Code Cells in Jupyter Notebook

Now that we’ve discussed markdown cells at length, we’ll move on to the other type of cell in Jupyter Notebook where the actual coding happens: the code cell. As the name implies, this is the type of cell where Python code can be typed and run. It can be used to perform a myriad of essential processes including data entry, mathematical operations, library importation, machine learning algorithms, and more.

As data scientists, we’ll be spending the vast majority of our time on code cells. In this article, we’ll have an in-depth iscussion on the nature of code cells, how to use them, and how to exercise best practices so you can get the most out of them.

Understanding Code Cells

Code cells are the building blocks of Jupyter Notebooks that allow for interactive computing and data analysis. They are editable blocks where you can write and execute snippets of code, typically in Python, although Jupyter supports many other languages as well. The purpose of code cells goes beyond merely running code; they provide an interactive environment where data scientists and analysts can experiment with data, test hypotheses, and develop entire workflows within a single, cohesive document. When a code cell is run, the Notebook’s kernel executes the code and returns the output directly below the cell, enabling immediate observation and iteration.

How Code Cells Differ from Markdown Cells

Unlike markdown cells, which are used for creating formatted text and documentation, code cells are where the actual computational work happens. While markdown cells use Markdown syntax for formatting, code cells accept raw code in the language that your Notebook’s kernel understands.

Markdown cells are static, meaning they display text and graphics as is, while code cells are dynamic, capable of producing different outputs depending on the input code and data. This dynamic nature is what makes code cells powerful tools for experimentation and problem-solving in coding.

The Anatomy of a Code Cell

A code cell in a Jupyter Notebook consists of several key components that facilitate its functionality:

  • Input Area. This is where you write your code. It can contain a single line or multiple lines of instructions. This area supports syntax highlighting, which helps in distinguishing keywords, variables, strings, and other elements of the code.
  • Output Area. Immediately below the input area, this is where the results of the executed code are displayed. Outputs can vary widely, from text and tables to charts and other visualizations. If a code cell performs calculations or generates text, the results appear here as plain output. If the code generates visual output, it appears as images inline with the rest of the Notebook.
  • Execution Indicator. Each code cell has an execution indicator which shows its state. The indicator could show a number, which represents the order in which the cell was last executed within the Notebook, helping keep track of the flow of operations. A ‘*’ in this indicator suggests that the cell is currently running, which is useful for understanding the progress of data processing, especially with time-consuming computations.

How Code Cells Execute Python Code and Other Supported Languages

Code cells execute code in the language that the Jupyter Notebook’s kernel supports. While Python is the most common language used in Jupyter Notebooks, kernels for many other languages like R, Julia, and Scala are also available. When a code cell is executed, this is what happens behind the scenes:

  1. Code Parsing. The code is parsed by the kernel. The kernel checks the syntax and other language-specific rules.
  2. Execution. The kernel then executes the code line by line or as a whole block, depending on the programming language’s nature.
  3. Output Rendering. After execution, the kernel sends any outputs back to the Notebook interface, which renders them in the output area of the code cell.
  4. Error Handling. If there’s an error in the code, it’s also displayed in the output area. Jupyter provides detailed traceback information to help diagnose the issue.

To the naked eye, all of these steps happen in a split second for simple executions, making it hard to grasp what’s actually taking place. Still, it’s important to grasp what actually happens to your code and how machines handle the processes.

Best Practices for Managing Code Execution

Managing the execution of code cells effectively is essential for maintaining the clarity and efficiency of your Notebook. Here are some best practices when writing and running code on Jupyter Notebook’s code cells:

  • Sequential Execution. Always run code cells in the logical order intended, especially in complex Notebooks, to ensure all variables and libraries are initialized correctly.
  • Isolated Execution. Sometimes, testing a specific code block without running the entire Notebook is necessary. Jupyter allows for the execution of a single cell or a group of cells independently.
  • Restarting the Kernel. If the Notebook becomes non-responsive or the order of execution is disrupted, restarting the kernel can clear the current state of the executed cells and allow for a fresh start.

Understanding the anatomy and functionality of code cells is crucial for leveraging the full capabilities of Jupyter Notebooks. By mastering these elements, users can ensure their Notebooks are both effective as computational tools and clear in conveying their analytical narratives.

Executing Code in Cells

Executing code within Jupyter Notebooks is straightforward yet powerful. Here’s how to run code effectively in the interactive environment:

  1. Writing Code. Enter your code in the input area of a code cell. You can write anything from a simple calculation to complex functions or classes.
  2. Running the Cell. Execute the code by pressing Shift + Enter, clicking the run button in the toolbar, or clicking on the “play” icon near the code cell. This command runs the current cell, displays the output below it, and moves to the next cell.
  3. Immediate Feedback. After execution, the output (if any) appears directly below the code cell. This can include text, tables, error messages, or visualizations, depending on the code’s function.

Understanding Code Execution Order

In Jupyter Notebook, the order in which code cells are executed matters significantly:

  • Execution Order Indicator. Each code cell is prefixed with an indicator like In [3]:, which means the cell was the third one executed since the notebook was opened or the kernel was last restarted. This order affects how variables and functions are stored in the notebook’s memory.
  • Dependencies. If one cell relies on variables or functions defined in another, running them out of sequence can lead to errors or unexpected behavior, highlighting the importance of maintaining the intended execution flow.

Cell States and Kernel Interactions

The interaction between the cell states and the notebook’s kernel is vital for efficient notebook functionality:

  • Cell States: A cell can be in one of several states:
    • Editable. Where you can write and edit code.
    • Running: Indicated by an asterisk (*) in the execution indicator, showing the cell is currently processing.
    • Executed. Marked by a number in the execution indicator, showing the completion of execution and the sequence in which it was run.
  • Kernel. The kernel is the engine that runs the code written in your Jupyter Notebook. It handles all computations:
    • Starting/Stopping. You can manually start, stop, or restart the kernel via the notebook’s toolbar. Restarting the kernel is often used to reset the computational environment (clearing all variables and functions from memory).
    • Interrupting. If a code cell is taking too long to run, you might choose to interrupt the kernel to stop execution. This is useful for preventing infinite loops or overly long computations.

Kernel Responsiveness

Sometimes, the kernel might become unresponsive. Monitoring the kernel’s status (idle, busy, or dead) can help you manage how it interacts with your code cells. An unresponsive kernel may require a restart to continue working efficiently.

By understanding how to execute code, the significance of execution order, and the states of both cells and the kernel, users can more effectively manage their data analysis tasks within Jupyter Notebooks. This section not only boosts productivity but also aids in troubleshooting common issues that may arise during the interactive sessions.


Hopefully, this information helped you better understand the nature and function of code cells in Jupyter Notebook. In our next post, we’ll get into writing Python code to get your data science journey started in earnest.

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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