Day 34 of 50 Days of Python: Working with Jupyter Notebooks and Markdown for Reporting
Part of Week 5: Data Analysis and Visualisation
Welcome to Day 34 of our Python series! We're diving into a staple of the modern data workflow: Jupyter Notebooks. If you’ve ever needed to blend data, code, visuals, and explanations into one seamless report, this is the tool for you.
By combining Markdown with executable code blocks, Jupyter enables analysts, data engineers, and scientists alike to build clear, structured, and highly shareable reports. Doesn’t matter if you’re exploring a dataset, debugging a machine learning pipeline, or presenting insights to stakeholders, Jupyter Notebooks can turn raw looking basic code into nice looking narratives.
Getting Set Up with Jupyter Notebooks
You can run Jupyter Notebooks locally or in the cloud and this is the part I’d usually just give you both. But for ease of access for everyone I’d recommend using Google Colab, It’s 100% free to use and even has free GPU tiers for Machine Learning (something we’ll need for the next week of the Python series).
Understanding Jupyter & Markdown
A Jupyter Notebook is a hybrid document composed of code cells and Markdown cells. Here’s how they work together:
Code cells: Run Python (or R, Julia, etc.) and display output instantly.
Markdown cells: Provide context, commentary, titles, and instructions.
In terms of markdown most of you may have already used the basics when writing documentation, but if not this should give you an idea of syntax:
# Heading 1
## Heading 2
**bold**, *italic*, `inline code`
- Bullet points
1. Numbered lists
[Links](https://example.com)Advanced markdown usage enables you to embed images, tables and LaTeX equations. Something Substack needs to adopt, especially tables.
The Ideal Notebook Structure
Here’s a proven format for producing readable, structured notebooks:
Title & Objective
Import Dependencies
Load & Preview Data
Data Cleaning / Preparation
Exploratory Analysis
Visualisations
Key Insights
Next Steps or Recommendations
Each section should begin with a brief Markdown description of what you’re doing and why. The goal is to make your notebook self-explanatory to readers with minimal guidance. You can thank QA for this template as it’s something that was touched on during my apprenticeship with them.
Real-World Use Case: Marketing Campaign Analysis
Let’s say you're evaluating the performance of a cross-channel marketing campaign. Your stakeholders want a report, but they also want to understand how you arrived at your conclusions.
A Jupyter Notebook can include:
Charts showing campaign performance over time
Markdown blocks explaining anomalies or standout trends
Code loading and transforming data from a CSV or SQL source
Conclusions and business recommendations
You’ve just produced a data-driven report that is:
Reproducible
Visually intuitive
Shareable via GitHub, nbviewer, or as an HTML/PDF export
Next Up: Day 35: Streamlit for Building Simple Data Apps
While Jupyter Notebooks are perfect for exploration and reporting, sometimes you want to turn your work into an interactive app. Tomorrow, we’ll touch on Streamlit, a powerful yet lightweight framework that lets you build and deploy data apps with just a few lines of Python. Especially used now for deploying apps using custom GPT models, so it’s definately a skill you want to build.
As per usual, until then… Happy coding.
