Use Jupyter notebooks as both executable analysis and readable documentation: learn the two modes, run cells predictably, organize work with Markdown, and verify the notebook from a clean kernel before sharing it. These practices make data-science notebooks easier to navigate, reproduce, and publish.
1. Learn the two notebook modes
JupyterLab separates editing a cell from acting on the notebook. In Edit Mode, you change the cell’s contents; in Command Mode, you perform notebook-level actions such as running cells, inserting cells, or changing a cell’s type.
- Press Enter to enter Edit Mode.
- Press Esc to return to Command Mode.
- In JupyterLab, a blue cell border indicates Edit Mode and a gray border indicates Command Mode.
Knowing which mode is active helps explain why a keyboard action edits text in one moment but operates on the notebook in another. The JupyterLab documentation describes the modes and their controls at JupyterLab keyboard commands.
2. Make Shift-Enter your everyday run command
In Notebook 7, Shift-Enter runs the current cell, displays its output, and moves to the next cell. When used on the final cell, it creates a new cell below. This makes it a practical default for reading and executing a notebook in sequence. See the Notebook 7 documentation for the documented behavior.
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Run cells in the order that the analysis is meant to be read. If you jump around while exploring, remember that the kernel retains state; a later clean run is needed to confirm the notebook does not depend on variables created out of order.
3. Search the notebook, not just the visible page
Notebook 7 provides notebook-aware search. Use Ctrl-F on Windows or Linux, or Cmd-F on macOS, to search notebook content. The notebook search plugin can inspect the document in ways a browser’s ordinary find function may not, since browser find does not access the full notebook by default.
This is useful for locating a variable, section, or result in a long analysis. The shortcut and plugin are documented in the Notebook 7 documentation.
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4. Write a narrative, not a pile of experiments
A notebook can combine executable code with plain-language descriptions, data, equations, visualizations, and interactive controls. That makes it more than a script with outputs: it can explain why the analysis was done and how to interpret it. Project Jupyter describes this format in its notebook overview.
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- State the question the analysis addresses.
- Identify the data source and any important scope or limitations.
- Explain meaningful transformations before or after the code that performs them.
- Interpret the result rather than expecting readers to infer its meaning from output alone.
A reader should be able to follow the reasoning without having to reconstruct the purpose of every cell.
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5. Keep cells focused and execution order clear
Organize cells into purposeful sections, such as setup, imports, data loading, cleaning, analysis, and presentation. A cell should do a coherent piece of work that is easy to understand and rerun—not hide several unrelated steps in a long block.
Avoid hidden dependencies on variables created many cells earlier. The clearest check is to restart the kernel and run the notebook from top to bottom before sharing it. If that fails, fix the ordering or document a genuine external prerequisite instead of relying on an interactive session’s leftover state.
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Place interpretation close to the chart, table, or other output it describes. Label axes, units, and relevant categories so a reader can understand what is being shown. Remove exploratory output that does not support the notebook’s stated question; unnecessary results make it harder to tell which findings matter.
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Keep the code, explanation, and output together where possible. This lets a reviewer connect a conclusion to the computation that produced it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Use tags and extensions only when they help
JupyterLab supports cell tags, which tools such as nbconvert and papermill can use to control export or parameterized execution. Tags are helpful when they replace repetitive manual steps, but they are not a substitute for explaining how the notebook works.
JupyterLab’s extension system can add notebook-specific capabilities, including renderers; ipywidgets is one example of an interactive tool. Because extensions and widgets may not be available in a reader’s environment, document any non-default dependency that is required to run or display the notebook. See the JupyterLab documentation on notebooks and cell tags and extensions.
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8. Choose the kernel that matches the project
A kernel is the mechanism Jupyter uses to provide language support. Jupyter supports multiple kernels, so select the one associated with the project’s intended language and environment. Keep that choice consistent across the notebook rather than silently switching kernels between cells or sessions.
Record important package versions and environment requirements so another person has a practical path to rerunning the analysis. The Project Jupyter documentation explains kernels at Jupyter kernels.
9. Export a static copy with nbconvert
For people who need to read the analysis but do not need a live kernel, nbconvert can turn a notebook into a static artifact. The documented output formats include HTML, Markdown, LaTeX/PDF, and reStructuredText. Choose the format your audience can open, and retain the .ipynb file as the editable source for future work.
Conversion options and supported formats are covered in the nbconvert usage documentation.
10. Make sharing a reproducibility checkpoint
Before sending a notebook, review it as a new reader would: start from a clean kernel, run all cells, and check that the outputs reflect the current code and data. Confirm that required files, packages, and any non-default extensions are identified, then export an appropriate static version if the audience does not need to execute the notebook.
A notebook is designed to communicate computational work as well as perform it. A successful final check therefore means the analysis runs in its intended order and the document makes its question, reasoning, and results understandable to someone else.
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