How to Use Jupyter Notebook in PyCharm Effectively

Running a Jupyter notebook inside PyCharm gives you something browser-based Jupyter never could: a real debugger, live variable inspection, and full IDE code completion, all inside a single .ipynb file.

Most data scientists split their time between notebooks for exploration and scripts for production. PyCharm closes that gap.

This guide covers everything you need to know about how to use Jupyter notebook in PyCharm, from initial setup and kernel configuration to cell execution, the variable viewer, notebook debugging, and fixing the most common errors.

By the end, you’ll have a working notebook environment inside PyCharm and a clear picture of when to reach for .ipynb files versus percent format scripts.

What is Jupyter Notebook Support in PyCharm

Jupyter Notebook support in PyCharm means you can open, edit, run, and debug .ipynb files directly inside the IDE, no browser required. The notebook interface lives inside PyCharm’s editor window, with cells executing inline and outputs rendering right below each cell.

This is not a plugin bolted on as an afterthought. JetBrains built the Jupyter integration into PyCharm’s core, which means the full IDE toolchain (code completion, the debugger, the variable inspector) all work inside notebook cells just like they do in regular Python scripts.

GitHub Octoverse 2024 data shows Python usage in Jupyter Notebooks on GitHub surged 92% year-over-year, which lines up with why so many developers are looking to run notebooks inside a proper IDE rather than a browser tab.

How PyCharm Handles the Jupyter Kernel

When you open a .ipynb file, PyCharm automatically starts a Jupyter server in the background using the active Python interpreter. You don’t need to run jupyter notebook from the terminal. The kernel connects to whatever environment you’ve configured for the project.

Key difference from browser Jupyter: the kernel lifecycle is managed through PyCharm’s interface. Restart, interrupt, and shutdown controls sit in the notebook toolbar, not a separate browser tab.

What the Integration Actually Gives You

A few things that matter in practice:

• Inline output rendering for matplotlib, Plotly, and pandas DataFrames
• Interactive tables instead of static DataFrame outputs
• Code completion and type hints inside cells
• Breakpoint debugging directly in notebook cells
• A Variables panel that updates live as cells execute

The JetBrains 2023 developer ecosystem survey found that 42% of data science professionals use Jupyter notebooks as their primary tool. Running those notebooks inside PyCharm adds IDE-grade features that browser-based Jupyter simply can’t match.

PyCharm Editions That Support Jupyter Notebooks

This changed significantly in 2025. Previously, Jupyter notebook support was locked behind PyCharm Professional. Starting with PyCharm 2025.1, JetBrains merged the Community and Professional editions into a single unified product.

Basic Jupyter support is now free. Running cells, debugging, output rendering, and intelligent code assistance in notebooks are all available without a paid subscription.

Free vs. Pro Jupyter Features

Every new install gets a free 30-day Pro trial automatically. Pro costs $109 for the first year. Students and teachers can get it for free.

What Changed in 2025.1 and Later

PyCharm 2025.2 was the last standalone Community Edition release. From 2025.3 onward, all users are on the unified product. If you’re still running PyCharm Community, upgrading to 2025.3 gives you Jupyter support at no extra cost.

Google Colab support also landed as a free core feature in PyCharm 2025.3.2. You can connect to Colab-hosted notebooks directly from the IDE, which is useful if your team runs experiments on Colab but prefers local editor tooling.

How to Set Up a Jupyter Notebook in PyCharm

The setup process is straightforward, but the order matters. Get the interpreter right first, then install Jupyter, then create the notebook.

Installing Jupyter in Your Project Environment

Open your project in PyCharm. Go to Settings > Project > Python Interpreter and confirm which environment is active (venv, conda, or system Python).

Then install the Jupyter package into that environment. You have two options:

• Use PyCharm’s Python Packages tool window: search for jupyter and click Install
• Open the terminal inside PyCharm and run pip install jupyter (or conda install jupyter for conda environments)

From PyCharm 2025.3 onward, the IDE uses IPyKernel instead of the full JupyterLab package. This makes kernel startup noticeably faster, which is a real improvement if you’re restarting kernels frequently during data analysis.

Creating and Opening a Notebook File

To create a new notebook:

1. Right-click the target folder in the Project tool window
2. Select New > Jupyter Notebook
3. Name the file (PyCharm adds the .ipynb extension automatically)

To open an existing notebook: double-click any .ipynb file in the Project tool window. PyCharm starts the Jupyter server automatically and opens the notebook in the editor.

You can also use the Welcome screen’s Notebook button to create a notebook without setting up a full project first.

Connecting PyCharm to an Existing Jupyter Server

If you run a Jupyter server separately (or on a remote machine with a Pro subscription), you can connect to it by URL. In the notebook toolbar, click the server selector and choose Configure Jupyter Server. Enter the server URL and token.

This works well for teams that run shared Jupyter servers, or for anyone who wants to keep a persistent kernel running across PyCharm sessions.

How to Run Cells and Manage Outputs

Cell execution in PyCharm feels close to browser Jupyter, but with a few extra controls worth knowing.

Running Individual Cells

Shift+Enter runs the current cell and moves to the next one. That’s the same as standard Jupyter. A few others that are actually useful:

• Ctrl+Enter (or Cmd+Enter on Mac): run cell, stay on the same cell
• Alt+Enter: run cell and insert a new cell below
• Click the green play button in the cell gutter for single-cell runs

The notebook toolbar also has options to run all cells, run all above, or run all below. These show up under the Run menu and in the toolbar buttons at the top of the editor.

Managing Outputs and the Kernel

Outputs can be cleared per cell (right-click the output area) or for the entire notebook via Edit > Clear All Outputs.

Kernel controls sit in the toolbar. Restart clears all variable state and stops any running execution. Interrupt stops a running cell without clearing state, useful when a computation is taking too long.

DataFrames from pandas and Polars render as interactive tables rather than the truncated static output you get in browser Jupyter. You can sort columns, filter rows, and scroll horizontally without writing any extra code. According to JetBrains, this is one of the most requested features data professionals wanted when moving from standalone Jupyter to an IDE-based workflow.

How to Use the Variable Viewer in PyCharm Notebooks

The Variables panel is one of the genuinely useful differences between running notebooks in PyCharm versus a browser. Took me a while to notice it was there, honestly.

Accessing the Jupyter Variables Panel

The panel appears automatically at the bottom of the screen when you run a notebook cell. If it doesn’t show, go to View > Tool Windows > Jupyter Variables.

It updates live after each cell execution. Every variable in the current kernel scope shows up: its name, type, and value. DataFrames always appear at the top of the list (as of PyCharm 2025.3), which saves time when you’re working with multiple variables during exploratory data analysis.

Viewing DataFrames Inline

Click any DataFrame in the Variables panel to open it in the Data View tool window. From there:

• Sort by any column header
• Filter rows using the filter bar
• View column statistics (compact or detailed)
• Switch to chart view directly from the toolbar

This removes the need to write df.describe() or df.head() constantly just to check your data state. JetBrains data shows 69% of data scientists use Jupyter notebooks for exploratory data analysis. The variable viewer is built exactly for that use case.

Using the Variable Panel Instead of Print Debugging

A lot of people coming from browser Jupyter have a habit of scattering print() calls through their notebooks to inspect intermediate values.

The Variables panel makes this unnecessary. Run a cell, check the panel. The value is there, updated in real time. For DataFrames specifically, the inline viewer gives you far more information than any print statement could.

How to Configure the Python Interpreter for Jupyter

The Jupyter kernel that PyCharm starts uses whichever Python interpreter is set as the project interpreter. If those don’t match, you’ll hit package-not-found errors that are confusing to debug the first time.

Setting the Project Interpreter

Go to Settings > Project: [Your Project Name] > Python Interpreter. The current interpreter shows at the top. Click the gear icon to add or switch interpreters.

PyCharm supports:

• Virtualenv (venv)
• Conda environments
• Pipenv and Poetry
• System Python

From PyCharm 2025.3, uv is the default environment manager for new projects. If you’re starting fresh, uv handles environment creation and package installation faster than pip in most cases.

Switching Kernels Mid-Notebook

The kernel selector sits in the notebook toolbar, top right. Click it to see all available kernels based on configured interpreters. Switching kernels restarts the execution environment, so any variables from the previous kernel are lost.

This matters when you have multiple projects with different environments. If you open a notebook from Project A while working in Project B, double-check the kernel selector before running any cells.

Troubleshooting Interpreter Mismatch Errors

The most common error here is No module named 'jupyter'. This almost always means the package is installed in a different environment than the one PyCharm is using as the project interpreter.

Fix it by either installing jupyter into the active environment, or switching the project interpreter to the environment where jupyter is already installed. The Python Packages tool window (bottom toolbar) shows exactly which packages are available in the current interpreter, which makes this diagnosis fast.

If you’re using a PyCharm Community vs Professional setup and wondering why certain Jupyter features are missing, check the edition comparison table in the section above. Remote server connections and dynamic tables require a Pro subscription.

How to Convert Between .ipynb and .py Files in PyCharm

Notebooks are for exploration. Scripts are for production. Most data workflows eventually need both, and PyCharm makes switching between them straightforward.

JetBrains data shows notebooks are primarily used for prototyping and experimentation, while .py scripts handle structured, deployable code. Knowing how to move between the two formats saves a lot of manual copy-paste work.

Converting a Notebook to a Python Script

Right-click the .ipynb file in the Project tool window and select Convert to Python File. PyCharm handles the conversion automatically.

What gets preserved and what doesn’t:

• All code cells convert to plain Python code
• Markdown cells convert to comments
• Cell outputs are stripped (they don’t exist in .py files)
• Cell metadata and tags are lost

The resulting script uses the # %% percent format to mark cell boundaries. This format is supported by VS Code, Spyder, and PyCharm, so the file stays portable.

Using Percent Format Scripts as an Alternative

The percent format is worth understanding on its own. A .py file with # %% markers runs like a notebook in PyCharm’s scientific mode, with each section executing as a separate cell.

Why some teams prefer this over .ipynb:

• Plain .py files diff cleanly in Git (no JSON noise)
• Easier to do code review in pull requests
• Supports all standard PyCharm refactoring tools

If your team uses version control heavily, running notebooks as percent format scripts is cleaner than committing .ipynb files with embedded outputs.

When to Use Each Format

You can also go the other direction: right-click a .py file and select Convert to Jupyter Notebook to get a .ipynb file with cells split at each # %% marker.

Looking at how PyCharm compares against browser-based Jupyter and other editors? The PyCharm vs Jupyter breakdown covers the key differences in detail.

Debugging Jupyter Notebooks in PyCharm

Browser-based Jupyter has no real debugger. You get tracebacks, print statements, and %pdb magic commands. That’s it.

PyCharm’s integrated Jupyter debugger changes this completely. It’s one of the clearest practical reasons to run notebooks inside an IDE rather than a browser.

Setting Breakpoints Inside Notebook Cells

Click in the gutter to the left of any line inside a code cell. A red dot appears, marking the breakpoint.

From PyCharm 2025.1 onward, the IDE automatically places a breakpoint on the first line of a cell if none are set manually. This makes jumping into debug mode faster for quick inspections.

To start debugging: right-click inside the cell and select Debug Cell, or click the debug icon in the cell toolbar. The Jupyter Notebook Debug tool window opens at the bottom of the screen.

Stepping Through Code in the Debugger

JetBrains describes the debugger as allowing you to set breakpoints, inspect variables, and evaluate expressions directly within notebooks.

Once paused at a breakpoint:

• Use the stepping toolbar to move line by line
• Inspect live variable values in the Variables tab
• Step into functions called from the current cell
• Resume execution to the next breakpoint

One limitation: debugging runs within a single code cell at a time. If your cell calls a function defined in another cell that was previously executed, PyCharm can step into it.

Using Traceback Links and Inline Values

Inline value display is a separate feature worth enabling. Go to Settings > Jupyter > Jupyter General and check “Show inline values.”

After running a cell, variable values appear directly next to their usages in the editor. No need to open the Variables panel or hover over anything.

For errors, PyCharm’s traceback output includes clickable blue links that jump directly to the line that failed. Click the link, fix the code, re-run. Much faster than hunting through a long notebook manually.

Anyone who’s spent time learning to debug in PyCharm will find the notebook debugger familiar. Same interface, same controls, just applied to cells instead of script files.

Common Errors When Using Jupyter in PyCharm and How to Fix Them

Most Jupyter errors in PyCharm come down to one thing: environment mismatch. The package is installed somewhere, the kernel is looking somewhere else.

The Jupyter documentation itself flags this as the most common source of reported issues. Multiple Python environments are often the root cause, with the notebook server running in one environment while the kernel pulls from another.

“No module named jupyter” Error

The most common error, and the easiest to fix.

Cause: Jupyter is not installed in the environment PyCharm is using as the project interpreter.

Fix: Open the Python Packages tool window (bottom toolbar), search for jupyter, and install it. Or open the integrated terminal and run pip install jupyter.

After installing, restart PyCharm and try again. The kernel should start without the error.

Kernel Not Starting After Interpreter Configuration

This one is trickier. The kernel selector shows the right environment, but nothing happens when you try to run a cell.

Root cause: a conflict between kernel spec files from different environments. Running jupyter kernelspec list in the terminal shows all registered kernels and their paths. If a stale spec is pointing to the wrong Python binary, the kernel will fail silently.

The fix in most cases: delete the conflicting kernel folder from ~/.local/share/jupyter/kernels/ and let PyCharm register a fresh one. From PyCharm 2025.3, the IDE uses IPyKernel directly rather than the full JupyterLab package, which reduced these conflicts significantly.

Port Conflicts When Connecting to an External Server

Default Jupyter server port: 8888.

If another process is already using that port (another Jupyter instance, a local web server), PyCharm will either fail to connect or connect to the wrong server.

Check for port conflicts with lsof -i :8888 on macOS/Linux or by checking Task Manager on Windows. Start your Jupyter server on a different port with jupyter notebook --port 8889, then update the server URL in PyCharm’s Configure Jupyter Server settings.

Notebook Not Rendering Outputs After Kernel Restart

After restarting the kernel, all variable state is cleared. Outputs from previous runs remain visible in the notebook file, but they’re stale.

Nothing is broken here. Re-run the cells from the top. If outputs still don’t appear after running, check that the Markdown plugin is enabled under Settings > Plugins. PyCharm’s documentation notes that the Markdown plugin is required for all Jupyter notebook functionality to work correctly.

If you’re deciding between PyCharm’s Professional features and the free core tier for your data workflows, the PyCharm keyboard shortcuts guide is a good companion reference for speeding up day-to-day notebook work once the setup issues are out of the way.

FAQ on How To Use Jupyter Notebook In PyCharm

Does PyCharm support Jupyter notebooks for free?

Yes. Since PyCharm 2025.1, basic Jupyter notebook support is free for all users. Running cells, debugging, and output rendering are included in the core tier. Advanced features like dynamic tables and remote server connections require a Pro subscription.

How do I create a new Jupyter notebook in PyCharm?

Right-click any folder in the Project tool window, select New > Jupyter Notebook, and name the file. PyCharm adds the .ipynb extension automatically and starts the Jupyter server when you run the first cell.

Why is my Jupyter kernel not starting in PyCharm?

Almost always an environment mismatch. The kernel is looking for packages in the wrong Python interpreter. Check Settings > Python Interpreter and confirm jupyter is installed in the active environment. Running pip install jupyter in PyCharm’s terminal usually fixes it.

Can I use conda environments with Jupyter in PyCharm?

Yes. Set your conda environment as the project interpreter under Settings > Python Interpreter. PyCharm detects jupyter automatically if it’s installed in that environment. The kernel selector in the notebook toolbar lets you switch environments mid-session.

How do I debug a Jupyter notebook cell in PyCharm?

Click the gutter to the left of any line in a code cell to set a breakpoint. Right-click inside the cell and select Debug Cell. The Jupyter Notebook Debugger opens at the bottom, where you can step through code and inspect live variable values.

How do I convert a .ipynb file to a Python script in PyCharm?

Right-click the .ipynb file in the Project tool window and select Convert to Python File. Code cells become plain Python. Markdown cells become comments. Cell outputs are stripped. The result uses # %% percent format to mark cell boundaries.

What is the Variables panel in PyCharm notebooks?

It’s a live inspector that updates after every cell execution, showing each variable’s name, type, and value. Click any DataFrame in the panel to open it in the Data View window, where you can sort, filter, and explore data without writing extra code.

How do I connect PyCharm to a remote Jupyter server?

Click the kernel selector in the notebook toolbar and choose Configure Jupyter Server. Enter the remote server URL and token. Remote Jupyter server support, including variables view and interactive outputs on the remote machine, requires a PyCharm Pro subscription.

What is the difference between PyCharm’s Jupyter support and JupyterLab?

JupyterLab runs in a browser with no native debugger or IDE tooling. PyCharm’s Jupyter integration adds breakpoint debugging, full code completion, Git versioning, and a live variable inspector, all inside the same environment you use for your regular Python scripts.

How do I fix the “No module named jupyter” error in PyCharm?

Jupyter isn’t installed in your active interpreter. Open the Python Packages tool window, search for jupyter, and install it. Alternatively, run pip install jupyter in PyCharm’s integrated terminal. Restart the IDE afterward to let the kernel register correctly.

Conclusion

This conclusion is for an article presenting how to use Jupyter notebook in PyCharm, a setup that replaces scattered browser tabs with a proper data science workflow inside a single IDE.

The Python interpreter configuration is the foundation. Get that right, and kernel management, package installation, and cell execution all follow without friction.

From there, the variable viewer, interactive DataFrame tables, and breakpoint debugging inside notebook cells are what actually separate PyCharm from vanilla JupyterLab.

The .ipynb to .py` conversion workflow and percent format scripts give you a clean path from exploratory data analysis into production-ready code.

Whether you’re working with conda environments, connecting to a remote Jupyter server, or fixing a kernel startup error, PyCharm handles the full notebook lifecycle in one place.

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Bogdan Sandu

Bogdan Sandu specializes in web design, focusing on creating user-friendly websites, and innovative UI kits.

Many of his resources are available on various design marketplaces and for free on Codepen.

Over the years, he's worked with a range of clients and contributed to design publications like Design Your Way, Designmodo, WebDesignerDepot, WPDean, Speckyboy, and Slider Revolution among others.

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