Informatics
Practices

🗺️ Complete Syllabus & Distribution of Marks

CBSE Informatics Practices (065), Class XII, Session 2026-27. The theory paper is 70 marks and the practical is 30 marks.

Unit 1 · Data Handling using Pandas and Data Visualization (25)

Unit 2 · Database Query using SQL (25)

Unit 3 · Introduction to Computer Networks (10)

Unit 4 · Societal Impacts (10)

Practical · Distribution of 30 Marks

📐 CBSE Sample Paper Blueprint

The 70-mark theory paper has 37 questions across five sections. This is the official CBSE design followed in the Sample Question Paper.

Unit-wise weightage in the paper

🎯 How to Use This Guide

🐼 Chapter 1 · Python Libraries & Pandas Series

A library is a collection of ready-made modules and functions. In Informatics Practices we mainly use NumPy (numerical arrays), Pandas (data handling) and Matplotlib (plotting). This chapter covers Pandas Series — a one-dimensional labelled array.

1.1 Python Libraries

A library bundles related modules so we can reuse code. The three key data-science libraries:

1.2 What is a Series?

A Series is a one-dimensional labelled array that can hold data of any single type (int, float, string, etc.). It has two parts: the data (values) and the index (labels). If you don't give an index, Pandas uses default positions 0, 1, 2, …

1.3 Creating a Series

import pandas as pd
import numpy as np
s = pd.Series([10, 20, 30])
print(s)
# 0    10
# 1    20
# 2    30
# dtype: int64

s = pd.Series([85, 90, 78], index=['Maths','Sci','Eng'])
print(s['Sci'])      # 90

d = pd.Series({'a':1, 'b':2, 'c':3})   # keys become the index
sc = pd.Series(5, index=[0,1,2])       # scalar repeated → 5,5,5

1.4 head(), tail(), Indexing & Slicing

1.5 Vector (Mathematical) Operations

Operations apply element-wise and align by index. Arithmetic with a scalar affects every element.

s = pd.Series([10, 20, 30])
print(s + 5)        # 15, 25, 35
print(s * 2)        # 20, 40, 60
print(s > 15)       # False, True, True

1.6 Useful Series Attributes

📝 Practice Question Bank — Chapter 1

🧾 Chapter 2 · Pandas DataFrames

A DataFrame is a two-dimensional, size-mutable, labelled data structure with rows and columns — like a table or spreadsheet. Each column can hold a different data type, and both rows and columns have labels (the row index and the column headers).

2.1 Series vs DataFrame

2.2 Creating a DataFrame

import pandas as pd
data = {'Name':['Asha','Ravi','Meera'], 'Marks':[85, 90, 78]}
df = pd.DataFrame(data)
print(df)
#     Name  Marks
# 0   Asha     85
# 1   Ravi     90
# 2  Meera     78

rec = [{'Name':'Asha','Marks':85}, {'Name':'Ravi','Marks':90}]
df = pd.DataFrame(rec)   # each dict becomes a ROW

2.3 Useful DataFrame Attributes

2.4 Selecting Columns and Rows

df['Marks']              # one column (as a Series)
df[['Name','Marks']]     # two columns (as a DataFrame)
df[0:2]                  # first two ROWS by slicing

2.5 Adding, Deleting & Renaming

# add a new column
df['Grade'] = ['A','A','B']
# add/append a row
df.loc[3] = ['Sara', 88, 'A']
# delete a column
df = df.drop('Grade', axis=1)      # axis=1 → column
# delete a row
df = df.drop(2, axis=0)            # axis=0 → row
# rename a column
df = df.rename(columns={'Marks':'Score'})

2.6 head(), tail() & Boolean Indexing

df.head(2)               # first 2 rows
df.tail(1)               # last row
df[df['Marks'] > 80]     # only rows where Marks > 80 (boolean indexing)

📝 Practice Question Bank — Chapter 2

🔁 Chapter 3 · Importing / Exporting Data (CSV)

A CSV (Comma-Separated Values) file stores tabular data as plain text, one row per line with values separated by commas. Pandas can import a CSV into a DataFrame and export a DataFrame to a CSV.

3.1 What is a CSV File?

Name,Marks,Grade
Asha,85,A
Ravi,90,A
Meera,78,B

3.2 Importing — read_csv()

import pandas as pd
df = pd.read_csv('students.csv')      # first row used as header
print(df.head())

3.3 Exporting — to_csv()

df.to_csv('result.csv')               # writes the DataFrame to a CSV
df.to_csv('result.csv', index=False)  # do NOT write the row index

3.4 CSV vs DataFrame

📝 Practice Question Bank — Chapter 3

📊 Chapter 4 · Data Visualization (Matplotlib)

Data visualization means presenting data as charts so trends and comparisons become easy to see. The pyplot module of Matplotlib draws line plots, bar graphs and histograms (the three required for the exam).

4.1 Why Visualize? The pyplot Module

Charts reveal patterns, trends and outliers faster than tables of numbers. We always start with:

import matplotlib.pyplot as plt

4.2 The Three Chart Types

4.3 Worked Examples

import matplotlib.pyplot as plt
days = [1,2,3,4,5]
temp = [30,32,31,29,33]
plt.plot(days, temp)
plt.title('Temperature over days')
plt.xlabel('Day'); plt.ylabel('Temp (°C)')
plt.show()

subjects = ['Maths','Sci','Eng']
marks = [85, 90, 78]
plt.bar(subjects, marks, color='violet')
plt.title('Marks by subject')
plt.show()

scores = [45,55,55,60,65,65,65,70,80,90]
plt.hist(scores, bins=5)
plt.title('Score distribution')
plt.show()

4.4 Customising a Plot

plt.plot(days, temp, label='Temp', color='green', marker='o')
plt.legend()
plt.savefig('temp.png')      # save before show()
plt.show()

📝 Practice Question Bank — Chapter 4

🔢 Chapter 5 · SQL Functions (Math / Text / Date)

SQL functions process values and return a result. Single-row functions return one value per row and fall into three groups: Math, Text (string) and Date. (Aggregate functions, which work on many rows, are in Chapter 6.)

5.1 Math Functions

SELECT POWER(5,2), ROUND(123.456,2), MOD(10,3);
-- 25      123.46      1

5.2 Text (String) Functions

SELECT UCASE('delhi'), LENGTH('Computer'), MID('Informatics',1,4);
-- DELHI     8     Info

5.3 Date Functions

SELECT YEAR('2026-06-29'), MONTHNAME('2026-06-29'), DAYNAME('2026-06-29');
-- 2026     June     Monday

5.4 DUAL & Using Functions on Columns

Functions can be applied to column values in a query:

SELECT name, UCASE(name), LENGTH(name) FROM student;
SELECT MOD(marks,10) FROM student WHERE marks > 50;

📝 Practice Question Bank — Chapter 5

📦 Chapter 6 · Aggregate Functions & GROUP BY

Aggregate (multi-row) functions take many rows and return a single summary value. GROUP BY splits rows into groups so an aggregate is computed per group; HAVING filters those groups and ORDER BY sorts the result.

6.1 The Five Aggregate Functions

SELECT MAX(marks), MIN(marks), AVG(marks), SUM(marks) FROM student;

6.2 COUNT(*) vs COUNT(column)

SELECT COUNT(*) FROM student;          -- total rows
SELECT COUNT(email) FROM student;      -- rows where email is not NULL
SELECT COUNT(DISTINCT city) FROM student;  -- distinct cities

6.3 GROUP BY

GROUP BY collects rows that share a value in a column into groups, then an aggregate is applied to each group.

SELECT city, AVG(marks)
FROM student
GROUP BY city;

6.4 HAVING vs WHERE

SELECT city, COUNT(*)
FROM student
WHERE marks >= 40          -- filter rows first
GROUP BY city
HAVING COUNT(*) > 2;       -- then filter groups

6.5 ORDER BY

ORDER BY sorts the final result — ASC (ascending, default) or DESC (descending).

SELECT name, marks FROM student ORDER BY marks DESC;

📝 Practice Question Bank — Chapter 6

🔗 Chapter 7 · Joining Two Tables (Equi-Join)

A join combines rows from two (or more) tables based on a related column. An equi-join matches rows where a common column has equal values — this is the most common way to query data spread across two tables.

7.1 Cartesian Product

If you select from two tables without a join condition, SQL returns every row of the first table paired with every row of the second — the Cartesian product (cross join). For m and n rows, the result has m × n rows (mostly meaningless).

SELECT * FROM emp, dept;     -- Cartesian product (no condition)

7.2 Equi-Join

-- emp(eid, name, deptno) and dept(deptno, dname)
SELECT name, dname
FROM emp, dept
WHERE emp.deptno = dept.deptno;

7.3 Table Aliases & Qualified Names

When both tables have a column of the same name, write table.column to remove ambiguity; aliases make queries shorter.

SELECT e.name, d.dname
FROM emp e, dept d
WHERE e.deptno = d.deptno
ORDER BY e.name;

7.4 Keys Recap

📝 Practice Question Bank — Chapter 7

🌐 Chapter 8 · Introduction to Computer Networks

A computer network is a collection of computers and devices connected to share data and resources (files, printers, internet). This chapter covers network types, devices, topologies, the Internet and the web.

8.1 Types of Network (by area covered)

8.2 Network Devices

8.3 Network Topologies

8.4 Internet, WWW, URL & Applications

8.5 Websites, Web Servers & Browsers

📝 Practice Question Bank — Chapter 8

⚖️ Chapter 9 · Societal Impacts

Technology brings benefits and responsibilities. This chapter covers how to behave ethically and stay safe online, protect creative work, and handle the environmental and health effects of technology.

9.1 Digital Footprint

9.2 Net & Communication Etiquette

A responsible internet user (netizen) follows netiquette: be respectful, no spamming/trolling, credit others' work, don't type in ALL CAPS (shouting), and protect others' privacy. Communication etiquette also covers polite, clear and honest online messages.

9.3 Data Protection & IPR

9.4 Cybercrime & Cyber Safety

Stay safe: use strong passwords + two-factor authentication, browse on HTTPS, don't click unknown links, never share OTPs, and keep software updated.

9.5 E-waste & Health Concerns

E-waste is discarded electronic devices (phones, computers, batteries) containing toxic materials. Manage it with the 3 R's — Reduce, Reuse, Recycle — through authorised recyclers, never in normal bins.

Health concerns from technology overuse include eye strain, back/neck pain, repetitive stress injury, and reduced physical activity. Remedies: correct posture, regular breaks (20-20-20 rule), good lighting and limited screen time.

📝 Practice Question Bank — Chapter 9

⌨️ Practical & Project Guide

The practical exam is 30 marks: Pandas/Matplotlib programs (8) + SQL queries (7) + Project (8) + Practical file (4) + Viva (3). Below are ready-to-use programs and project guidance.

A · Pandas / Matplotlib Programs

import pandas as pd
marks = pd.Series({'Maths':85,'Science':90,'English':78})
print(marks)                 # full Series
print("Top:", marks.max())   # highest
print("Avg:", marks.mean())  # average
print(marks[marks > 80])     # subjects above 80

import pandas as pd
data = {'Name':['Asha','Ravi','Meera'], 'Marks':[85,90,78]}
df = pd.DataFrame(data)
print(df[df['Marks'] > 80])          # toppers
df['Result'] = ['Pass','Pass','Pass'] # add column
df.to_csv('result.csv', index=False)  # export

import pandas as pd
df = pd.read_csv('students.csv')
print(df.head())            # first 5 rows
print(df.shape)             # (rows, columns)
print(df['Marks'].mean())   # average marks

import matplotlib.pyplot as plt
days = [1,2,3,4,5]; temp = [30,32,31,29,33]
plt.plot(days, temp, marker='o', label='Temp')
plt.title('Temperature'); plt.xlabel('Day'); plt.ylabel('°C')
plt.legend(); plt.savefig('temp.png'); plt.show()

import matplotlib.pyplot as plt
# Bar graph
plt.bar(['M','S','E'], [85,90,78], color='violet')
plt.title('Marks'); plt.show()
# Histogram
scores = [45,55,55,60,65,65,70,80,90]
plt.hist(scores, bins=5)
plt.title('Distribution'); plt.show()

B · SQL Query Practice

C · Project Work (8 marks)

📋 CBSE Sample Question Paper + Marking Scheme

The official CBSE Sample Question Paper for Informatics Practices (065), Class XII, Session 2025-26, reproduced with its marking scheme. Click Show Answer & Marking under any question.

import pandas as pd
ser_data = {'January':31, 'February':28, 'March':31}
s = pd.Series(ser_data)
print(s)

  StuName  Score
0   Abhay     85
1  Ananya     92
2   Javed     88

         State    Capital
0  Maharashtra     Mumbai
2       Kerala  Thiruvananthapuram

import pandas as pd, numpy as np
data = np.array([85,90,78,88])
s = pd.Series(data, index=['Mathematics','Science','English','History'])
print(s)

import pandas as pd
rec = [{'Course':'Data Science','Duration':12},
       {'Course':'Artificial Intelligence','Duration':18},
       {'Course':'Web Development','Duration':6}]
df = pd.DataFrame(rec)
print(df)

CREATE TABLE EMPLOYEES(
  EmployeeID INT PRIMARY KEY,
  EmpName VARCHAR(25),
  HireDate DATE,
  Salary_in_Lacs FLOAT(4,2));

INSERT INTO EMPLOYEES VALUES
 (101,'Ravi Kumar','2015-06-01',1.70);

import matplotlib.pyplot as plt          #Statement-1
Months = ['January','February','March','April','May']
Revenue = [50,65,75,80,95]
plt.plot(Months, Revenue, label='Revenue')  #Statement-2
plt.xlabel('Months'); plt.ylabel('Revenue')
plt.title('Monthly Revenue')            #Statement-3
plt.savefig('monthly_revenue.png')      #Statement-4
plt.legend(); plt.show()

I.   print(df.tail(3))
II.  df['Experience'] = [5, 8, 10, 6, 7]
III. df.drop(columns=['Salary'], inplace=True)
IV.  df.rename(columns={'Department':'Dept'}, inplace=True)
V.   print(df[["Name", "Salary"]])

I.   SELECT LEFT(product_code, 5) FROM Products;
II.  SELECT COUNT(Order_Id) FROM Orders;
III. SELECT YEAR(order_date) FROM Orders;
IV.  SELECT TRIM(Address) FROM Customers;
V.   SELECT DATE(NOW());

I.   SELECT LENGTH('DatabaseSystems');
II.  SELECT INSTR(Product_Name, 'a') FROM Products;
III. SELECT POWER(Tran_Amount, 2) FROM Transactions;
IV.  SELECT AVG(Salaries) FROM Employees;
V.   SELECT SUM(Salary) FROM Employees;

📝 Practice Papers + Marking Schemes

Full 37-question papers on the CBSE blueprint (Sections A–E, 70 marks), each with a complete marking scheme. Click Show Answer & Marking under any question.

import pandas as pd
d = {'Delhi':30, 'Mumbai':20, 'Chennai':10}
s = pd.Series(d)
print(s)

1    20
2    30
dtype: int64

import pandas as pd
d = {'Name':['Asha','Ravi','Meera'], 'Marks':[85,90,78]}
df = pd.DataFrame(d)
print(df[df['Marks'] > 80])

CREATE TABLE BOOK(
  BID INT PRIMARY KEY,
  Title VARCHAR(30),
  Price FLOAT);
INSERT INTO BOOK VALUES (1,'Informatics',350);

import matplotlib.pyplot as plt
sub = ['Maths','Sci','Eng']; marks = [85,90,78]
plt.bar(sub, marks, color='violet')
plt.title('Marks'); plt.xlabel('Subject'); plt.ylabel('Marks')
plt.savefig('marks.png')
plt.show()

import pandas as pd
df = pd.read_csv('data.csv')
print(df.shape)

import pandas as pd
s = pd.Series([85,70,90,60], index=['M','S','E','H'])
print(s[s >= 80])

CREATE TABLE CLUB(id INT PRIMARY KEY, name VARCHAR(20), fee INT);
INSERT INTO CLUB VALUES (1,'Chess',500);

import matplotlib.pyplot as plt
scores = [45,55,55,65,70,80,90]
plt.hist(scores, bins=5)
plt.title('Scores'); plt.xlabel('Range'); plt.ylabel('Frequency')
plt.show()

import pandas as pd
rec = [{'Course':'IP','Fee':500},{'Course':'CS','Fee':600}]
df = pd.DataFrame(rec)
print(df)

import matplotlib.pyplot as plt
m=['Jan','Feb','Mar']; r=[50,65,75]
plt.plot(m, r, label='Revenue')
plt.title('Revenue'); plt.xlabel('Month'); plt.ylabel('Lakhs')
plt.legend(); plt.savefig('rev.png'); plt.show()

import pandas as pd
df = pd.read_csv('marks.csv')
print(df[df['Marks'] > 50])

import matplotlib.pyplot as plt
city=['Delhi','Mumbai']; pop=[30,20]
plt.bar(city, pop)
plt.title('Population'); plt.xlabel('City'); plt.ylabel('Millions')
plt.savefig('pop.png'); plt.show()

import pandas as pd
df = pd.DataFrame({'A':[1,2],'B':[3,4]})
print(df.shape)   # (2, 2)

import pandas as pd
s = pd.Series([30,80,45,90])
print(s[s > 50])

import matplotlib.pyplot as plt
m=['Jan','Feb','Mar']
plt.plot(m,[40,55,60],label='Sales')
plt.plot(m,[50,50,60],label='Target')
plt.legend(); plt.title('Sales vs Target')
plt.savefig('st.png'); plt.show()

import pandas as pd
df = pd.DataFrame({'Name':['A','B','C'],'Price':[100,200,300]})
print(df['Price'].mean())   # 200.0

import matplotlib.pyplot as plt
scores=[35,40,55,60,65,70,85,90]
plt.hist(scores, bins=6)
plt.title('Scores'); plt.xlabel('Marks'); plt.ylabel('Frequency')
plt.show()

import pandas as pd
s = pd.Series({'pen':10,'book':50,'bag':300})
print(s)

import matplotlib.pyplot as plt
yr=[2022,2023,2024]; users=[10,25,40]
plt.plot(yr, users, marker='o')
plt.title('Users'); plt.xlabel('Year'); plt.ylabel('Users (lakh)')
plt.savefig('users.png'); plt.show()

import pandas as pd
df = pd.DataFrame({'Name':['A','B'],'Age':[20,16]})
print(df[df['Age'] > 18])

import matplotlib.pyplot as plt
team=['A','B','C']; score=[12,18,9]
plt.bar(team, score, color='teal')
plt.title('Scores'); plt.xlabel('Team'); plt.ylabel('Score')
plt.savefig('score.png'); plt.show()

import pandas as pd
df = pd.read_csv('emp.csv')
print(df['Salary'].mean())

import matplotlib.pyplot as plt
ages=[12,15,18,18,22,25,30]
plt.hist(ages, bins=4)
plt.title('Ages'); plt.xlabel('Age'); plt.ylabel('Count')
plt.show()

import pandas as pd
df = pd.DataFrame({'Name':['A','B'],'Marks':[85,90]})
df.to_csv('result.csv', index=False)

import matplotlib.pyplot as plt
wk=[1,2,3]; steps=[5000,7000,6500]
plt.plot(wk, steps, label='Steps')
plt.title('Activity'); plt.xlabel('Week'); plt.ylabel('Steps')
plt.legend(); plt.savefig('act.png'); plt.show()

🏆 Exam Strategy

📚 Glossary (A–Z)

Key Informatics Practices (065) terms for quick revision.