Skip to main content

1.8 Jupyter Notebooks: How They Work

Jupyter Notebooks have revolutionized the way data analysis and exploration are conducted. These interactive documents combine code, visualizations, and explanatory text, allowing seamless collaboration and knowledge sharing among health informaticists. Let's delve into the technical details of how Jupyter Notebooks work and why they're an essential tool in your health informatics journey.

Anatomy of a Jupyter Notebook​

A Jupyter Notebook is composed of individual cells, which can contain either code or Markdown content. This modular structure promotes a dynamic workflow where you can iteratively execute code, visualize results, and provide explanations within the same document.

Code Cells​

Code cells are where you write and execute your code. They can contain code in various programming languages, including Python, R, and Julia. When you run a code cell, its output is displayed immediately below the cell. This allows you to observe the effects of your code and make real-time adjustments.

# This is a Python code cell
x = 5
y = 10
sum_result = x + y
sum_result

Markdown Cells​

Markdown cells, like this one, enable you to include explanatory text, headings, bullet points, images, and more. They use simple Markdown syntax for formatting.

Markdown Example:

## Health Data Exploration

In this notebook, we'll explore a dataset containing patient vitals and medical history.

Execution Flow​

Jupyter Notebooks follow a top-down execution flow. This means that you should run cells in sequence, starting from the top. The results of code cells persist throughout the notebook session, allowing you to refer back to previous computations.

Importing Libraries​

In Jupyter Notebooks, you can import libraries just like you would in a regular Python script. Here's an example of importing the pandas and numpy libraries:

import pandas as pd
import numpy as np

Installing Packages​

If you're working in a remote Jupyter Notebook environment and need to install a new Python package, you can do so using code cells. For example, to install the pandas package, you can run:

!pip install pandas

Interactive Exploration and Collaboration​

Jupyter Notebooks empower you to visualize data, analyze trends, and communicate findings within the same environment. This interactive nature encourages exploration and hypothesis testing, making it a valuable tool for health informaticists.

Code Reusability and Version Control​

Jupyter Notebooks facilitate code reusability by allowing you to define functions and variables in one cell and use them in subsequent cells. Additionally, you can integrate version control systems like Git to track changes to your notebook and collaborate with others effectively.

Integrating with Other Tools​

Jupyter Notebooks seamlessly integrate with a wide range of data analysis and visualization libraries, making it easy to create interactive visualizations, statistical analyses, and machine learning models.


Jupyter Notebooks combine code, data, and narrative in a single platform, fostering effective communication and exploration. As you dive into health informatics, harness the power of Jupyter Notebooks to analyze health data, develop insights, and drive meaningful outcomes.

Incorporating Jupyter Notebooks into your workflow can greatly enhance your ability to analyze health data, develop insights, and collaborate with peers to drive meaningful outcomes in the field of health informatics.