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Yes. PyCharm supports data-science work in Python, including Jupyter notebooks, data inspection, and plots. You still need to set up a Python interpreter and install the libraries your project uses. Since PyCharm became a unified product in 2025.1, its core features—including Jupyter Notebook support—are free, while Pro adds advanced features.
What you can do for data science in PyCharm
PyCharm combines Python development tools with features for scientific computing. That makes it useful for both notebook-based exploration and more structured projects. JetBrains documents its scientific features and supported workflows in its scientific features guide and data science and machine learning tools overview.
Work with Jupyter notebooks
You can edit, run, and debug Jupyter notebooks in PyCharm. Notebook output can include streams, images, and other media. The IDE also provides a notebook debugger. See JetBrains’ Jupyter notebook support documentation for setup details.
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PyCharm’s Data View can display NumPy arrays and pandas dataframes in tables, with options such as column statistics and charts. These capabilities depend on the relevant packages being installed in the interpreter selected for your project.
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View plots
PyCharm includes a Plots tool window for visualizations and documents workflows using Matplotlib and Plotly. The scientific project tutorial walks through running code and viewing generated graphs.
Set up the project’s Python environment
PyCharm is the development environment; it does not replace Python or the data-science libraries. Install Python, select or configure an interpreter for the project, and install the packages that your code needs into that environment. Depending on your workflow, these may include NumPy, pandas, Matplotlib, or Plotly. JetBrains’ Python support guide covers interpreter and project setup, while its tutorial illustrates a conda environment with NumPy and Matplotlib.
Which PyCharm features are free?
JetBrains combined the former Community and Professional editions into unified PyCharm starting with version 2025.1. Core features, including Jupyter Notebook support, are free; a Pro subscription adds advanced features. The current unified-product overview also describes a 30-day Pro trial. Because feature availability can change, check the unified PyCharm overview and quick start guide if a particular capability determines your choice.
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Check third-party integrations before relying on them
Do not assume older tutorials describe today’s bundled integrations. In its release notes for PyCharm 2026.2.1, JetBrains says Data Wrangler, Hugging Face, and Google Colab support were unbundled and are no longer bundled or actively maintained by the PyCharm team. Compatible versions may still be available from JetBrains Marketplace, but check current availability and maintenance before building a workflow around one. See the PyCharm 2026.2.1 release notes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is PyCharm a good fit for your workflow?
PyCharm is a reasonable choice if you want to develop and maintain Python data projects in an IDE that also supports code navigation, debugging, project management, notebooks, and data inspection. Before committing to it, check that your workflow’s needs line up with the features available in free core PyCharm, Pro, or an external integration.
- Choose it if you want notebooks alongside regular Python code in one project.
- Confirm the required libraries are installed in the interpreter your project uses.
- Verify the current availability and maintenance status of any external integration you depend on.
These documented capabilities make PyCharm a viable option, but they do not establish that it is universally better than other environments. No comparative performance testing is available here.
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