What is Google Colab?

Google Colab (Colaboratory) is a free, cloud-hosted Jupyter Notebook environment.

It allows you to:

• Run Python in the browser (no local install)
• Use free GPUs/TPUs (great for ML workloads)
• Share notebooks easily
• Connect to Google Drive

For data analytics, Colab is excellent when:

• Your machine is slow
• You want a quick setup
• You want to share a notebook link with others

Create a new notebook

1. Go to: https://colab.research.google.com/
2. Sign in with a Google account
3. Click New notebook

A notebook opens with a default code cell.

Understanding Colab interface

Key menus you’ll use:

• Runtime: run cells, restart runtime, change hardware
• File: save a copy, download .ipynb.ipynb, download .py.py
• Edit: find/replace, shortcuts

Colab runs notebooks on remote servers called a runtime.

Run your first code

In a code cell:

print("Hello from Colab")

print("Hello from Colab")

Run with Shift + EnterShift + Enter.

Uploading datasets

You have a few common options.

Option 1: Upload directly (temporary)

• Left sidebar → Files
• Click Upload and choose your CSV

Important:

• Uploaded files are stored in the runtime temporary disk.
• If the runtime resets, uploaded files are gone.

Option 2: Google Drive (recommended)

Mount your Drive:

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

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

Then access files like:

import pandas as pd
path = "/content/drive/MyDrive/data/titanic.csv"
df = pd.read_csv(path)
df.head()

import pandas as pd
path = "/content/drive/MyDrive/data/titanic.csv"
df = pd.read_csv(path)
df.head()

Option 3: Download from a URL

import pandas as pd
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv"
df = pd.read_csv(url)
df.head()

import pandas as pd
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv"
df = pd.read_csv(url)
df.head()

Installing packages in Colab

Colab has many libraries installed, but not everything.

Use pippip inside a notebook:

!pip install yfinance

!pip install yfinance

Or make it a bit quieter:

!pip -q install yfinance

!pip -q install yfinance

Changing runtime hardware (GPU/TPU)

1. RuntimeRuntime → Change runtime typeChange runtime type
2. Set:
   • Hardware accelerator: GPU (or TPU)

You can confirm GPU availability:

import torch
print("CUDA available:", torch.cuda.is_available())

import torch
print("CUDA available:", torch.cuda.is_available())

(If you don’t use PyTorch, you can also check with other tools.)

Saving and exporting your work

Save to Drive

• FileFile → Save a copy in DriveSave a copy in Drive

Download notebook

• FileFile → DownloadDownload → .ipynb.ipynb

Export to Python

• FileFile → DownloadDownload → .py.py

Colab tips for data analytics

Quickly preview data

import pandas as pd
import numpy as np
 
df = pd.DataFrame({
    "city": ["Delhi", "Mumbai", "Pune"],
    "sales": [120, 90, 150]
})
 
df

import pandas as pd
import numpy as np
 
df = pd.DataFrame({
    "city": ["Delhi", "Mumbai", "Pune"],
    "sales": [120, 90, 150]
})
 
df

Display full columns/rows (Pandas)

import pandas as pd
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", 100)

import pandas as pd
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", 100)

Next

Continue to: Virtual Environments for Data Science to understand why environments matter and how to maintain clean project dependencies.