usage up 16%.The biggest winner of the Python web frameworks was FastAPI, which jumped from 29% to 38% (a 30% increase). While all of the major frameworks grew year over year, FastAPIs nearly 30% jump is impressive. I can only speculate why this is. To me, I think this jump in Python for web is likely partially due to a large number of newcomers to the Python space. Many of these are on the ML/AI/data science side of things, and those folks often dont have years of baked-in experience and history with Flask or Django. They are likely choosing the hottest of the Python web frameworks, which today looks like its FastAPI. There are many examples of people hosting their ML models behind FastAPI APIs.The trend towards async-friendly Python web frameworks has been continuing as well. Over at Talk Python, I rewrote our Python web app in async Flask (roughly 10,000 lines of Python). Django has been steadily adding async features and is nearly complete. Though today, at version 5.2, its DB layer needs a bit more work, as the team says: Were still working on async support for the ORM and other parts of Django.Python web servers shift toward async and Rust-based toolsIts worth a brief mention that the production app servers hosting Python web apps and APIs are changing too. Anecdotally, I see two forces at play here: 1) The move to async frameworks necessitates app servers that support ASGI, not just WSGI and 2) Rust is becoming more and more central to the fast execution of Python code (well dive into that shortly).The biggest loss in this space last year was the complete demise of uWSGI. We even did a Python Bytes podcast entitled We Must Replace uWSGI With Something Else examining this situation in detail.We also saw Gunicorn handling less of the async workload with async-native servers such as uvicorn and Hypercorn, which are able to operate independently. Newcomer servers, based on Rust, such as Granian, have gained a solid following as well.Rust is how we speed up Python nowOver the past couple of years, Rust has become Pythons performance co-pilot. The Python Language Summit of 2025 revealed that Somewhere between one-quarter and one-third of all native code being uploaded to PyPI for new projects uses Rust, indicating that people are choosing to start new projects using Rust.Looking into the survey results, we see that Rust usage grew from 27% to 33% for binary extensions to Python packages.We see this in the ecosystem with the success of Polars for data science and Pydantic for pretty much all disciplines. We are even seeing that for Python app servers such as the newer Granian.Typed Python is getting better toolingAnother key trend this year is typed Python. Youve probably seen Python type information in function definitions such as:def add(x: int, y: int) -> int: ...These have been in Python for a while now. Yet, there is a renewed effort to make typed Python more common and more forgiving. Weve had tools such as mypy since typings early days, but the goal there was more along the lines of whole program consistency. In just the past few months, we have seen two new high-performance typing tools released:ty from Astral an extremely fast Python type checker and language server written in Rust.Pyrefly from Meta a faster Python type checker written in Rust.They are both vying to be the next generation tooling for type checking. Moreover, both of these tools provide extremely fast language server protocols (LSPs).Notice anything similar? They are both written in Rust, backing up the previous claim that Rust has become Pythons performance co-pilot.By the way, I interviewed the team behind ty when it was announced a few weeks ago if you want to dive deeper into that project.Code and docs make up most open-source contributionsThere are many different and unique ways to contribute to open source. Probably the first thing that comes to most peoples minds when they think of a contributor is someone who writes code and adds a new feature to that project. However, there are less visible and important ways to make a contribution, such as triaging issues in reviewing pull requests.So, what portion of the community has contributed to open source, and in which ways have they done so?The survey tells us that one-third of devs contributed to open source. This manifests primarily as code and documentation/tutorial additions.Docs are #1Where do you typically learn as a developer or data scientist? Respondents said that docs are #1. There are many ways to learn languages and libraries, but people like docs best. This is good news for open-source maintainers. This means that the effort put into documentation (and embedded tutorials) is well spent. Its a clear and straightforward way to improve users experience with your project.Moreover, this lines up with Developer Trends in 2025, a podcast panel episode I did with experienced Python developers, including JetBrains own Paul Everitt. The panelists all agree that docs are #1, though the survey ranked YouTube much higher than the panelists, at 51%. Remember, our community has an average of 12 years of experience, and 45% of them are younger than 30 years old.A powerful newcomer is the suite of AI tools we all have been hearing about (and many of us using). AI tools as a learning source increased from 19% to 27% (up 42% year over year)!Postgres reigns as the database king for PythonistasWhen asked which database (if any) respondents chose, they overwhelmingly said PostgreSQL. PostgreSQL is the king of Python databases, and only its growing, going from 43% to 49%. Thats +14% year over year, which is remarkable for a 28-year-old open-source project.One interesting detail here, beyond Postgres being used a lot, is that every single database in the top six grew in usage year over year. This is likely another indicator that web development itself is growing again, as discussed above.Forward-looking trendsAgentic AI will be wildMy first forward-looking trend is that agentic AI will be a game-changer for coding. Agentic AI is often cited as a tool of the much maligned and loved vibe coding. However, vibe coding obscures the fact that agentic AI tools are remarkably productive when used alongside a talented engineer or data scientist.Surveys outside the PSF survey indicate that about 70% of developers were using or planning to use AI coding tools in 2023, and by 2024, around 44% of professional developers use them daily.JetBrains State of Developer Ecosystem 2023 report noted that within a couple of years, AI-based code generation tools went from interesting research to an important part of many developers toolboxes. Jump ahead to 2025, according to the State of Developer Ecosystem 2025 survey, nearly half of the respondents (49%) plan to try AI coding agents in the coming year.Program managers at major tech companies have stated that they almost cannot hire developers who dont embrace agentic AI. The productive delta between those using it and those who avoid it is simply too great (estimated at about 30% greater productivity with AI).Async, await, and threading are becoming core to PythonThe future will be abuzz with concurrency and Python. Weve already discussed how the Python web frameworks and app servers are all moving towards asynchronous execution, but this only represents one part of a powerful trend.Python 3.14 will be the first version of Python to completely support free-threaded Python. Free-threaded Python, which is a version of the Python runtime that does not use the GIL, the global interpreter lock, was first added as an experiment to CPython 3.13.Just last week, the steering council and core developers officially accepted this as a permanent part of the language and runtime. This will have far-reaching effects. Developers and data scientists will have to think more carefully about threaded code with locks, race conditions, and the performance benefits that come with it. Package maintainers, especially those with native code extensions, may have to rewrite some of their code to support free-threaded Python so they themselves do not enter race conditions and deadlocks.There is a massive upside to this as well. Im currently writing this on the cheapest Apple Mac Mini M4. This computer comes with 10 CPU cores. That means until this change manifests in Python, the maximum performance I can get out of a single Python process is 10% of what my machine is actually capable of. Once free-threaded Python is fully part of the ecosystem, I should get much closer to maximum capacity with a standard Python program using threading and the async and await keywords.Async and await keywords are not just tools for web developers who want to write more concurrent code. Its appearing in more and more locations. One such tool that I recently came across is Temporal. This program leverages the asyncio event loop but replaces the standard clever threading tricks with durable machine-spanning execution. You might simply await some action, and behind the scenes, you get durable execution that survives machine restarts. So understanding async and await is going to be increasingly important as more tools make interesting use of it, as Temporal did.I see parallels here of how Pydantic made a lot of people more interested in Python typing than they otherwise would have been.Python GUIs and mobile are risingMy last forward-looking trend is that Python GUIs and Python on mobile are rising. When we think of native apps on iOS and Android, we can only dream of using Python to build them someday soon.At the 2025 Python Language Summit, Russell Keith-Magee presented his work on making iOS and Android Tier 3-supported platforms for CPython. This has been laid out in PEP 730 and PEP 738. This is a necessary but not sufficient condition for allowing us to write true native apps that ship to the app stores using Python.More generally, there have been some interesting ideas and new takes on UIs for Python. We had Jeremy Howard from fast.ai introduce FastHTML, which allows us to write modern web applications in pure Python. NiceGUI has been coming on strong as an excellent way to write web apps and PWAs in pure Python.I expect these changes, especially the mobile ones, to unlock powerful use cases that well be talking about for years to come.Actionable ideasYouve seen the results, my interpretations, and predictions. So what should you do about them? Of course, nothing is required of you, but I am closing out this article with some actionable ideas to help you take advantage of these technological and open-source waves.Here are six actionable ideas you can put into practice after reading this article. Pick your favorite one that youre not yet leveraging and see if it can help you thrive further in the Python space.Action 1: Learn uvuv, the incredible package and Python management tool jumped incredibly from 0% to 11% the year it was introduced (and that growth has demonstrably continued to surge in 2025). This Rust-based tool unifies capabilities from many of the most important ones you may have previously heard of and does so with performance and incredible features.Do you need Python on the machine? Simply run uv venv venv, and you have both installed the latest stable release and created a virtual environment. Thats just the beginning. If you want the full story, I did an interview with Charlie Marsh about the second generation of uv over on Talk Python.If you decide to install uv, be sure to use their standalone installers. It allows uv to manage itself and get better over time.Action 2: Use the latest PythonWe saw that 83% of respondents are not using the latest version of Python. Dont be one of them. Use a virtual environment or use a container and install the latest version of Python. The quickest and easiest way these days is to use uv, as it wont affect system Python and other configurations (see action 1!).If you deploy or develop in Docker containers, all you need to do is set up the latest version of Python 3.13 and run these two lines:RUN curl -LsSf https://astral.sh/uv/install.sh | shRUN uv venv --python 3.13 /venvIf you develop locally in virtual environments (as I do), just remove the RUN keyword and use uv to create that environment. Of course, update the version number as new major versions of Python are released.By taking this action, you will be able to take advantage of the full potential of modern Python, from the performance benefits to the language features.Action 3: Learn agentic AIIf youre one of the people who have not yet tried agentic AI, you owe it to yourself to give it a look. I understand why people avoid using AI and LLMs. For one thing, theres dubious legality around copyrights. The environmental harms can be real, and the threat to developers jobs and autonomy is not to be overlooked. But using top-tier models for agentic AI, not just chatbots, allows you to be tremendously productive.Im not recommending vibe coding. But have you ever wished for a library or package to exist, or maybe a CLI tool to automate some simple part of your job? Give that task to an agentic AI, and you wont be taking on technical debt to your main application and some part of your day. Your productivity just got way better.The other mistake people make here is to give it a try using the cheapest free models. When they dont work that great, people hold that up as evidence and say, See, its not that helpful. It just makes up stuff and gets things wrong. Make sure you choose the best possible model that you can, and if you want to give it a genuine look, spend $10 or $20 for a month to see whats actually possible.JetBrains recently released Junie, an agentic coding assistant for their IDEs. If youre using one of them, definitely give it a look.Action 4: Learn to read basic RustPython developers should consider learning the basics of Rust, not to replace Python, but to complement it. As I discussed in our analysis, Rust is becoming increasingly important in the most significant portions of the Python ecosystem. I definitely dont recommend that you become a Rust developer instead of a Pythonista, but being able to read basic Rust so that you understand what the libraries youre consuming are doing will be a good skill to have.Action 5: Invest in understanding threadingPython developers have worked mainly outside the realm of threading and parallel programming. In Python 3.6, the amazing async and await keywords were added to the language. However, they only applied to I/O bound concurrency. For example, if Im calling a web service, I might use the HTTPX library and await that call. This type of concurrency mostly avoids race conditions and that sort of thing.Now, true parallel threading is coming for Python. With PEP 703 officially and fully accepted as part of Python in 3.14, well need to understand how true threading works. This will involve understanding locks, semaphores, and mutexes.Its going to be a challenge, but it is also a great opportunity to dramatically increase Pythons performance.At the 2025 Python Language Summit, almost one-third of the talks dealt with concurrency and threading in one form or another. This is certainly a forward-looking indicator of whats to come.Not every program you write will involve concurrency or threading, but they will be omnipresent enough that having a working understanding will be important. I have a course I wrote about async in Python if youre interested in learning more about that format. Plus, JetBrains own Cheuk Ting Ho wrote an excellent article entitled Faster Python: Concurrency in async/await and threading, which is worth a read.Action 6: Remember the newbiesMy final action to you is to keep things accessible for beginners every time you build or share. Half of the Python developer base has been using Python for less than two years, and most of them have been programming in any format for less than two years. That is still remarkable to me.So, as you go out into the world to speak, write, or create packages, libraries, and tools, remember that you should not assume years of communal knowledge about working with multiple Python files, virtual environments, pinning dependencies, and much more.Interested in learning more? Check out the full Python Developers Survey Results here.Start developing with PyCharmPyCharm provides everything you need for data science, ML/AI workflows, and web development right out of the box all in one powerful IDE. Try PyCharm for free About the author Michael Kennedy Michael is the founder of Talk Python and a PSF Fellow. Talk Python is a podcast and course platform that has been exploring the Python ecosystem for over 10 years. At his core, Michael is a web and API developer.