Best Python IDE for Data Science in 2026

The best Python IDE for data science in 2026: Positron, VS Code with notebooks, JupyterLab, PyCharm Pro and Spyder compared, with when to pick each.

The best Python IDE for data science depends on how you work: Positron if you want an IDE built for data science, VS Code if you already live in it, JupyterLab for notebook-first work, and PyCharm Pro for database-heavy or remote projects. All of them can run notebooks. What differs is how much the environment is organised around data.

At a glance

Positron

Positron is a free IDE from Posit, the company behind RStudio and Shiny, and its page calls it purpose-built for data science. It supports Python and R, with a persistent console, notebook-style exploration, a Variables and Plots pane beside the editor, and a Data Explorer for large local or remote datasets. It can also help you develop and preview local apps, APIs, notebooks or reports, and it includes an AI assistant (Posit Assistant) that works with your live session.

Its licence is the Elastic License 2.0, which is source-available rather than open source. It is the closest thing to an RStudio-style experience for Python, which makes it the natural choice if you split your time between the two languages.

VS Code with the Python and Jupyter extensions

If you already use VS Code for other work, you don’t need a separate tool. The Python and Jupyter extensions together give native Jupyter notebook support: create a notebook from the Command Palette, pick a kernel from your environment, and run cells. After running code, the Variables view lists the variables in your running kernel, and you can open a DataFrame from it in the Data Viewer to view, sort and filter its rows. A separate Data Wrangler extension adds a richer interface for data exploration.

The advantage is that exploration and production code live in the same editor, with testing, linting and Git alongside. The trade-off is that the data-science features arrive as extensions rather than as a layout designed around them.

JupyterLab

JupyterLab is the fuller Jupyter interface, with tabbed multi-notebook editing and a customisable layout, and it is where notebooks are most at home. Install and launch it with:

pip install jupyterlab
jupyter lab

Choose it when notebooks are the product: teaching, sharing analyses, or running notebooks on a server. Notebooks run on kernels, so the same interface covers other languages too. For the notebook format itself and a reactive alternative, see the notebook notes at the end of this guide.

PyCharm Pro

JetBrains’ PyCharm has free core features, including basic Jupyter notebook support: running and debugging notebooks, code insight, quick-fixes and completion. PyCharm Pro adds the parts that matter for heavier data work: full-scale local and remote Jupyter notebooks with a dedicated debugger, dataset tools, interactive tables, dashboards and Conda support, plus built-in database tools for more than 40 databases and remote development over SSH, WSL or Docker.

It is the strongest choice if your analysis lives close to a database, runs on a remote machine, or grows into a large codebase. It is heavier than the alternatives, and you pay for the Pro features; students and educators can get Pro free, and each major release has a 30-day trial.

Spyder

Spyder describes itself as a powerful scientific environment written in Python, for Python. It combines editing, analysis, debugging and profiling with data exploration, interactive execution and visualisation, and it is free and open source under the MIT licence. It suits people who think in scripts with an interactive console, in the style of other scientific environments, rather than in notebooks.

Notebooks and IDEs work together

Most data scientists don’t pick one. A notebook is the best place to explore: run a cell, inspect the output, adjust, repeat. Once an approach works, move it into modules with tests in an editor. Positron, VS Code and PyCharm all run notebooks inside the IDE, so that move doesn’t need a second tool.

There are also newer notebook formats. marimo is a reactive Python notebook stored as plain .py files, so changes diff cleanly in git, and it is worth knowing about if reproducibility and version control matter to you. See Jupyter vs marimo for the comparison.

Which should you pick?

• You use Python and R: Positron.
• You already use VS Code: stay there and add the Jupyter extension.
• You teach, share or present analyses: JupyterLab.
• You work with databases, remote machines or a large codebase: PyCharm Pro.
• You like a script plus a console, and a scientific layout: Spyder.
• You are new to all of it: JupyterLab or VS Code. Both are a short install away and free.

The IDE is only part of the setup. For the data libraries themselves, see Polars vs Pandas, NumPy vs Pandas and Matplotlib vs Seaborn vs Plotly. For the wider editor comparison, see the best Python IDEs and editors.