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PyCharm is a dedicated Python integrated development environment (IDE) made by JetBrains. For researchers it is a desktop application where you write Python analysis code, run it, step through it with a debugger, manage the packages it needs, and work in Jupyter notebooks, all in one window. Most pages that explain it still describe two separate editions, Community and Professional. JetBrains’ current documentation describes a single, unified PyCharm instead, and that change is the first thing to know before you install it.
What PyCharm is used for in research
JetBrains describes PyCharm as a dedicated Python IDE for Python, web and data science work. In a research setting the relevant pieces are:
- Project-based Python environments. Each project gets its own interpreter and environment, created with venv, pipenv, poetry or Conda, so one study’s package versions do not disturb another’s. That separation is a practical building block of computational reproducibility.
- Running and debugging scripts. Analysis scripts run from the editor with output in a Run tool window.
- Jupyter Notebook support. Notebooks open inside the IDE, with cells, a kernel, and a variables window.
- An interactive Python console and Anaconda support. Useful for exploratory work alongside scripts.
- Scientific and statistical data analysis and visualization tools, listed by JetBrains among the features of the paid Pro subscription.
R is not built in: JetBrains documents R support as a plugin. If your analysis is mainly in R, see RStudio on the cloud instead.
Which edition: the unified PyCharm (2025.1 onward)
Starting with version 2025.1, JetBrains merged the former Community and Professional editions into one unified PyCharm. According to the quick start guide, the core features, including Jupyter Notebook support, are free. A Pro subscription adds the advanced features, and a new installation comes with a 30-day Pro trial; afterwards you either subscribe or continue with the free core features. For current terms, check JetBrains.
PyCharm compared with Jupyter and Anaconda
PyCharm is the editing and running environment, not a Python distribution and not a notebook server of its own. It needs a Python installation to exist first, and it can use Conda environments. For what Anaconda adds to a plain Python install, read the differences between Python and Anaconda. For where notebook code runs when you are not in an IDE, see Google Colab vs Jupyter Notebook.
Install or access (official source only)
Download PyCharm only from JetBrains: jetbrains.com/pycharm/download. The download page provides a SHA checksum you can use to verify the file. Install a Python interpreter first (from python.org); PyCharm does not install Python for you.
Install routes in the JetBrains installation guide
- The JetBrains Toolbox App (the recommended route), at jetbrains.com/toolbox/app.
- A standalone installer: .exe on Windows, .dmg on macOS, tar.gz on Linux.
- Snap on Linux. The guide warns that Snap installs can have performance issues.
- Silent installation on Windows, for managed machines.
System requirements
From the JetBrains installation guide, checked 2026-10-29:
- x86_64 or arm64 processor with 4 cores, 8 GB RAM (3 GB available to the IDE), 10 GB disk space, and a 1280×720 screen.
- Windows 10 or 11; macOS 15 and 26; Linux on Ubuntu 24.04 or 26.04 LTS, Fedora 43 or 44, Debian 13, or Amazon Linux 2023 (GNOME or KDE, glibc 2.28 or later). The guide states that BSD-based operating systems are not supported.
- Python 3.9 to 3.15 is supported. Java is not required, because the IDE bundles its own runtime (JBR 21).
Step-by-step: run a script and a notebook
Create a project and run a script
- Choose File | New Project, or New… on the Welcome screen.
- Set the project location and the Python interpreter. Create the environment with venv, pipenv, poetry or Conda.
- Add your script. Right-click in the editor and choose Run <script name>, or click the Run icon in the gutter beside the
__main__line. - PyCharm creates a temporary Run/Debug configuration automatically and shows the output in the Run tool window.
- To add a library, open the Python Packages tool window, which shows the installed packages and installs new ones.
Work in a Jupyter notebook
- Make sure the Markdown plugin is enabled under Settings | Plugins, on the Installed tab.
- Open or create a notebook in the project.
- Run any code cell. This starts a Jupyter server for the notebook.
- Use the notebook toolbar: Run Cell and Select Below, Run All, Restart Kernel, Clear All Outputs, and Open Jupyter Console. The cell menu adds Run Above Cells, Run Cell and Below and Clear Output.
Output and how to read it
Script output appears in the Run tool window in the order the program printed it. Notebook cell output appears under each cell, and Clear All Outputs removes it all before you save or share a notebook.
To inspect the values your notebook holds in memory, use the Jupyter Variables tool window. It opens after a run when Show Variables tool window on execution is on, under Settings | Jupyter | Jupyter General. In that window, Refresh Variables reloads the list and Sort By reorders it. Restarting the kernel and running all cells from the top is the standard way to confirm that a notebook’s results do not depend on a hidden earlier state, a core habit of reproducible research.
Troubleshooting
- No interpreter to choose in the New Project dialog. PyCharm requires a Python installation; install Python first, then select it as the interpreter.
- Notebook features are missing. Check that the Markdown plugin is enabled in Settings | Plugins, on the Installed tab.
- Slow performance on Linux. The installation guide warns about performance issues with the Snap package; use the Toolbox App or the tar.gz installer instead.
- Installing on BSD. BSD-based operating systems are not supported.
Free alternatives
| Tool | How its documentation describes it | Install routes in its documentation |
|---|---|---|
| Spyder | “A powerful scientific environment written in Python, for Python”; open source under the MIT License | Standalone installers for Windows, macOS and Linux (recommended for most users); conda (conda create -c conda-forge -n spyder-env spyder); pip (pip install spyder, which its docs caution can occasionally cause tricky issues) |
| JupyterLab | “A next-generation web-based user interface for Project Jupyter” | conda, mamba, uv, pip, pipenv, Docker and others, per its overview page |
PyCharm’s own core features, including Jupyter Notebook support, are free under the unified edition described above, so the choice is mostly about workflow: a full project IDE (PyCharm), a data-analysis-oriented environment (Spyder), or a browser-based notebook interface (JupyterLab). For a worked analysis you can run in any of these, see Python for exploratory data analysis.
FAQ
What is PyCharm?
PyCharm is a Python integrated development environment from JetBrains, covering Python, web and data science work. It combines an editor, a runner, a debugger, package management and Jupyter Notebook support.
Is PyCharm free?
Per JetBrains’ quick start guide, the core features of the unified PyCharm, including Jupyter Notebook support, are free. A Pro subscription adds advanced features, and new installations include a 30-day Pro trial.
What happened to PyCharm Community and Professional?
Starting with 2025.1, JetBrains merged them into one unified PyCharm.
Does PyCharm support R?
Not built in. JetBrains documents an R plugin that adds R support.
Does PyCharm include Python?
No. You need a Python installation first, from python.org, and then select it as the project interpreter.
Can PyCharm run Jupyter notebooks?
Yes. Running a code cell starts a Jupyter server, and the Markdown plugin must be enabled for notebook support.
Sources
- JetBrains, PyCharm quick start guide, jetbrains.com/help/pycharm/quick-start-guide.html, checked 2026-10-29.
- JetBrains, PyCharm installation guide, jetbrains.com/help/pycharm/installation-guide.html, checked 2026-10-29.
- JetBrains, Jupyter Notebook support in PyCharm, jetbrains.com/help/pycharm/jupyter-notebook-support.html, checked 2026-10-29.
- Spyder documentation, docs.spyder-ide.org and its installation page, checked 2026-10-29.
- JupyterLab documentation, jupyterlab.readthedocs.io, checked 2026-10-29.
