Keeping Your Jupyter Notebooks Clean

Automated quality checks for Jupyter notebooks.
Python Recipes
Published

February 20, 2026

Modified

February 20, 2026

Keeping Your Jupyter Notebooks Clean cover

Keeping Your Jupyter Notebooks Clean cover

Jupyter notebooks can be a real mess for version control systems. They are often full of massive dataframes, messy plots, or sprawling printouts. Including execution outputs in notebooks committed to a shared git repo makes diffs and code reviews painful.

There might be other conventions your team strives to follow: clear titles, descriptive explanations, clean sectioning. Without automated pre-commit guardrails, maintaining that discipline across a team is nearly impossible.

Since notebooks are just JSON files under the hood, we can easily prepare a zero-dependency Python script to be our watchdog.

Let’s define a helper class to represent the structure of a notebook cell, and then write some functions to check for common quality issues.

Show the code 
"""Check Jupyter notebooks for quality criteria."""

import json
import sys
from pathlib import Path
from typing import TypedDict, cast


class Cell(TypedDict):
    """A cell in a Jupyter notebook."""

    cell_type: str
    outputs: list[dict]
    execution_count: int
    source: list[str]
    metadata: dict[str, str]

This script will perform a couple of simple checks on each notebook:

Show the code 
1def first_cell_is_markdown(cells: list[Cell]) -> bool:
    """Check if the first cell in the notebook is a markdown cell."""
    first = cells[0]
    cell_type: str | None = first.get("cell_type")
    return cell_type == "markdown"


2def outputs_are_empty(cells: list[Cell]) -> bool:
    """Check if all cells in the notebook have empty outputs."""
    for cell in cells:
        outputs = cell.get("outputs")
        if outputs:
            return False
    return True
1
Ensures the first cell of the notebook is a Markdown cell. This is useful for making sure every notebook starts with a title or introduction.
2
Iterates through all cells and checks if any have outputs. This helps prevent committing large dataframes, plots, or potentially sensitive information that might be stored in the notebook’s execution results.

Then we need some logic to run these checks on all notebooks in a directory:

Show the code 
1def check_notebook(path: Path) -> bool:
    """Check if a notebook satisfies the quality criteria."""
    json_string = path.read_text(encoding="utf8")
    data = json.loads(json_string)

    cells = data.get("cells")
    if not cells:
        # We shouldn't have empty notebooks in our repo
        return False

    cells = cast("list[Cell]", cells)
    return all(
        (
            first_cell_is_markdown(cells),
            outputs_are_empty(cells),
            # Potentially more checks could be added here in the future.
        )
    )


2def check_directory(path_str: str) -> int:
    """Check all notebooks in a directory and its subdirectories."""
    all_notebooks = Path(path_str).glob("**/*.ipynb")
    failed = [path for path in all_notebooks if not check_notebook(path)]

    if not failed:
        return 0

    failed_str = "\n\t".join(str(path) for path in failed)
    print(f"Failed check on notebooks:\n\t{failed_str}")
    return 1


if __name__ == "__main__":
    sys.exit(check_directory("."))
1
Inspects cell arrays, verifying that title cells are present and outputs are stripped.
2
Recursively scans **/*.ipynb across directories using pathlib.Path.glob and reports failures.

Automating these little chores saves a lot of headache in the long run. It’s a simple base that you can easily tweak or add more rules to as you go.

Note

Download the complete code from the jupyter_notebook_quality_assurance_checks.py script.

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