Working with Colab Notebooks Intermediate

Colab notebooks are powerful interactive documents that combine executable code, rich text, visualizations, and more. This lesson covers cells, magic commands, package management, Google Drive integration, and collaboration features.

Code Cells and Text Cells

Colab notebooks contain two types of cells:

• Code cells: Execute Python code. Output appears directly below the cell. Variables persist across cells within the same runtime session.
• Text cells: Use Markdown for documentation. Support headings, lists, links, images, LaTeX math, and HTML.

# Code cell example: variables persist across cells
data = [1, 2, 3, 4, 5]
total = sum(data)
print(f"Sum: {total}, Mean: {total/len(data)}")

Magic Commands

Colab supports IPython magic commands that extend beyond standard Python:

# Line magics (single %)
%timeit sum(range(1000))          # Time a single line
%who                               # List all variables
%whos                              # List variables with details
%env MY_VAR=value                  # Set environment variable
%matplotlib inline                 # Enable inline plots

# Cell magics (double %%)
%%timeit                           # Time the entire cell
x = sum(range(1000))

%%capture output                   # Capture cell output
print("This is captured")

%%writefile script.py              # Write cell contents to file
print("Hello from script")

%%bash                             # Run bash commands
echo "Hello from bash"
ls -la

Installing Packages

Use the ! prefix to run shell commands, including pip:

# Install a package
!pip install transformers

# Install a specific version
!pip install pandas==2.1.0

# Install multiple packages
!pip install plotly bokeh streamlit

# Install from a requirements file
!pip install -r requirements.txt

# Upgrade a package
!pip install --upgrade scikit-learn

# Check installed version
!pip show tensorflow

Importing Libraries

Many popular libraries are pre-installed in Colab:

# Pre-installed in Colab
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
import tensorflow as tf
import torch

# Check versions
print(f"NumPy: {np.__version__}")
print(f"Pandas: {pd.__version__}")
print(f"TensorFlow: {tf.__version__}")
print(f"PyTorch: {torch.__version__}")

Forms and Interactive Widgets

Colab supports special form fields and interactive widgets:

# @title Form Fields Example
name = "World"  # @param {type:"string"}
count = 3       # @param {type:"integer"}
rate = 0.5      # @param {type:"number"}
use_gpu = True  # @param {type:"boolean"}

for i in range(count):
    print(f"Hello, {name}! (iteration {i+1})")

# Interactive widgets with ipywidgets
from ipywidgets import interact, widgets

@interact(x=(0, 10, 1), y=(0, 10, 1))
def multiply(x=3, y=5):
    print(f"{x} * {y} = {x * y}")

Mounting Google Drive

Access your Google Drive files directly from Colab:

from google.colab import drive
drive.mount('/content/drive')

# Now access files at /content/drive/MyDrive/
import pandas as pd
df = pd.read_csv('/content/drive/MyDrive/data/my_dataset.csv')
print(df.head())

Sharing Notebooks

Share Colab notebooks just like Google Docs:

• Share button: Click "Share" in the top-right corner to share with specific people or create a link
• Permissions: Set viewers (can run but not edit), commenters, or editors
• Copy link: Anyone with the link can view or copy the notebook
• Publish: Share on social media or embed in websites

Version History

Colab keeps track of changes to your notebooks:

• Go to File → Revision history to see all past versions
• Name important versions with File → Save and pin revision
• Restore any previous version by selecting it from the history
• Compare changes between versions side by side

Collaboration Features

Colab supports real-time collaboration similar to Google Docs:

• Real-time editing: Multiple users can edit the same notebook simultaneously
• Cursor visibility: See where collaborators are editing in real time
• Chat: Use the built-in chat panel to communicate while working together
• Comments: Add comments to specific cells for code review or discussion
• Shared runtime: Each collaborator gets their own runtime, so running code does not interfere