To use PyCharm for data science, first select a Python interpreter for your project, install your libraries into that interpreter, and then choose a workflow: a Jupyter notebook for cell-by-cell exploration, a Python script for reusable code, or the Python console for quick interactive commands. PyCharm’s scientific features are enabled by default, and Jupyter support is included in its free core functionality.
1. Create a project and choose its Python interpreter
PyCharm runs project code through a configured Python interpreter. Set one up for the project before installing libraries or running analysis; otherwise, you may install a package into one Python environment while the project uses another.
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- Create or open a project in PyCharm.
- Configure a project interpreter in the project’s Python settings. JetBrains lists local options including system Python and environments managed with Virtualenv, pipenv, Poetry, uv, hatch, or conda.
- Choose an environment whose package set suits the project. A separate environment helps keep the project’s dependencies distinct from those used elsewhere.
JetBrains lists remote interpreter options—including SSH, Docker, Docker Compose, and WSL on Windows—as PyCharm Pro features. Local interpreter choices are available for configuring a local project. JetBrains’ interpreter guide describes the available options.
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Use PyCharm’s Python Packages tool window or the interpreter settings to add packages. Confirm that the selected project interpreter is the target: a library installed into a different environment will not be available to the project’s Python code. PyCharm uses pip by default and supports conda for conda environments. See JetBrains’ package-management guide.
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JetBrains’ scientific-features documentation names Matplotlib, NumPy, Plotly, and pandas as libraries to install for their corresponding workflows. The IDE’s data and plot tools depend on the relevant libraries being available to the project interpreter. Scientific features in PyCharm explains the integrations.
3. Choose the right way to work
| Workflow | Best suited to | How to use it |
|---|---|---|
| Jupyter notebook | Exploration and analysis in cells, with outputs beside the code. | Create or open an .ipynb file, add code cells, and run a cell. PyCharm starts the Jupyter server when you execute a cell. |
| Python script | Reusable analysis organized as ordinary source files. | Write and run Python code in project files using the configured project interpreter. |
| Python console | Short interactive commands and quick exploration alongside project files. | Open Tools | Python Console. It uses the project interpreter by default. |
Use a notebook for cell-by-cell analysis
In a notebook, add code cells and execute them as you explore. PyCharm supports notebook editing and execution, as well as output inspection for stream data, images, and other media. Its documented notebook features also include debugging. Follow the Jupyter notebook quick start to create or open a notebook and begin running cells.
Use scripts for repeatable work
Put analysis that should be run again, shared, or incorporated into a larger project in Python files. Scripts use the project’s configured interpreter just as notebook code does, so keep the environment and dependencies associated with the project.
Use the console for quick experiments
The Python console is useful for trying a short expression or inspecting a value without creating a notebook cell or editing a source file. Open it from Tools | Python Console; JetBrains documents that it runs against the project interpreter by default and provides IDE code assistance. See the Python console documentation.
4. Inspect data and plots
When the relevant libraries are installed, PyCharm can display supported NumPy arrays and pandas dataframes in data views, making their contents easier to inspect in tabular form. Visualizations produced by Python libraries can appear in the Plots tool window, where JetBrains documents controls for resizing, zooming, and saving plots.
These are IDE views of data and outputs created by Python libraries; they do not replace those libraries. If a dataframe, array, or plot is unavailable, check that its package is installed in the selected project interpreter and that the code has produced the object or visualization. The scientific-features guide covers the documented data and plot integrations.
5. Debug notebooks and inspect results as you iterate
PyCharm documents a dedicated Jupyter Notebook Debugger. Its scientific features documentation also describes plots appearing during debugging at a breakpoint. These are supported capabilities, not a guarantee that every project setup or third-party library will behave identically. If notebook execution or a visualization fails, first verify that the project is using the intended interpreter and that the required packages are installed there.
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JetBrains’ PyCharm 2026.2 help says Scientific mode is no longer a separate setting; the scientific features have been enabled by default since PyCharm 2024.1. Instructions that tell you to switch on a separate Scientific mode are outdated for those versions.
JetBrains says that, starting with PyCharm 2025.1, Community and Professional were combined into a unified product. Core functionality, including Jupyter support, is free, while Pro adds additional features. Edition boundaries can change; consult JetBrains’ PyCharm quick-start guide for current product information.
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