Using Markdown Cells in Jupyter Notebook

In a Jupyter Notebook, a cell is the basic unit of organization and execution. It’s essentially a visual container that holds either code or text. Cells that are used for holding and running code are understandably called code cells while cells that are intended for text are known as markdown cells. Each cell operates as an individual block where users can write and execute snippets of code sequentially, or compose formatted text to provide explanations, mathematical equations, or instructions.

In this week’s post, we’ll learn about the simpler of the two cell types: the markdown cell. As the name suggests, these cells contain text formatted using Markdown, a lightweight markup language intended for documentation. While that may not sound nearly as sexy as running code for data visualizations or machine learning models, the importance of documentation should never be underestimated. Therefore, it’s essential to everyone’s Python journey that they gain a working knowledge of markdown cell fundamentals.

Why Documentation is Essential to Python

In the realm of data science, documentation and formatting play crucial roles that extend beyond mere presentation. Good documentation and consistent formatting are foundational to ensuring that data science work is reproducible, understandable, and accessible both to the data scientist and to others. Here are some reasons why these practices are so vital:

  • Reproducibility. Data science is an empirical field, which means that experiments must be repeatable to validate results. Proper documentation of the code, data sources, transformations, and analytical methodologies within Jupyter notebooks ensures that experiments can be replicated by others or by the same researcher in the future.
  • Collaboration. Data science is often a collaborative effort. Well-documented code with clear formatting allows team members to understand each other’s work quickly, facilitating seamless collaboration. In a Jupyter Notebook, markdown cells are used to explain the purpose and the expected outcome of the code cells, which aids in collaborative understanding.
  • Communication. The end goal of a data science project is often to communicate findings to stakeholders who may not have a technical background. Markdown cells within Jupyter notebooks can be used to format explanations, contextual narratives, and conclusions that make the technical aspects of the work accessible to non-technical audiences.
  • Efficiency and Productivity. Consistent documentation and formatting enable the data scientist to revisit their own work after some time and quickly pick up where they left off. It acts as a roadmap for one’s thought process, making it easier to troubleshoot, refine, and extend analysis.
  • Educational Value. Well-documented notebooks serve as educational tools for others learning data science. They can follow the logical progression of the analysis, understand decision-making processes, and learn best practices in coding and data analysis.
  • Quality Control. Documentation acts as a form of quality control, ensuring that each step of the analysis is justified and well thought out. It forces the practitioner to think critically about their work, resulting in more rigorous analysis.

The importance of documentation and formatting in data science cannot be overstated. They are not just about making the work look good; they are about ensuring the integrity, clarity, and usefulness of the data science work itself. Jupyter Notebooks, with their integration of code and markdown cells, provide an excellent platform for data scientists to practice and perfect these essential skills.

What is Markdown?

Markdown is a lightweight markup language with plain-text formatting syntax. Its primary objective is to be as readable as possible. The language allows people to write using an easy-to-read, easy-to-write plain text format, which is then converted into structurally valid HTML or XHTML. It is often used to format readme files, for writing messages in online discussion forums, and to create rich text using a plain text editor.

Markdown History and Design Philosophy

Markdown was created in 2004 by John Gruber with substantial contributions from Aaron Swartz. The goal behind its design was to enable people to write using an easy-to-read and easy-to-write plain text format and optionally convert it to structurally valid XHTML (or HTML). The key design philosophy behind Markdown is readability – that the language should be as readable as possible. The idea is that a Markdown-formatted document should be publishable as-is, as plain text, without looking like it’s been marked up with tags or formatting instructions.

This design philosophy is aligned with the primary goal of markup languages – to identify and describe the text within a document – with a secondary goal of being easy for humans to read and write. Markdown’s syntax is intended not to interfere with the content or its meaning, making it an ideal format for someone who wants to write quickly and efficiently without being bogged down by the formatting details that are often required in more complex markup languages.

Why Use Markdown in Documentation for Notebooks?

There are several reasons why markdown is used by Python coders for documentation. Here are just a few:

  • Ease of Use. Markdown is designed to be quick to write and easy to understand. It is less complex than HTML and more focused on the content rather than the presentation, making it an excellent tool for scientists, researchers, and analysts who use Jupyter Notebooks. Users can format their text with simple symbols like # for headings, * for emphasis, and []() for links, which allows for more efficient writing without the need to switch between the keyboard and mouse or to remember complex tags.
  • Readability of Plain Text. The primary advantage of Markdown is its readability. Even without rendering, the text is comprehensible, making the document understandable in its raw form. This readability makes sharing and reviewing code and content more straightforward, as the narrative and annotations remain clear outside the Jupyter environment. This is especially useful for version control systems, where differences in content need to be easily identified.
  • Integration with Code. Markdown cells in Jupyter Notebooks seamlessly integrate with code cells. This allows for a narrative approach to data analysis, where the rationale behind each code cell can be explained right above or below the code. This integration is crucial for creating a story around the data, explaining the logic of the code, and making the entire analysis more transparent and replicable.
  • Support for Rich Text Elements. Markdown in Jupyter Notebooks supports a variety of rich text elements essential for data science communication. These include:
    • Headings and Subheadings. Organizing content into sections and subsections.
    • Bold and Italic Text. Emphasizing important points and terms.
    • Lists. Enumerating steps, features, or items for clarity.
    • Hyperlinks. Referencing external resources, providing quick access to further reading and supporting materials.
    • Images. Integrating visual content like graphs, charts, and diagrams.
    • Tables. Displaying structured data for comparison and analysis.
    • LaTeX Equations. Writing mathematical notation for precise expression of formulas and models.
    • Code Blocks and Syntax Highlighting. Including examples of code and highlighting syntax for various programming languages.

Jupyter Notebooks’ support for Markdown goes a long way in making scientific computing and data analysis tasks more user-friendly. The combination of Markdown for documentation and narrative, with code cells for executable content, makes Jupyter Notebooks an ideal tool for interactive computational narratives, where the story of the analysis is told through both words and code.

Basics Markdown Syntax in Jupyter Notebooks

Understanding the basics of Markdown syntax is essential for creating well-structured and readable documents. Here’s how to use the key elements of Markdown in your Jupyter Notebooks:


Headings are used to organize content hierarchically. Use the # symbol followed by a space to create a heading. The number of # symbols before the heading text represents the level of the heading:

# Heading 1

## Heading 2

### Heading 3

#### Heading 4

##### Heading 5

###### Heading 6

Emphasis (Bold and Italics)

To emphasize text, you can make it italic or bold. For italics, wrap the text in a single asterisk * or underscore _. For bold, use double asterisks ** or underscores __:

*italicized text*

_italicized text_

**bold text**

__bold text__

Ordered and Unordered Lists

Lists are a great way to present information point by point. Use asterisks *, plus signs +, or hyphens for unordered lists and numbers followed by a period for ordered lists:

– Unordered list item 1

– Unordered list item 2

– Unordered list item 3

  1. Ordered list item 1
  2. Ordered list item 2
  3. Ordered list item 3

Links and Images

To add hyperlinks, wrap the link text in square brackets [], and then the URL in parentheses ():

[OpenAI]( [OpenAI](

For images, the syntax is similar, but you start with an exclamation mark !, followed by the alt text in square brackets [], and then the URL or path to the image in parentheses ():

![Alt text for image](

Code Formatting and Highlighting

Inline code can be indicated with backticks `:

`Inline code` with backticks

`Inline code` with backticks

For blocks of code, you can use triple backticks “` or indent each line with four spaces. To add syntax highlighting, specify the language directly after the first set of backticks:


def hello_world():

print(“Hello, world!”)


Understanding and using these basic Markdown syntax elements will significantly enhance the readability and structure of your Jupyter Notebook documents, making them more useful and accessible to readers.

Tips for Writing Effective Markdown in Jupyter

  • Organizing Content with Headings and Subheadings. Effective use of headings and subheadings creates a clear structure within your notebook, which guides readers through your analysis. Headings should be descriptive and reflect the content of the section they introduce. Use a hierarchical structure starting with # for main headings, and progressively more # symbols for subheadings (e.g., ##, ###). This not only improves readability but also helps in creating a navigable table of contents.
  • Using Lists to Break Down Complex Ideas. Lists are excellent tools for breaking complex ideas into smaller, digestible pieces. Use unordered lists (bullets) to present a collection of points without a natural sequence. When detailing a process or presenting a sequence of steps, use ordered lists (numbers). This visual separation of concepts can make your logic flow more apparent and easier to follow.
  • Including Images and Graphs to Complement Explanations. “A picture is worth a thousand words,” and in data science, images and graphs can be essential for conveying complex information simply and effectively. Use images to illustrate complex concepts, and graphs to display data and analysis results. In Markdown, you can embed images using the ![Alt text](URL) Ensure your images are well-captioned and include alt text for accessibility.
  • Writing Mathematical Equations Using LaTeX Syntax. Jupyter Notebooks’ support for LaTeX syntax is invaluable for writing mathematical notation. Enclose LaTeX commands in dollar signs $ for inline equations, and double dollar signs $$ for display equations. For example:

Inline equation: $E = mc^2$

Display equation:


e^{i\pi} + 1 = 0


This feature allows for precise expression of complex mathematical ideas, which is critical in many data science applications.

  • Integrating Markdown with Code Cells. Integrating Markdown with code cells in Jupyter Notebooks is a powerful way to combine narrative with code. Here’s how you can use Markdown to enhance the comprehension and impact of your code cells.
  • Using Markdown to Explain Code Logic. Markdown cells can be placed before or after code cells to explain the logic behind the code. This is where you can outline the purpose of the following code block, describe the algorithm, or discuss the reasons for choosing a particular method. By explaining the logic in plain language, you help others follow the decisions that led to your code structure, making the code more approachable and understandable.
  • Documenting Data Analysis Steps. Data analysis is a step-by-step process, and each step should be documented to provide context to the analysis. Use Markdown cells to:
    • Describe the data being used and its source.
    • Explain data preprocessing steps like cleaning and transformation.
    • Discuss the choice of analysis or modeling techniques.
    • Outline the hypotheses being tested or the questions being answered.

Documenting these steps will not only help others to follow along but also serve as a reminder to your future self about the flow and decisions made during the analysis.

  • Annotating Visualizations and Results. After executing code cells that generate visualizations or produce results, use subsequent Markdown cells to:
    • Interpret the visualizations or results.
    • Discuss any surprising or expected findings.
    • Suggest reasons for the observed outcomes.
    • Recommend next steps based on the results.

Annotations should add value to the visualizations by offering insights that might not be immediately obvious from the visuals alone. This can include pointing out patterns, trends, or anomalies that are significant.

Best Practices for Integration

  • Proximity: Place Markdown cells close to the related code cells to provide immediate context.
  • Segmentation: Use headings within Markdown cells to segment different aspects of the explanation, such as “Objective,” “Methodology,” “Findings,” and “Conclusions.”
  • Clarity: Be concise and clear in your explanations. Avoid using overly complex language.
  • Consistency: Maintain a consistent style and tone throughout your notebook. If you start by addressing the reader directly, continue to do so. If you use certain terms to describe elements of your code, use them consistently.
  • Comprehensiveness: Ensure that your Markdown cells provide all necessary information to understand the code and its output. Assume the reader has no prior knowledge of your work.

Integrating Markdown with code cells is a hallmark of good notebook practice. It transforms a simple script into a comprehensive computational narrative that is engaging and informative. This approach to combining code and narrative is part of what makes Jupyter Notebooks an invaluable tool for data science.


Embracing Markdown in your Jupyter Notebook workflow is not just a best practice; it’s a step towards building a more coherent, transparent, and accessible data science practice. Whether you’re cleaning data, performing complex analyses, or creating predictive models, Markdown documentation can transform your notebooks from mere code containers to comprehensive, educational stories.

I encourage you to integrate Markdown into your daily Jupyter routines. Begin with simple annotations and gradually expand to more complex formatting as you become more comfortable with the syntax. The effort you put into learning Markdown will pay dividends in the form of clearer, more useful, and more impactful Jupyter Notebooks.

Remember, the goal is not just to write code but to write about your code. This practice will not only benefit your peers and collaborators but also serve as a valuable resource for your future self. Markdown is your ally in this journey, making the documentation process as smooth and efficient as possible. Embrace it, and you’ll find your work reaching new heights of professionalism and utility.

About Glen Dimaandal

Glen Dimaandal
Glen Dimaandal is a data scientist from the Philippines. He has a post-graduate degree in Data Science and Business Analytics from the prestigious McCombs School of Business in the University of Texas, Austin. He has nearly 20 years of experience in the field as he worked with major brands from the US, UK, Australia and the Asia-Pacific. Glen is also the CEO of SearchWorks.PH, the Philippines’ most respected SEO agency.
Glen Dimaandal
Glen Dimaandal is a data scientist from the Philippines. He has a post-graduate degree in Data Science and Business Analytics from the prestigious McCombs School of Business in the University of Texas, Austin. He has nearly 20 years of experience in the field as he worked with major brands from the US, UK, Australia and the Asia-Pacific. Glen is also the CEO of SearchWorks.PH, the Philippines’ most respected SEO agency.

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