PyCharm Reviews & Product Details

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.

PyCharm makes Python development effortlessly efficient, its intelligent auto completion, powerful refactoring tools, and top-tier debugging capabilities are absolute lifesavers. Having Git integration, virtual env management, and terminal tool built directly into one clean workspace saves so much time. Review collected by and hosted on G2.com.

It's resource heavy, leading to high RAM and CPU usage that slows down older machines. Initial setup and project indexing takes time. Review collected by and hosted on G2.com.

Easy to use, configure, and personalize, with plenty of plugins and extensions you can add to really make it your own. It supports different AI agents and easy to integrate them. The performance is good but you need a good computer. Review collected by and hosted on G2.com.

The heavy process that runs, is not valid for outdated computers but very useful for the new ones. 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.

Ability to easily set up projects and utilize libraries installed by anaconda. It has similar execution speed to spyder, but file management is easier. The ability to simply run scripts and notebooks is great. The UI is fairly intuitive and project workflow is smooth. Having a free tier for academics and students is nice. Review collected by and hosted on G2.com.

Variable visualization is not as easy as Spyder or other IDEs. It's a little too beefy for writing short codes to answer small questions rather than larger projects. 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.