01python Intro Ipynb Colab Google Colab

Leo Migdal

-Nov 18, 2025, 7:15 PM

There was an error while loading. Please reload this page. Google Colab (Colaboratory) has become a go-to platform for data scientists, machine learning practitioners, and Python developers due to its free access to GPUs, easy sharing, and seamless integration with Google Drive. However, one common challenge users face is reusing code from existing Jupyter notebooks (.ipynb files) stored in Google Drive. Unlike regular Python files (.py), .ipynb files are JSON-based and not natively importable as modules in Colab. This guide will walk you through the process of importing .ipynb modules from Google Drive into Google Colab, enabling you to reuse functions, classes, and code snippets efficiently.

By the end, you’ll be able to modularize your code and streamline your Colab workflows. Before you begin, ensure you have the following: Google Colab runs in a cloud environment, so it can’t directly access files on your local machine or Google Drive unless explicitly authorized. To connect Colab to your Drive: Verification: You’ll see a message like Mounted at /content/drive. Your Drive files are now accessible at the path /content/drive/MyDrive/ in Colab.

Python Intro for Colab is a set of Jupyter Notebooks that can directly be executed in Google Colaboratory (https://colab.research.google.com), rendering the first steps in Python as easy as possible. (c) Peter H. Gruber. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This provides a short tutorial for Google Colab as an alternative to Jupyter for running Python code. We show how to bring in, modify and run a Jupyter Notebook from a Github repository.

Colab (short for “Colaboratory”) is a Google cloud service. It allows users to write and execute Python code in a web-based environment without needing to install anything locally. Within limits, Colab is free to use, and it interacts with a user’s Google Drive, so Colab notebooks can import additional Python libraries from *.py files. Additionally, the instructions here would allow usage from a Chromebook or on a CPU that does not allow local, laptop file storage. To demonstrate Colab, we will use a case study of running the Jupyter Notebook in this Pandas introduction Github repository called Pandas_Intro_For_Noncoders. This tutorial walks step-by-step through using Colab to run the notebook including modifying the repository notebook to import its data from a repository folder on Google Drive.

Note that several helpful code snippets are available in pasteable format at the bottom of this blog. Opening Colab and Cloning the Pandas_Intro_For_Noncoders Github Repository <img decoding="async" class="wp-image-2363 aligncenter lazyload" src="https://datadelveengineer.com/wp-content/uploads/2023/10/colab_clone_repository.png" alt="" width="675" height="413" /> There was an error while loading. Please reload this page. Welcome to the world of data programming!

Before we dive into Python itself, let's get familiar with the main tool we'll be using throughout this course: Google Colaboratory, or Colab. Think of it as your digital workbench for all things data science in this class. Imagine you're trying to build something complex, maybe assemble furniture or cook a gourmet meal. You could try to do it with just a few basic, separate tools scattered around. But isn't it much easier if you have a dedicated workshop or a well-organized kitchen with everything you need integrated and within reach? An IDE, which stands for Integrated Development Environment, is like that well-equipped workshop, but for writing computer code.

It brings together all the essential tools you need into one convenient place, making the process of writing, testing, and fixing code much smoother. Typically, an IDE provides: Using an IDE helps you be more productive and focus on the logic of your analysis, rather than fighting with basic tools. For this course, Google Colab will be our IDE, specifically tailored for working with data in Python. That idea of an IDE being a programmer's "workshop" or creative "studio" isn't just an analogy we're using – it's reflected right in the names of many widely-used development tools! You might recognize names like:

01python Intro Ipynb Colab Google Colab

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By The End, You’ll Be Able To Modularize Your Code

Python Intro For Colab Is A Set Of Jupyter Notebooks

Colab (short For “Colaboratory”) Is A Google Cloud Service. It

Note That Several Helpful Code Snippets Are Available In Pasteable