Feed your spreadsheet to the Pandas!

Lightning talk: FOSS4G Auckland 21/11/2025

The Pandas library for Python streamlines data analysis by making number crunching both fast and intuitive. It excels at handling common "load-calculate-save" workflows with concise, readable code - ideal for everyday data tasks.

This ready to clone repository contains a beginner-friendly workflow using Python Notebooks and Pandas, designed for rapid experimentation and iteration, showing you how to:

• Load datasets
• Filter and transform data
• Perform basic calculations
• Plot outputs
• Export results

Note

There are a lot of ways to install Python, manage packages, run analyses and interact with Notebooks. Advanced users can take these recommendations with a hefty grain of salt - this repository outlines a simple and stable flow that should be easy for Python/Pandas beginners.

IPython Notebooks provide a visual interactive means of running Python scripts and immediately viewing the output. Notebooks are very handy in some situations, but not appropriate in other contexts. Sometimes, I will use a Notebook to develop core functionality using a test dataset, then copy the functional code out for something like an API service, other times a Notebook is all I need.

Prerequisites

• Python (version 3.13 recommended as of November 2025) installed and accessible from CLI as python, python3 or python3.13, depending on your system (referred to as <python> below)
• VS Code editor with Python and Jupyter extensions installed

Running

1. clone this repository (if using Git) or "Download ZIP" from GitHub
2. navigate a command prompt or terminal to the folder containing this readme
3. use your system Python (see Prerequisites) to create a new .venv folder and install the virtual environment in it: <python> -m venv .venv
4. activate the virtual environment:
   • Linux/macOS (bash/zsh): source .venv/bin/activate
   • Windows cmd.exe: .venv\Scripts\activate.bat
   • Windows PowerShell: .venv\Scripts\Activate.ps1
   • See the documentation for other configurations: https://docs.python.org/3/library/venv.html#how-venvs-work
5. install dependencies: pip install -r requirements.txt, which will install:
   • ipykernel Python Notebooks functionality
   • matplotlib plotting/charting
   • openpyxl read/write Excel
   • pandas data analysis library
6. open this folder in VS Code: code .
7. in VS Code open the example.ipynb file
8. use the run buttons to run specific cells or run all to run the entire Notebook