🔵 Data Science · Lesson 34

Jupyter Notebook and Google Colab

What is Jupyter Notebook and Google Colab?

Jupyter Notebook and Google Colab means jupyter Notebook and Google Colab are popular tools for writing Python code, notes, outputs and charts together.

In real programs, this topic helps in writing code with notes. Learn the idea first, then type the program yourself and compare the output.

💡 At a Glance

Point	Details
Course Area	Data Science Tools and concepts used to analyse, clean and present data.
Main Use	writing code with notes
Example File	`jupyter-colab.py`
Practice Focus	Run, change values, and explain the output line by line.

Why should you learn this?

It is useful for writing code with notes.
It connects with showing charts and outputs.
It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Term	Meaning
notebook	Interactive document that mixes code, output and notes.
cell	Single executable block in a notebook.
markdown	Simple text formatting used for notes in notebooks.
Colab	Cloud-based notebook service useful for Python and ML practice.
data science workflow	data science workflow is an important term in this topic.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

Basic Pattern

import pandas as pd
data = {"Name": ["Aarav", "Riya"], "Marks": [85, 92]}
df = pd.DataFrame(data)
df

Complete Example Program

Python – jupyter-colab.py

# In a notebook cell
import pandas as pd

data = {"Name": ["Aarav", "Riya"], "Marks": [85, 92]}
df = pd.DataFrame(data)
df

Expected Output

Name Marks 0 Aarav 85 1 Riya 92

Program Explanation

import pandas as pd imports ready-made features from a module/library.
data = {"Name": ["Aarav", "Riya"], "Marks": [85, 92]} stores a value in data.
df = pd.DataFrame(data) stores a value in df.
df performs the next step of the program logic.

Where will you use it?

Writing code with notes.
Showing charts and outputs.
Sharing notebooks.

Common Mistakes

Analysing data before checking missing values, duplicates and data types.
Changing original data without keeping a clean copy.
Creating charts without title, labels or explanation.

Practice Tasks

Type the program in jupyter-colab.py and run it.
Change input values or sample data and observe the new output.
Create one example related to writing code with notes.
Write 5 lines explaining the logic in your own words.

Summary

Jupyter Notebook and Google Colab is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.

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