p_foundations_google_colab.qmd

---
title: 'Introduction to Google Colab'
---

{{< video https://youtu.be/fp-2xtbFRYo?si=JO_AMNhh_XOJaFsP >}}  

## Learning Objectives

- Understand what Google Colab is and its advantages for data science and AI
- Learn how to access and navigate Google Colab
- Create and manage notebooks in Google Colab
- Run Python code in Colab cells
- Use text cells for explanations and formatting
- Import and use pre-installed libraries for data analysis
- Import and use data to perform analysis
- Share Colab notebooks

## Introduction

Google Collaboratory, or Colab for short, is a free online platform that allows you to work with Python or R code in your browser. It's a great way to get started with Python, as you don't have to install anything on your computer.

Some limitations if you're running heavy workload though. Can get timeout. But for beginner data analysts, it's perfect and free.

Note that this document is a summary of the video, rather than a replacement. You should watch the video for a more complete experience.

## Getting Started With Colab

- Search for "Google Colab" in your favorite search engine. 
- Usually the first result will be the correct one. Currently, it's colab.research.google.com, but it may change in the future.
- Sign in with your Google account (create a Gmail account if you don't have one)

## Creating and Managing Notebooks

- Notebooks are the main way to organize work in Colab. They contain code cells and text cells.
- Create a new notebook: File > New Notebook
- Rename your notebook for better organization

## Working With Code Cells

- There should be a button at the top left that allows you to add code or text cells
- Code cells are where you write and execute Python code
- Type ``1 + 1 in a cell then run it
- Run a cell by clicking the play button or using keyboard shortcuts:
  - `Command + Enter` on Mac or `Ctrl + Enter` on Windows: Run the current cell
  - `Shift + Enter`: Run the current cell and create a new one below
- Try to get comfortable with the keyboard shortcuts
- Your code cell may take a while to run the first time, as the Python engine needs to be initialized
- When you run a cell, the final output is displayed below the cell
- To see multiple outputs, explicitly print them with `print()` For example:

```python
print(1)
print(2)
```

## Text Cells

- Use text cells for explanations and titles
- The toolbar above the text cell allows you to format text, but pay attention to the generated markdown

## Example of Working With Data

- Click on the Files tab to see the "sample_data" folder
- Import the California housing test dataset:

```python
import pandas
housing_data = pandas.read_csv("/content/sample_data/california_housing_test.csv")
housing_data.describe()
```
- View the dataset by typing `housing_data` in a cell and running it

## Practice Q: Importing Data

- Import the "california_housing_train" dataset
- Use the `describe()` function to get a summary of the dataset

## Getting Data From Your Drive

- In the Files tab, click the button to mount your drive
- Create a folder and upload a CSV file from your computer to the folder
- We can import the data with pandas as before

## Practice Q: Importing Data

- Import a CSV file you uploaded to your drive
- Use the `describe()` function to get a summary of the dataset

## Where Is Your Notebook Saved?

- All work is automatically saved to your Google Drive
- Access your notebooks at drive.google.com in the "Colab Notebooks" folder

## Sharing and Collaborating

- Share notebooks with a link, giving viewer or editor access
- Access notebooks later from your Google Drive
- Download notebooks in various formats (ipynb, py)

## Conclusion

Google Colab provides a powerful, accessible platform for data science and AI projects. Its pre-configured environment and easy sharing features make it an excellent way to get started with data analysis.