Python Workflows

Published

February 2026

In this course you’ll use two types of Python files: notebooks for analysis and scripts for data acquisition. Understanding when to use each is more important than which tool you use to open them.

1 Notebooks vs Scripts

1.1 Notebooks (.ipynb)

Notebooks combine code, output, and documentation in a single file. You write code in cells and run them one at a time, seeing results immediately. Use notebooks for:

Learning new concepts and experimenting
Exploratory data analysis
Curve fitting and making plots
Creating documented analysis workflows
Figures for lab reports

1.2 Scripts (.py)

Scripts are plain text files containing Python code that runs from start to finish. Use scripts for:

Real-time data acquisition
Automated measurements
Long-running experiments
Code that controls hardware

1.3 Why Scripts for Data Acquisition?

For data acquisition and instrument control, scripts are preferred over notebooks because:

Reliability: Scripts run without the overhead of the Jupyter kernel
Error handling: Easier to implement proper cleanup if something goes wrong
Automation: Scripts can be run from the command line
Version control: Plain text files are easier to track in Git

The provided automation scripts (like 04_beam_profiler.py) are designed to run as standalone scripts, not in notebooks.

1.4 Quick Reference

Task	Use
Learning a new concept	Notebook
Quick data exploration	Notebook
Fitting and plotting data	Notebook
Writing lab report figures	Notebook
Real-time data acquisition	Script
Automated measurements	Script
Long-running experiments	Script
Controlling hardware	Script

2 Recommended Tools

2.1 VS Code (Recommended)

VS Code handles both notebooks and scripts, making it our recommended environment. It’s already installed on the lab computers. If you want to install it on your personal computer, download it from code.visualstudio.com.

To run a notebook: Open any .ipynb file and run cells with Shift+Enter.

To run a script: Open a .py file and click the play button (top right) or press F5.

2.1.1 Required Extensions

Install these extensions (click the Extensions icon in the left sidebar or press Ctrl+Shift+X):

Extension	Publisher	Purpose
Python	Microsoft	Python language support
Pylance	Microsoft	Fast, feature-rich language server
Jupyter	Microsoft	Run notebooks in VS Code

2.1.2 Useful Shortcuts

Run cell: Shift+Enter
Select Python interpreter: Click the Python version in the bottom status bar
Open terminal: Ctrl+`
Command palette: Ctrl+Shift+P
Go to definition: F12 or Ctrl+Click

2.2 JupyterLab (Alternative)

JupyterLab is a browser-based environment for running notebooks. It’s a good alternative if you prefer the classic notebook experience.

To start JupyterLab:

py -m jupyter lab

This opens a browser window where you can create and run notebooks.

2.2.1 Useful Shortcuts

Run cell: Shift+Enter
Add cell above/below: A / B (in command mode)
Delete cell: D D (in command mode)
Switch to Markdown: M (in command mode)

Note

JupyterLab only runs notebooks. For scripts, use VS Code.

2.3 Google Colab (Alternative)

Google Colab runs notebooks in your browser with no software installation required. It’s a good option if you’re already familiar with it or want to work from a computer without Python installed.

To use Colab:

Go to colab.research.google.com
Sign in with your Google account
Create a new notebook or upload an existing .ipynb file

Limitation

Colab runs on Google’s servers, so it cannot communicate with lab hardware. Use Colab only for data analysis—you’ll need to collect data using the lab computers and then upload your data files to Colab for analysis.

3 File Organization

Here’s an example structure for organizing your lab work. Create a folder for each lab and keep your data, analysis notebooks, and scripts together:

phys4430/
├── gaussian-beams/
│   ├── data/              # Raw data files (CSV)
│   ├── beam_profiler.py   # Script for data acquisition
│   ├── analysis.ipynb     # Notebook for analysis
│   └── figures/           # Saved plots
├── lab-2/
│   ├── data/
│   ├── analysis.ipynb
│   └── ...
└── ...

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