Cloud-Based Python Vibe Coding — Trying Python without Local Setup using Google Colab

There are essentially two ways to start using Python for data analysis: set up Python on your own machine, or run it in the cloud via Google Colab. This page is the entry point for the second option — for those who want to try Python without installing anything, or who would rather not use their own machine's resources.

01 Two paths — which one fits you

Neither option is "correct" in the abstract. Pick the one that fits your goals and constraints.

Path A — Local

Python Vibe Coding Course

You plan to use Python continuously in your research
You want full reproducibility (requirements.txt, DOI on Zenodo) when publishing
You handle larger datasets or longer computations
Requirements: 8 GB+ RAM and 1–2 hours of initial setup

Path B — Cloud (this page)

Google Colab

You just want to try Python before committing
You'd rather not install new software on your machine
Your machine has limited resources, or you work on an iPad / Chromebook
Requirements: a Google account and a browser

"Try it in Colab first, then move to a local setup once it sticks" is also a perfectly common path. You don't have to commit to the heavier option upfront — start with whichever fits where you are now.

02 What is Google Colab

Google Colaboratory (Colab) is a free cloud-hosted Jupyter Notebook service from Google. Sign in to your Google account in a browser and you have Python ready — zero installation.

No setup required — Python, pandas, numpy, matplotlib and other major libraries are pre-installed
Free — Anyone with a Google account can use it within reasonable limits
Easy to share — Notebooks share like Google Docs (link sharing, collaborative editing)
GPU available — Free GPU access (with quotas) for ML workloads
Google Drive integration — Store data and notebooks in your own Drive

The free tier has limits: sessions time out after about 12 hours, compute resources are capped, and long heavy computations are not the right fit. For learning and exploration, however, the free tier is usually plenty.

03 What the linked article covers

On a separate blog, I have a step-by-step walkthrough on building a reproducible data analysis environment with Google Colab, LLMs, and Google Drive. It covers, in order:

Opening Colab for the first time and running your first cell
Asking ChatGPT or Claude to write code for the analysis you want, in plain language
Pasting the resulting code into Colab and running it without breaking anything
Storing data in Google Drive and mounting Drive from Colab to read/write files
Saving outputs (figures, CSVs) back to Drive and sharing them with collaborators

Following along while reading takes about 30 minutes to an hour to cover the basics.

Read the blog post ↗

04 When you outgrow Colab

As you use Colab more, certain frictions tend to surface: sessions time out and interrupt long jobs, uploading big datasets to Drive every time gets tedious, and version-pinning is harder to communicate to collaborators. When that happens, the natural next step is a local setup.

No more session timeouts; you can run long computations
Easy access to large datasets already on your machine
requirements.txt captures your environment, so reproducibility for paper publication becomes straightforward
Editors like VSCode give a more comfortable writing experience

For setting up a local environment, see the Python Vibe Coding Course on this site, which walks through the whole flow in five parts. It also covers how to migrate analysis you wrote in Colab to a local environment.

✓ Summary

There are two paths to start with Python: local and cloud (Colab)
If you want to try Python without installing anything, start with Colab
For serious research workflows and reproducibility on publication, you'll likely want to move to a local setup eventually
Step-by-step instructions live in the linked blog post and the Python Vibe Coding Course