I love that all that I need is in one place. Even without AI subscription I can have AI suggestion for autocomplete and it's just so easier to do some repetitive job. Working with DB? You can execute SQL snippet inside your code. Working with pandas? You can open dataframe as table. Working with Django/FastAPI/Flask? Here are endpoints connection. Working in team, but your project is non-standard? Here are shared templates that you can define yourself. And PyCharm is becoming better each year. They implements support for current stack (like using conda/uv/uvloop/etc). I'm using PyCharm Pro for 8+ years and I totally recommend it for all Python projects Review collected by and hosted on G2.com.

There were a lot of crashes years ago. There are still some issues, but it's not critical. For example, after updating PyCharm to latest stable version, some plugins (including plugins by JetBrains) are not supported and there is no backward compatibility, so you have to just turn off them. It's a little annoying Review collected by and hosted on G2.com.

PyCharm Professional provides the most comprehensive environment for machine learning workflows. The built-in Scientific mode acts as a specialized view that includes a variables panel, a plots gallery, and an interactive console, making data exploration highly efficient. The native Jupyter Notebook integration allows for opening, creating, and running .ipynb files directly within the IDE without switching contexts. For training large models, the ability to connect to remote interpreters on SSH servers or Docker containers is invaluable. This architecture enables writing and debugging code locally while executing heavy training jobs on remote GPU instances. Furthermore, the robust code intelligence, advanced debugger, and built-in database tools are fully configured out of the box, ensuring that complex machine learning repositories remain well-structured and maintainable. Review collected by and hosted on G2.com.

The IDE is incredibly memory-intensive and heavy on system resources, particularly when indexing massive machine learning repositories or parsing large datasets. Because the core data science capabilities—like Jupyter integration, the DataFrame viewer, and remote interpreters—are locked behind the Professional Edition, the subscription price can be steep for individual practitioners. Additionally, the sheer volume of integrated tools and deep configuration menus presents a significant learning curve compared to lighter text editors. Review collected by and hosted on G2.com.

What I like most about PyCharm is its intelligent code completion and strong debugging tools. The IDE catches errors as I write and offers helpful suggestions that speed up development. Its interface is clean and well organized, which makes it easier to manage larger projects without feeling overwhelmed. I also appreciate the convenient integration with Git and virtual environments, since it lets me manage version control and project dependencies in one place. Overall, PyCharm boosts my productivity by cutting down on repetitive tasks and making code navigation much smoother. Review collected by and hosted on G2.com.

One drawback of PyCharm is that it can be resource-intensive, especially when working on larger projects or on systems with limited memory. Startup time can sometimes feel slower compared to lighter code editors. There is also a learning curve for new users because of the large number of features and configuration options available. While the Community Edition is useful, some advanced features are only available in the paid version, which may not be ideal for individual developers or students. Review collected by and hosted on G2.com.

It is very helpful and has very rare modules which are not usually found or can be imported in other code editors. Very helpful for python starters and UI is very similar to VS code. Review collected by and hosted on G2.com.

It takes a lot of time for indexing, which is frustrating as we will be only be able to work on a project once indexing is done. To run a set of code you need the indexing to be done fully. VS code is much better than PyCharm for codeforces contests. Both of them have the helper extensions but VS Code UI/UX is much better for a contest environment. Review collected by and hosted on G2.com.

It gave me access to Python without being a developer. It made it relatively easy for me to create the first basic tools and understanding the basic behind more complex coding.

Also, for my limited needs, it's free. Review collected by and hosted on G2.com.

At the beginning I had some basic barriers (like I had to understand what the terminal was for, how do download some needed tools, how to access previous projects, as well). Let's say that for someone that has never coded in his life, the tool could be a bit more intuitive / user friendly Review collected by and hosted on G2.com.

I work with Python in PyCharm and what I like the most is how smart and efficient it feels to work with.

Easy to write clean code and fix issues quickly with features such as intelligent code completion, debugging and built-in tools that keep me productive. Review collected by and hosted on G2.com.

One downside to PyCharm is that it can be a bit of a resource hog and slow, especially on large projects or weaker machines.

Startup time can be a little long and some advanced features are only available in the paid version. Review collected by and hosted on G2.com.

As a Machine Learning Engineer at a mid-sized tech company, I use PyCharm Professional as my central command station for taking models from the research phase all the way into production. My day-to-day involves pulling messy datasets from our internal warehouses, writing extensive preprocessing pipelines in Pandas, and training deep learning models using PyTorch. Beyond just the data science work, I use it to wrap our trained models into FastAPI applications, configure Docker containers, and manage our deployment code. It is where I write, test, and package everything before it hits our cloud infrastructure.The standout feature for my specific workflow is the remote development capability. Training large models locally isn't an option, and PyCharm allows me to seamlessly link my local editor to our remote GPU servers. I can write and navigate code locally with zero latency, but when I hit run, the code executes on the heavy hardware over SSH. The visual debugger is also a total game-changer for machine learning. Instead of littering my code with print statements to figure out where a tensor shape mismatch occurred, I can pause execution, visually inspect the multi-dimensional arrays, and see exactly what is going wrong in memory. I also have to praise the built-in database tools. Being able to query our PostgreSQL databases, inspect the schema, and directly pull that data into a Python environment without ever switching windows keeps me completely focused. Review collected by and hosted on G2.com.

For all of its intelligent features, PyCharm is notoriously resource-hungry. If you are opening a massive repository with a bloated virtual environment, the initial indexing phase takes so long that you might as well go make a cup of coffee. Running PyCharm alongside a few local Docker containers will quickly eat up your RAM and make your laptop fans sound like a jet engine. Additionally, while JetBrains has tried hard to improve their Jupyter Notebook integration, it still feels clunky and rigid compared to just running a notebook natively in a web browser. For quick, exploratory data analysis and plotting, I usually avoid PyCharm entirely because it feels too heavy and restrictive for that specific task. Review collected by and hosted on G2.com.

My primary roles at a mid-size tech firm include data science duties like EDA,/model training and creating scalable/pipelines. We use PyCharm Pro for most of the work, mainly to expand our code from the raw, non-production-ready, hacky code we produce in standalone Jupyter notebooks to well-designed, robust, production-ready Python apps. The day is a long one of writing data transformation code, troubleshooting scripts, managing virtual machines and environment, before getting them to our cloud platform.The built-in PyCharm code intelligence and debugging are really unique features for the Python space. The visual debugger allows me to stop the program, making a list of all of the variables right there, including large, deeply nested data structures, or complex machine-learning vectors; that alone has saved me several hours of having to write all kinds of temporary print statements. Additionally, the automated refactoring tools are A LOT of help, for instance when my script becomes quite unreadable, I can rename a variable, extract a method or move a class from one place to the another in the whole project and the location reference only changes for the refactored code without affecting anything. Also, the tools and functions for creating, testing and running complex SQL queries on our data warehouse are included as part of the Professional version, so I can do these things in the same application without ever leaving it for another database tool. Review collected by and hosted on G2.com.

The major drawback is, of course, the weight and intensive usage of the software. Whenever I use the huge repository to open a new set of files, to pull new changes, or to install new library, the background indexing kicks in and my laptop fans make an immediate "jet plane" sound. Uses a lot of memory, can be a big constraint if I need to run my own Docker containers on my home machine or load big sets of data into memory to be analyzed. Also I'm always very careful not to double click in the project explorer on a multi-gigabyte csv file, otherwise pycharm will show the text in it and stand completely inactive for the remainder of the time that it will have to be forced quit. Review collected by and hosted on G2.com.

The most helpful thing is the ui it is very simple and not very difficult to find anything. it ability to run large python files is the best thing about the pycharm. While working on building AI models our team used pycharm as the code editor as every library that we needed for our project was available and we just needed to integrate it. Review collected by and hosted on G2.com.

It lags a lot like when i try to upload a large folder even after full upload sometimes it is removed which make the work very hectic. It does not support any AI integrations which makes difficult to resolve doubts in our code or correct the errors in our code. While working we looked for AI to review our code but it was not there in pycharm editor this was not a good thing in today's time. Review collected by and hosted on G2.com.

I work as a software engineer at a mid-sized tech company where our team mainly builds backend REST APIs and data processing pipelines. My day-to-day involves writing a massive amount of Python code, managing local Docker containers, and running complex database queries. I use PyCharm Professional as my primary IDE because it acts as a centralized workstation for all of these tasks, preventing me from having to constantly juggle five different applications just to get a single feature built.What I appreciate most about PyCharm is how deeply it actually understands Python. The code completion and refactoring tools go way beyond basic text matching; making widespread changes like renaming classes or extracting methods across a large codebase feels incredibly safe and intuitive. The built-in database integration is another massive benefit for my workflow. Instead of switching back and forth between my code and a separate SQL client like DBeaver, I can inspect tables, run queries, and modify schemas right next to my Python files. I also have to highlight the native visual debugger. Being able to step through complex, multi-layered API requests and inspect local variables in real-time has saved me countless hours compared to just dropping print statements everywhere. Review collected by and hosted on G2.com.

On the downside, PyCharm is notoriously heavy and takes a serious toll on system resources. When I have a couple of large microservices open along with several Docker containers running in the background, my laptop definitely starts to lag, and the initial indexing time when you first clone and open a new repository can really test your patience. The sheer number of features, tool windows, and settings menus can also be quite overwhelming. It took me a good few months to figure out where everything was, and I notice that newer junior developers joining our team often feel intimidated by the interface at first glance. Review collected by and hosted on G2.com.