Tamil Nadu Class 12 Informatics Practices Complete Guide — Samacheer Kalvi

Class 12 Informatics Practices — Tamil Nadu State Board

Total Marks: Theory 70 + Practical 30 = 100

Syllabus Overview

Unit 1 — Python Revision

Key Concepts

Functions:

def greet(name):
    return "Hello, " + name

result = greet("Tamil")
print(result)

File Handling:

# Writing to file
f = open("data.txt", "w")
f.write("Hello")
f.close()

# Reading from file
f = open("data.txt", "r")
print(f.read())
f.close()

Modules:

• import math — mathematical functions
• import random — random numbers
• import datetime — date/time

List Comprehension:

squares = [x**2 for x in range(1, 6)]
# Output: [1, 4, 9, 16, 25]

Unit 2 — Data Handling with Pandas I

Series

A one-dimensional labelled array.

import pandas as pd

s = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
print(s)
print(s['b'])      # Access by label: 20
print(s[1])        # Access by position: 20

DataFrame

A two-dimensional labelled data structure.

data = {
    'Name': ['Anitha', 'Bala', 'Chitra'],
    'Marks': [85, 92, 78],
    'City': ['Chennai', 'Madurai', 'Coimbatore']
}
df = pd.DataFrame(data)
print(df)
print(df['Name'])           # Column access
print(df.iloc[1])           # Row by position
print(df.loc[0, 'Marks'])   # Specific cell

DataFrame Operations

df.head()         # First 5 rows
df.tail()         # Last 5 rows
df.shape          # (rows, columns)
df.info()         # Column types
df.describe()     # Statistics
df.isnull().sum() # Count missing values
df.dropna()       # Remove missing rows
df.fillna(0)      # Fill missing with 0

Filtering

# Rows where Marks > 80
filtered = df[df['Marks'] > 80]

# Multiple conditions
filtered = df[(df['Marks'] > 80) & (df['City'] == 'Chennai')]

Unit 3 — Data Handling with Pandas II

Groupby

# Average marks by city
grouped = df.groupby('City')['Marks'].mean()
print(grouped)

# Multiple aggregations
result = df.groupby('City')['Marks'].agg(['mean', 'max', 'min'])

Merging DataFrames

df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['A', 'B', 'C']})
df2 = pd.DataFrame({'ID': [1, 2, 4], 'Score': [90, 85, 70]})

# Inner join (common rows)
merged = pd.merge(df1, df2, on='ID', how='inner')

# Left join (all from left)
merged = pd.merge(df1, df2, on='ID', how='left')

Sorting

df.sort_values('Marks', ascending=False)   # Descending
df.sort_values(['City', 'Marks'])           # Multiple columns

Unit 4 — Data Visualisation with Matplotlib

import matplotlib.pyplot as plt

Line Chart

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 30, 20]
plt.plot(x, y, color='blue', marker='o', linestyle='--')
plt.title("Sales Trend")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.show()

Bar Chart

subjects = ['Tamil', 'English', 'Maths', 'Science']
marks = [85, 78, 92, 88]
plt.bar(subjects, marks, color='green')
plt.title("Student Marks")
plt.show()

Histogram

data = [55, 60, 65, 70, 75, 80, 85, 90]
plt.hist(data, bins=5, color='orange', edgecolor='black')
plt.title("Marks Distribution")
plt.show()

Pie Chart

sizes = [30, 20, 25, 25]
labels = ['Tamil', 'English', 'Maths', 'Science']
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title("Subject Marks Share")
plt.show()

Unit 5 — Database Concepts

Key Terms

Relational Model Concepts

• Entity: Real-world object (Student, Book)
• Entity-Relationship (ER) Diagram: Visual representation of entities and relationships
• Normalisation: Reducing redundancy — 1NF, 2NF, 3NF
• ACID properties: Atomicity, Consistency, Isolation, Durability

Unit 6 — SQL

DDL Commands (Data Definition Language)

-- Create table
CREATE TABLE Student (
    Roll_No INT PRIMARY KEY,
    Name VARCHAR(50),
    Marks INT,
    City VARCHAR(30)
);

-- Add column
ALTER TABLE Student ADD Grade CHAR(2);

-- Remove column
ALTER TABLE Student DROP COLUMN Grade;

-- Delete table structure
DROP TABLE Student;

-- Remove all records (keep structure)
TRUNCATE TABLE Student;

DML Commands (Data Manipulation Language)

-- Insert data
INSERT INTO Student VALUES (101, 'Anitha', 85, 'Chennai');

-- Update data
UPDATE Student SET Marks = 90 WHERE Roll_No = 101;

-- Delete rows
DELETE FROM Student WHERE Marks < 40;

-- Select all
SELECT * FROM Student;

-- Select specific columns
SELECT Name, Marks FROM Student;

-- Condition
SELECT * FROM Student WHERE City = 'Chennai';

-- Sorting
SELECT * FROM Student ORDER BY Marks DESC;

-- Pattern matching
SELECT * FROM Student WHERE Name LIKE 'A%';

Aggregate Functions

SELECT COUNT(*) FROM Student;              -- Number of rows
SELECT SUM(Marks) FROM Student;            -- Total marks
SELECT AVG(Marks) FROM Student;            -- Average
SELECT MAX(Marks) FROM Student;            -- Highest mark
SELECT MIN(Marks) FROM Student;            -- Lowest mark

-- Group by city
SELECT City, AVG(Marks) FROM Student GROUP BY City;

-- Having (filter after GROUP BY)
SELECT City, AVG(Marks) FROM Student 
GROUP BY City HAVING AVG(Marks) > 75;

Joins

-- INNER JOIN (matching rows only)
SELECT S.Name, C.Course_Name
FROM Student S
INNER JOIN Course C ON S.Roll_No = C.Roll_No;

-- LEFT JOIN (all from left, matching from right)
SELECT S.Name, C.Course_Name
FROM Student S
LEFT JOIN Course C ON S.Roll_No = C.Roll_No;

Unit 7 — Computer Networks

Types of Networks

Network Topologies

• Bus: All connected to one cable. Simple, single point of failure.
• Star: All connected to central hub/switch. Most common. Hub failure = network down.
• Ring: Each device connected to two others in loop. Token passing.
• Mesh: Every device connected to every other. Highly reliable, costly.

Protocols

Network Devices

• Hub: Broadcasts to all — obsolete
• Switch: Sends to specific device — efficient
• Router: Connects different networks, routes packets
• Modem: Converts digital ↔ analogue

Unit 8 — Cybersecurity

Common Threats

Safety Practices

• Use strong passwords (8+ chars, upper/lower/numbers/symbols)
• Enable two-factor authentication (2FA)
• Keep software updated (patches fix vulnerabilities)
• Use antivirus and firewall
• Avoid clicking unknown links (phishing)
• Use HTTPS websites only for sensitive data
• Regular data backup
• Use VPN on public networks

Cybersecurity Laws in India

• IT Act 2000: First cyber law — data protection, hacking penalty
• IT Amendment Act 2008: Cyberterrorism, identity theft provisions
• CERT-In: Indian Computer Emergency Response Team

Exam Tips

• SQL questions: Always write column names correctly; semicolon at end; use caps for SQL keywords
• Pandas questions: Show imports (import pandas as pd); print output where asked
• Matplotlib: Always include plt.show() at end
• Theory questions: Use table format for comparisons (types of networks, SQL commands)
• Practical exam: Practice at least 10 complete programs before exam day