Unit -1 -Python Pandas – I | Part 1| Informatics Practices | Sumita Arora | Class 12| NCERT Syllabus
Class 12 Informatics Practices – Unit 1: Python Pandas – Full Introduction (Day 1)
Welcome to Day 1 of our Class 12 Informatics Practices series! In this video, we begin Unit 1: Python – Pandas – 1, following the Sumita Arora textbook as prescribed in the CBSE curriculum. This session provides a complete introduction to the Pandas library in Python — a powerful tool used for data analysis and manipulation.
Whether you’re just starting or revising for your CBSE board exams, this video will build a strong foundation with clear explanations, analogies, and hands-on coding.
Timestamped Breakdown for Easy Navigation:
00:00 – Introduction to Informatics Practices and Sumita Arora Book
00:45 – Overview of Unit 1: Python Pandas
01:20 – Topics to Be Covered in the Unit
02:50 – What is Pandas?
04:30 – Understanding Python Libraries
06:15 – Why We Use Libraries in Programming
07:40 – Real-World Applications of Pandas
09:05 – Features and Advantages of Pandas
11:10 – Pandas in the Python Ecosystem
14:20 – Core Capabilities of Pandas
17:00 – What is a Data Structure?
18:25 – Explanation of Series and DataFrame
20:10 – Pandas Series Data Structure Explained
21:40 – Pandas DataFrame Data Structure Explained
23:00 – Differences Between Series and DataFrame
25:00 – Importing Pandas and NumPy
26:00 – Creating a Simple Series in Pandas
27:30 – Understanding Default Indexing in Series
Section 1.1 – Introduction to Pandas
What is Pandas?
Pandas is a Python library used for data manipulation and analysis.
It was developed by Wes McKinney and is built on top of the NumPy library.
The name "Pandas" comes from "panel data system."
Pandas helps us handle multi-dimensional and structured data efficiently.
What is a Library?
A library is a collection of pre-written code created by developers.
Instead of writing everything from scratch, we can import a library and use its functions or classes directly.
Think of a library like your school library:
A collection of books, each written by a different author.
In programming, a library is a collection of programs written by other programmers.
Why Use Libraries?
Using libraries:
Saves time
Offers reusable code
Helps access powerful tools without reinventing the wheel
So, Python libraries = collections of useful pre-written code.
Why Use Pandas?
Free and open source (BSD license)
High-performance data structures
Easy to use
Handles both 1D and 2D structured data
Widely used in data science and machine learning
Pandas is widely adopted because it:
Supports multiple file formats
Can handle large datasets
Easily selects subsets of data
Fills missing data
Groups and aggregates data
Reshapes and pivots datasets
Supports time series
Integrates with visualization libraries like Matplotlib and Seaborn
Section 1.3 – Pandas Data Structures
Pandas provides three main data structures:
Series (1D)
DataFrame (2D)
Panel (3D, deprecated – not covered here)
We’ll focus mainly on:
Series: One-dimensional, homogeneous data
DataFrame: Two-dimensional, heterogeneous data
What is a Data Structure?
A data structure is a way of organizing and storing data in memory so it can be accessed and modified efficiently.
It defines:
How data is stored
How data is accessed
What operations can be performed
How efficiently those operations are executed
Series in Pandas
One-dimensional
Stores homogeneous data
Represented as an indexed array
Supports both default indexing (0,1,2...) and custom labels
Example:
Index: 0, 1, 2, 3, 4
Values: 10, 20, 30, 40, 50
You can also create your own custom labels.
Key points about Series:
One-dimensional
Homogeneous
Indexed
Supports labeling
DataFrame in Pandas
Two-dimensional
Stores heterogeneous data
Contains rows and columns
Can be indexed and labeled
Similar to a table or spreadsheet
Series vs DataFrame
Feature Series DataFrame
Dimension 1D 2D
Data Type Homogeneous Heterogeneous
Indexing Single Index Row and Column Index
Mutability Values mutable, size fixed Fully mutable
Note: Changing the size of a Series creates a new Series internally.
But in DataFrame, rows and columns can be added or removed directly.
How to Use Pandas in Python
Before using Pandas, you need to install it:
pip install pandas
pip install numpy
Then, import it into your Python program:
import pandas as pd
import numpy as np
We usually use:
pd as the alias for pandas
np as the alias for numpy
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