Set up PyCharm for data science on Windows
- 1
Install Miniconda
Download Miniconda for Windows. This gives you Python + Conda in a minimal footprint.
- 2
Create a data science environment
(base)> conda create -n ds python=3.11(base)> conda activate ds(ds)> conda install pandas numpy matplotlib scikit-learn jupyter - 3
Configure in PyCharm
File → Settings → Python Interpreter → Add → Conda → select the
dsenvironment. See Conda guide. - 4
Enable Scientific Mode
View → Scientific Mode. This enables inline plot display, variable explorer and interactive console.
Jupyter notebooks in PyCharm
PyCharm Professional has full Jupyter notebook support (.ipynb files). PyCharm Community has limited Jupyter support via the scientific mode.
With Professional: open any .ipynb file and it renders as an interactive notebook. Run cells with Shift+Enter, add cells, see output inline.
jupyter notebook and work in the browser, then use PyCharm for .py files.Data science questions
PyCharm vs Jupyter for data science — which is better?
They are complementary. Jupyter notebooks are better for exploration, visualisation and sharing results. PyCharm is better for writing production code, debugging and refactoring. Many data scientists use both: Jupyter for exploration, PyCharm for building clean Python modules and packages from the notebook code.
pycharm conda — why not just use Jupyter lab?
PyCharm gives you a full IDE experience: code completion, refactoring, version control integration, testing and debugging. JupyterLab is better for notebook-style work. For a project that has both notebooks and .py modules (which most real data science projects do), PyCharm Professional handles both in one IDE.