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To use PyCharm for data science, create a project, select its Python interpreter, and install your libraries into that same environment. Then choose the workflow that fits the task: a Jupyter notebook for cell-by-cell exploration, a Python script for reusable code, or the Python Console for short interactive commands. PyCharm’s scientific tools can display supported pandas dataframes, NumPy arrays, and plots produced by installed libraries.
1. Create a project and choose its Python interpreter
Start with a PyCharm project and configure a Python interpreter for it. The interpreter is the Python environment that runs your code and determines which installed packages the project can use. PyCharm requires at least one configured interpreter.
In the project’s interpreter settings, choose an existing system interpreter or create a local environment. PyCharm’s documented local options include Virtualenv, pipenv, Poetry, uv, hatch, and conda. A project-specific environment keeps its package set separate from other projects, which helps prevent dependency changes for one project from affecting another.
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For remote execution, PyCharm Pro supports interpreters via SSH, Docker, Docker Compose, and WSL on Windows. The available options depend on your edition and setup; choose an environment that matches where your project needs to run.
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2. Install data-science packages into that interpreter
Use PyCharm’s Python Packages tool window or interpreter settings to install and manage packages. PyCharm uses pip by default and supports conda for conda environments. Before installing, check that the selected interpreter is the one assigned to the project: a package installed into a different Python environment will not be available to this project.
JetBrains lists NumPy and pandas for array and dataframe workflows, Matplotlib and Plotly for plotting, among other scientific-library uses. Install only the packages your code needs, using the package manager appropriate to the selected environment.
3. Choose a workflow: notebook, script, or console
| Workflow | Best suited to | How it works in PyCharm |
|---|---|---|
| Jupyter notebook | Exploration and analysis in separate, ordered cells | Open or create an .ipynb file, add cells, and run them. PyCharm starts a Jupyter server when you execute a cell. |
| Python script | Reusable analysis code organized in source files | Write and run ordinary Python files using the project interpreter. |
| Python Console | Short commands and quick experiments alongside project files | Open Tools | Python Console. It uses the project interpreter by default and provides IDE code assistance. |
Use a Jupyter notebook for cell-based exploration
Create or open a Jupyter notebook with the .ipynb extension, add code cells, and execute a cell to start the Jupyter server. PyCharm supports notebook editing and execution, and its notebook integration can inspect outputs such as stream data, images, and other media. It also documents notebook debugging.
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When analysis should live in source files that can be reused or maintained as a program, put it in a Python script. The project interpreter selected during setup applies to Python code generally, so keep the script’s dependencies installed in that environment.
Use the Python Console for quick experiments
For a brief calculation or a command you want to try without creating a notebook cell or editing a script, open Tools | Python Console. The console runs against the project interpreter by default, so it uses that environment’s installed packages.
4. Inspect data and plots
View arrays and dataframes
PyCharm’s scientific features include data views for supported NumPy arrays and pandas dataframes. Use the available data-view link or tool to inspect these objects in tabular form. The data-view workflow depends on the relevant libraries being installed in the project interpreter.
Work with plots
PyCharm’s Plots tool window can display visualizations produced by Python libraries. The documented controls include resizing and zooming plots and saving them. Install the plotting library your code uses in the selected environment; PyCharm displays and integrates with library output rather than replacing the library that generates the visualization.
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PyCharm documents a dedicated Jupyter Notebook Debugger for notebook work. Its scientific-features documentation also describes plots appearing while debugging at a breakpoint. These are supported capabilities, not a guarantee that every third-party library or project configuration will behave identically.
When a notebook cell or script cannot import a package, first check which interpreter the project is using and whether the package was installed there. For a missing or unexpected data view, verify that the object is a supported NumPy array or pandas dataframe and that the corresponding library is available in the selected environment.
What PyCharm edition do you need?
JetBrains’ PyCharm documentation identifies its pages as version 2026.2 help. It says scientific features have been enabled by default since PyCharm 2024.1 and states, “Scientific mode no longer exists as a separate setting.” Do not look for the older separate Scientific mode switch.
JetBrains says that starting with version 2025.1, Community and Professional were combined into a unified PyCharm product: core functionality, including Jupyter support, is free, while Pro adds features. Some options, including the documented remote interpreter types, are listed as Pro features. Edition boundaries can change, so check JetBrains’ current product documentation if a particular capability is essential.
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