📊 RBSE Class 12 — सूचना विज्ञान (Informatics Practices)
Code 04 | सम्पूर्ण Short Notes 2026
राजस्थान माध्यमिक शिक्षा बोर्ड | NCERT Based | RBSE Code 04 | Chapter-wise | Hindi-English Bilingual
🐍 Computer Science (083) Notes → RBSE Class 12 CS Short Notes
📋 विषय-सूची (Table of Contents) — RBSE Code 04
🐍 1. Python Pandas — Series 🔥
🐍 2. Python Pandas — DataFrame 🔥
🐍 3. DataFrame Operations 🔥
📊 4. Data Visualization Basics 🔥
📈 5. Matplotlib — Line, Bar, Histogram 🔥
📈 6. Matplotlib — Pie Chart & Customization
🗄️ 7. DBMS & MySQL Basics 🔥
🗄️ 8. SQL — DDL, DML, DQL 🔥
🗄️ 9. SQL — Aggregate Functions & GROUP BY 🔥
🗄️ 10. Python–MySQL Connectivity 🔥
🌐 11. Networking Concepts 🔥
🔒 12. Cyber Security & Ethics
🐍 Python Pandas — Data Handling
NCERT: Informatics Practices | Unit 1 — Data Handling using Pandas
🐍 अध्याय 1: Python Pandas — Series Most Important
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🔹 Pandas Library
Pandas Python की एक शक्तिशाली Open-Source library है जो Data Analysis और Data Manipulation के लिए उपयोग होती है। इसे Wes McKinney ने 2008 में विकसित किया। Pandas दो मुख्य Data Structures प्रदान करता है: Series (1D — एक column) और DataFrame (2D — table/spreadsheet)।
import pandas as pd # pd = standard alias
import numpy as np # NumPy भी आवश्यक
🔹 Series — One-Dimensional Data
Series एक labeled, one-dimensional array है। इसमें index (label) और values (data) होते हैं। किसी भी data type (int, float, str) के values हो सकते हैं।
# Series बनाने के तरीके
# 1. List से
s1 = pd.Series([10, 20, 30, 40])
print(s1)
# 0 10
# 1 20 ← Default index: 0,1,2,3
# 2 30
# 3 40
# 2. Custom Index के साथ
s2 = pd.Series([85, 92, 78], index=['Ram','Sita','Mohan'])
print(s2)
# Ram 85
# Sita 92
# Mohan 78
# 3. Dictionary से (keys → index, values → data)
d = {'Hindi': 85, 'English': 92, 'Maths': 78}
s3 = pd.Series(d)
# 4. Scalar Value (सभी index पर same value)
s4 = pd.Series(5, index=['a','b','c'])
# a 5
# b 5
# c 5
# 5. NumPy Array से
arr = np.array([10, 20, 30])
s5 = pd.Series(arr)
🔹 Series — Attributes & Accessing
s = pd.Series([10,20,30,40], index=['a','b','c','d'])
# Attributes
print(s.values) # [10 20 30 40] → NumPy array
print(s.index) # Index(['a','b','c','d'])
print(s.dtype) # int64
print(s.size) # 4 (कुल elements)
print(s.shape) # (4,)
print(s.nbytes) # memory bytes
print(s.empty) # False
print(s.name) # None (name set कर सकते हैं)
# Accessing Elements
print(s['a']) # 10 (label-based)
print(s[0]) # 10 (position-based)
print(s['a':'c']) # a=10, b=20, c=30 (label slice → inclusive)
print(s[0:2]) # a=10, b=20 (position slice → exclusive)
print(s[['a','c']]) # a=10, c=30 (fancy indexing)
print(s.head(2)) # पहले 2 elements
print(s.tail(2)) # अंतिम 2 elements
🔹 Series — Operations
s = pd.Series([10, 20, 30, 40], index=['a','b','c','d'])
# Arithmetic (element-wise)
print(s + 5) # 15, 25, 35, 45
print(s * 2) # 20, 40, 60, 80
print(s ** 2) # 100, 400, 900, 1600
# Two Series (index-aligned, NaN if mismatch)
s1 = pd.Series([10, 20, 30], index=['a','b','c'])
s2 = pd.Series([1, 2, 3], index=['b','c','d'])
print(s1 + s2)
# a NaN ← 'a' s2 में नहीं
# b 21.0 ← 20+1
# c 32.0 ← 30+2
# d NaN ← 'd' s1 में नहीं
# Boolean Filtering
print(s[s > 20]) # c=30, d=40
print(s[s % 20 == 0]) # b=20, d=40
# Statistical Functions
print(s.sum()) # 100
print(s.mean()) # 25.0
print(s.max()) # 40
print(s.min()) # 10
print(s.count()) # 4 (non-NaN count)
print(s.std()) # standard deviation
print(s.median()) # 25.0
print(s.describe()) # summary statistics
🔑 परीक्षा Tips: Series = 1D labeled array | pd.Series(list/dict/scalar) | Default index: 0,1,2... | s['label'] = label-based | s[0] = position-based | Label slice → inclusive | Position slice → exclusive | NaN → mismatch index | .sum(), .mean(), .max(), .min(), .count(), .describe()
📖 NCERT Reference: Informatics Practices, Chapter 1 — Querying & Data Handling (Pandas Series)
🐍 अध्याय 2: Python Pandas — DataFrame Most Important
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🔹 DataFrame — Two-Dimensional Data
DataFrame एक 2D labeled data structure है — rows और columns दोनों में index होता है। एक spreadsheet/table जैसा। प्रत्येक column एक Series है।
# DataFrame बनाने के तरीके
# 1. Dictionary of Lists से
data = {
'Name': ['Ram', 'Sita', 'Mohan'],
'Marks': [85, 92, 78],
'City': ['Jaipur', 'Jodhpur', 'Udaipur']
}
df = pd.DataFrame(data)
print(df)
# Name Marks City
# 0 Ram 85 Jaipur
# 1 Sita 92 Jodhpur
# 2 Mohan 78 Udaipur
# 2. Custom Index
df = pd.DataFrame(data, index=['S1','S2','S3'])
# 3. List of Dictionaries
data2 = [
{'Name':'Ram', 'Marks':85},
{'Name':'Sita', 'Marks':92}
]
df2 = pd.DataFrame(data2)
# 4. Dictionary of Series
s1 = pd.Series(['Ram','Sita'], index=['a','b'])
s2 = pd.Series([85, 92], index=['a','b'])
df3 = pd.DataFrame({'Name': s1, 'Marks': s2})
# 5. CSV File से (सबसे आम)
df = pd.read_csv("students.csv")
# CSV में save करना
df.to_csv("output.csv", index=False)
🔹 DataFrame — Attributes
print(df.shape) # (3, 3) → (rows, columns)
print(df.size) # 9 (total elements)
print(df.columns) # Index(['Name','Marks','City'])
print(df.index) # RangeIndex(start=0, stop=3)
print(df.dtypes) # प्रत्येक column का data type
print(df.values) # 2D NumPy array
print(df.T) # Transpose (rows↔columns)
print(df.empty) # False
print(df.ndim) # 2
🔹 Accessing Data — loc & iloc
# Column Access
print(df['Name']) # single column → Series
print(df[['Name','Marks']]) # multiple columns → DataFrame
# Row Access
print(df.loc[0]) # label-based row
print(df.iloc[0]) # position-based row
# loc → Label-based (inclusive)
print(df.loc[0:1, 'Name':'Marks']) # rows 0-1, cols Name to Marks
# iloc → Position-based (exclusive)
print(df.iloc[0:2, 0:2]) # rows 0-1, cols 0-1
# Single Cell
print(df.loc[0, 'Name']) # 'Ram'
print(df.iloc[0, 0]) # 'Ram'
print(df.at[0, 'Name']) # 'Ram' (faster for single cell)
🔑 Key: DataFrame = 2D (Table) | dict of lists → सबसे common | read_csv() → CSV load | to_csv() → save | df['col'] → column | loc → label (inclusive) | iloc → position (exclusive) | .shape → (rows, cols) | .T → transpose | .columns → column names
📖 NCERT Reference: Informatics Practices, Chapter 1 — DataFrame Basics
🐍 अध्याय 3: DataFrame Operations (संक्रियाएँ) Most Important
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🔹 Adding / Deleting Columns & Rows
# Column जोड़ना
df['Grade'] = ['A', 'A+', 'B'] # new column
df['Total'] = df['Marks'] + 10 # computed column
# Column हटाना
df = df.drop('Grade', axis=1) # axis=1 → column
del df['Total'] # alternative
# Row जोड़ना (append deprecated → use concat)
new_row = pd.DataFrame({'Name':['Geeta'],'Marks':[88],'City':['Kota']})
df = pd.concat([df, new_row], ignore_index=True)
# Row हटाना
df = df.drop(0, axis=0) # axis=0 → row (by index)
df = df.drop([1, 2], axis=0) # multiple rows
🔹 Boolean Indexing / Filtering
# शर्त के आधार पर data निकालना
print(df[df['Marks'] > 80]) # marks > 80
print(df[df['City'] == 'Jaipur']) # city = Jaipur
print(df[(df['Marks']>80) & (df['City']=='Jaipur')]) # AND
print(df[(df['Marks']>90) | (df['City']=='Jodhpur')]) # OR
🔹 Statistical Methods
print(df['Marks'].sum()) # कुल
print(df['Marks'].mean()) # औसत
print(df['Marks'].max()) # अधिकतम
print(df['Marks'].min()) # न्यूनतम
print(df['Marks'].median()) # मध्यिका
print(df['Marks'].mode()) # बहुलक
print(df['Marks'].std()) # मानक विचलन
print(df['Marks'].var()) # प्रसरण (variance)
print(df['Marks'].count()) # गणना (non-NaN)
print(df['Marks'].quantile(0.25)) # Q1
print(df.describe()) # सम्पूर्ण सांख्यिकी सारांश
🔹 Sorting & Other Operations
# Sorting
df.sort_values('Marks', ascending=False) # marks descending
df.sort_values(['City','Marks']) # multi-column sort
df.sort_index() # index से sort
# Rename
df.rename(columns={'Marks':'Score'}, inplace=True)
# Missing Values (NaN)
df.isnull() # NaN कहाँ-कहाँ है → True/False
df.isnull().sum() # प्रत्येक column में कितने NaN
df.dropna() # NaN वाली rows हटाओ
df.fillna(0) # NaN को 0 से भरो
df.fillna(df.mean()) # NaN को mean से भरो
# Iteration
for index, row in df.iterrows():
print(index, row['Name'], row['Marks'])
🔹 Pivot Table & GroupBy
# GroupBy — SQL जैसा
grouped = df.groupby('City')
print(grouped['Marks'].mean()) # city-wise average marks
print(grouped['Marks'].sum()) # city-wise total
print(grouped.count()) # city-wise count
# Pivot Table
pd.pivot_table(df, values='Marks', index='City', aggfunc='mean')
🔑 Key: drop(axis=1)→column हटाओ | drop(axis=0)→row हटाओ | df[df['col']>val] → filter | describe()→सम्पूर्ण stats | sort_values()→sort | isnull()→NaN check | dropna()→NaN rows हटाओ | fillna()→NaN भरो | groupby()→group-wise stats | read_csv()/to_csv()→CSV
📖 NCERT Reference: Informatics Practices, Chapter 1 — DataFrame Operations
📊 Data Visualization — Matplotlib
NCERT: Informatics Practices | Unit 2 — Data Visualization
📊 अध्याय 4: Data Visualization Basics (डाटा दृश्यीकरण) Most Important
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🔹 Data Visualization क्या है?
डाटा को चित्रात्मक/ग्राफ़िकल रूप (Charts, Graphs, Maps) में प्रदर्शित करना। उद्देश्य: डाटा को समझना आसान, patterns और trends पहचानना, निर्णय लेना। Python में Matplotlib library सबसे लोकप्रिय है।
🔹 Chart Types — कब क्या उपयोग करें?
| Chart | उपयोग | Matplotlib Function |
|---|---|---|
| Line Chart | समय के साथ बदलाव (Trends) — तापमान, शेयर बाज़ार, जनसंख्या | plt.plot() |
| Bar Chart | श्रेणियों की तुलना — विषयवार अंक, शहरवार जनसंख्या | plt.bar() / plt.barh() |
| Histogram | Frequency Distribution — अंकों का वितरण, आयु वर्ग | plt.hist() |
| Pie Chart | प्रतिशत/अनुपात दिखाना — बजट, मार्केट शेयर | plt.pie() |
🔹 Matplotlib — Setup
import matplotlib.pyplot as plt
# Basic Plot
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.xlabel("X-axis") # X-axis label
plt.ylabel("Y-axis") # Y-axis label
plt.title("My First Plot") # Title
plt.show() # Display
🔑 Key: import matplotlib.pyplot as plt | Line→Trends | Bar→तुलना | Histogram→Frequency | Pie→प्रतिशत | plt.show()→display | plt.xlabel/ylabel/title→labels
📖 NCERT Reference: Informatics Practices, Chapter 2 — Data Visualization
📈 अध्याय 5: Matplotlib — Line, Bar, Histogram Most Important
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🔹 Line Chart — plt.plot()
import matplotlib.pyplot as plt
# Simple Line Chart
months = ['Jan','Feb','Mar','Apr','May']
sales = [100, 150, 130, 180, 200]
plt.plot(months, sales, color='blue', marker='o',
linestyle='--', linewidth=2, markersize=8,
label='Sales 2026')
plt.xlabel("Month")
plt.ylabel("Sales (₹ Lakhs)")
plt.title("Monthly Sales Report")
plt.legend() # label दिखाता है
plt.grid(True) # grid lines
plt.show()
# Multiple Lines
plt.plot(months, sales, 'b-o', label='2026')
plt.plot(months, [80,120,110,160,190], 'r--s', label='2025')
plt.legend()
plt.show()
🔹 Line Style & Markers
| Parameter | Values |
|---|---|
| color | 'r'(red), 'b'(blue), 'g'(green), 'k'(black), 'y'(yellow), 'm'(magenta), '#FF5733'(hex) |
| linestyle | '-'(solid), '--'(dashed), '-.'(dash-dot), ':'(dotted) |
| marker | 'o'(circle), 's'(square), '^'(triangle), 'D'(diamond), '*'(star), '+'(plus) |
| linewidth | 1, 2, 3... (मोटाई) |
🔹 Bar Chart — plt.bar() / plt.barh()
# Vertical Bar Chart
subjects = ['Hindi','English','Maths','Science','SST']
marks = [85, 92, 78, 88, 75]
plt.bar(subjects, marks, color=['red','blue','green','orange','purple'],
width=0.5, edgecolor='black')
plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.title("Subject-wise Marks")
plt.show()
# Horizontal Bar Chart
plt.barh(subjects, marks, color='skyblue')
plt.xlabel("Marks")
plt.title("Marks (Horizontal)")
plt.show()
🔹 Histogram — plt.hist()
# Histogram — Frequency Distribution
marks = [45,55,60,65,70,72,75,78,80,82,85,88,90,92,95]
plt.hist(marks, bins=5, color='steelblue',
edgecolor='black', alpha=0.7)
# bins = कितने groups (bars) बनाने हैं
# alpha = transparency (0 to 1)
plt.xlabel("Marks Range")
plt.ylabel("Frequency (Students)")
plt.title("Marks Distribution")
plt.show()
# bins=[0,40,60,80,100] → custom ranges
Bar Chart vs Histogram: Bar Chart → Categories (discrete) की तुलना, bars के बीच gap। Histogram → Continuous data का frequency distribution, bars सटे हुए (no gap)।
🔑 Key: plt.plot()→Line | plt.bar()→Vertical Bar | plt.barh()→Horizontal | plt.hist()→Histogram | color, marker, linestyle→customization | bins→histogram groups | legend()→label box | grid(True)→grid | Bar = categories (gap) | Histogram = continuous (no gap)
📖 NCERT Reference: Informatics Practices, Chapter 2 — Line, Bar & Histogram Charts
📈 अध्याय 6: Matplotlib — Pie Chart & Customization
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🔹 Pie Chart — plt.pie()
# Pie Chart — अनुपात / प्रतिशत
subjects = ['Hindi','English','Maths','Science','SST']
marks = [85, 92, 78, 88, 75]
plt.pie(marks, labels=subjects,
autopct='%1.1f%%', # प्रतिशत दिखाना
startangle=90, # शुरुआत का कोण
colors=['red','blue','green','orange','purple'],
explode=[0, 0.1, 0, 0, 0], # Sita slice बाहर निकालना
shadow=True)
plt.title("Subject-wise Marks Distribution")
plt.legend(loc='lower right')
plt.show()
🔹 Saving Charts
# Chart को file में save करना
plt.plot([1,2,3], [10,20,30])
plt.title("Saved Chart")
plt.savefig("chart.png", dpi=300, bbox_inches='tight')
# dpi = resolution (dots per inch)
# bbox_inches='tight' → extra whitespace हटाता
plt.savefig("chart.pdf") # PDF format
plt.savefig("chart.jpg") # JPG format
🔹 Subplot — Multiple Charts in One Figure
# 1 row, 2 columns → 2 charts side by side
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
ax1.plot([1,2,3], [10,20,30], 'b-o')
ax1.set_title("Line Chart")
ax1.set_xlabel("X")
ax2.bar(['A','B','C'], [5,8,3], color='green')
ax2.set_title("Bar Chart")
plt.tight_layout() # overlap न हो
plt.show()
🔹 Chart Customization Summary
| Function | कार्य |
|---|---|
| plt.xlabel("text") | X-axis label |
| plt.ylabel("text") | Y-axis label |
| plt.title("text") | Chart title |
| plt.legend() | Legend box दिखाना |
| plt.grid(True) | Grid lines |
| plt.xticks(rotation=45) | X labels rotate |
| plt.ylim(0, 100) | Y-axis range |
| plt.figsize=(10,6) | Figure size |
| plt.savefig("file.png") | Save to file |
| plt.show() | Display chart |
🔑 Key: plt.pie() → labels, autopct='%1.1f%%', explode, shadow | savefig()→save | subplots()→multiple charts | tight_layout()→no overlap | dpi=300→high quality | figsize=(w,h)→size
📖 NCERT Reference: Informatics Practices, Chapter 2 — Pie Chart & Customization
🗄️ Database & SQL
NCERT: Informatics Practices | Unit 3 — Database Management
🗄️ अध्याय 7: DBMS & MySQL Basics Most Important
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🔹 Database & DBMS
Database: संबंधित डाटा का व्यवस्थित संग्रह। DBMS: डाटाबेस बनाने, प्रबंधित और access करने का सॉफ्टवेयर। RDBMS: Relational DBMS — डाटा Tables (Relations) में संग्रहित। उदाहरण: MySQL (Open Source), Oracle, PostgreSQL, MS SQL Server।
🔹 RDBMS Key Terms
| Term | अर्थ | उदाहरण |
|---|---|---|
| Relation/Table | Rows + Columns का संग्रह | STUDENT table |
| Tuple/Record/Row | एक पंक्ति (एक student) | (101, Ram, 85) |
| Attribute/Column/Field | एक गुण (property) | Name, Marks |
| Degree | Columns की संख्या | 4 columns → Degree=4 |
| Cardinality | Rows की संख्या | 3 rows → Cardinality=3 |
| Primary Key | Unique + NOT NULL identifier | Roll_No |
| Foreign Key | दूसरी table की PK refer करता | Dept_ID |
| Candidate Key | PK बन सकने वाले सभी columns | Roll_No, Aadhar |
🔹 MySQL Data Types
| Category | Types | उदाहरण |
|---|---|---|
| Numeric | INT, FLOAT, DOUBLE, DECIMAL | INT → 85, FLOAT → 85.5 |
| String | CHAR(n), VARCHAR(n), TEXT | CHAR(10) → fixed, VARCHAR(50) → variable |
| Date/Time | DATE, TIME, DATETIME, YEAR | DATE → '2026-02-16' |
🔑 Key: RDBMS → Tables | Degree → Columns | Cardinality → Rows | Primary Key → Unique+NOT NULL | Foreign Key → दूसरी table refer | CHAR → fixed length | VARCHAR → variable length | MySQL → Open Source
📖 NCERT Reference: Informatics Practices, Chapter 3 — Database Concepts
🗄️ अध्याय 8: SQL — DDL, DML, DQL Commands Most Important
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🔹 SQL Categories
DDL: CREATE, ALTER, DROP — संरचना। DML: INSERT, UPDATE, DELETE — डाटा बदलना। DQL: SELECT — डाटा पढ़ना।
🔹 DDL — CREATE, ALTER, DROP
CREATE DATABASE school;
USE school;
CREATE TABLE student (
roll_no INT PRIMARY KEY,
name VARCHAR(50) NOT NULL,
marks FLOAT,
city VARCHAR(30) DEFAULT 'Jaipur',
dob DATE
);
ALTER TABLE student ADD phone VARCHAR(15);
ALTER TABLE student DROP COLUMN phone;
ALTER TABLE student MODIFY marks INT;
DROP TABLE student;
🔹 DML — INSERT, UPDATE, DELETE
INSERT INTO student VALUES(101,'Ram',85.5,'Jaipur','2008-05-10');
INSERT INTO student(roll_no,name,marks) VALUES(102,'Sita',92);
UPDATE student SET marks=90 WHERE roll_no=101;
UPDATE student SET city='Jodhpur' WHERE name='Ram';
DELETE FROM student WHERE roll_no=103;
DELETE FROM student WHERE marks < 33; -- fail students
🔹 DQL — SELECT Queries
SELECT * FROM student;
SELECT name, marks FROM student;
SELECT DISTINCT city FROM student;
-- WHERE conditions
SELECT * FROM student WHERE marks > 80;
SELECT * FROM student WHERE city='Jaipur' AND marks>75;
SELECT * FROM student WHERE city='Jaipur' OR city='Jodhpur';
SELECT * FROM student WHERE NOT city='Kota';
-- BETWEEN, IN, LIKE, IS NULL
SELECT * FROM student WHERE marks BETWEEN 70 AND 90;
SELECT * FROM student WHERE city IN ('Jaipur','Jodhpur','Kota');
SELECT * FROM student WHERE name LIKE 'R%'; -- R से शुरू
SELECT * FROM student WHERE name LIKE '%am'; -- am पर अंत
SELECT * FROM student WHERE name LIKE '_i%'; -- दूसरा अक्षर i
SELECT * FROM student WHERE phone IS NULL;
-- ORDER BY
SELECT * FROM student ORDER BY marks DESC;
SELECT * FROM student ORDER BY city ASC, marks DESC;
🔑 Key: DDL→संरचना (CREATE/ALTER/DROP) | DML→डाटा (INSERT/UPDATE/DELETE) | DQL→पढ़ना (SELECT) | WHERE→शर्त | LIKE→pattern (% = कोई भी, _ = एक char) | BETWEEN→सीमा | IN→सूची | IS NULL→खाली | ORDER BY→क्रम (ASC/DESC)
📖 NCERT Reference: Informatics Practices, Chapter 4 — SQL Commands
🗄️ अध्याय 9: SQL — Aggregate Functions & GROUP BY Most Important
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🔹 Aggregate Functions
SELECT COUNT(*) FROM student; -- कुल rows
SELECT COUNT(city) FROM student; -- NULL नहीं गिनता
SELECT SUM(marks) FROM student; -- जोड़
SELECT AVG(marks) FROM student; -- औसत
SELECT MAX(marks) FROM student; -- अधिकतम
SELECT MIN(marks) FROM student; -- न्यूनतम
-- Alias
SELECT AVG(marks) AS average_marks FROM student;
🔹 GROUP BY & HAVING
-- City-wise average marks
SELECT city, AVG(marks) FROM student GROUP BY city;
-- City-wise count
SELECT city, COUNT(*) AS total FROM student GROUP BY city;
-- HAVING (group पर शर्त — WHERE group पर काम नहीं करता)
SELECT city, AVG(marks) AS avg_m
FROM student
GROUP BY city
HAVING AVG(marks) > 80;
-- WHERE vs HAVING
-- WHERE → row-level filter (GROUP BY से पहले)
-- HAVING → group-level filter (GROUP BY के बाद)
🔹 String & Math Functions
-- String Functions
SELECT UPPER('hello'); -- HELLO
SELECT LOWER('HELLO'); -- hello
SELECT LENGTH('Ram'); -- 3
SELECT LEFT('Python', 3); -- Pyt
SELECT RIGHT('Python', 3); -- hon
SELECT SUBSTRING('Python',2,3); -- yth (position 2 से 3 chars)
SELECT CONCAT('Ram',' ','Kumar'); -- Ram Kumar
SELECT TRIM(' Ram '); -- Ram
SELECT INSTR('Python','th'); -- 3
-- Math Functions
SELECT ROUND(3.567, 1); -- 3.6
SELECT ROUND(3.567); -- 4
SELECT MOD(10, 3); -- 1 (शेषफल)
SELECT POWER(2, 3); -- 8
SELECT ABS(-5); -- 5
-- Date Functions
SELECT NOW(); -- current datetime
SELECT CURDATE(); -- current date
SELECT YEAR('2026-02-16'); -- 2026
SELECT MONTH('2026-02-16'); -- 2
SELECT DAY('2026-02-16'); -- 16
SELECT DAYNAME('2026-02-16');-- Monday
🔑 Key: COUNT(*)→सभी rows | COUNT(col)→NULL नहीं | SUM/AVG/MAX/MIN | GROUP BY→समूह | HAVING→group शर्त | WHERE→row शर्त | UPPER/LOWER/LENGTH/SUBSTRING/CONCAT/TRIM | ROUND/MOD/ABS/POWER | NOW/CURDATE/YEAR/MONTH/DAY
📖 NCERT Reference: Informatics Practices, Chapter 4 — Aggregate Functions
🗄️ अध्याय 10: Python–MySQL Connectivity Most Important
▼
🔹 Steps: Connect → Cursor → Execute → Fetch/Commit → Close
import mysql.connector
# Step 1: Connection
conn = mysql.connector.connect(
host="localhost",
user="root",
password="1234",
database="school"
)
if conn.is_connected():
print("Connected!")
# Step 2: Cursor
cursor = conn.cursor()
# Step 3 & 4: SELECT (Read)
cursor.execute("SELECT * FROM student WHERE marks > 80")
rows = cursor.fetchall() # list of tuples
for row in rows:
print(row)
# fetchone() → एक row
# fetchmany(3) → 3 rows
# fetchall() → सभी rows
# Step 3 & 4: INSERT (Write)
sql = "INSERT INTO student VALUES(%s, %s, %s, %s)"
val = (104, "Geeta", 88, "Kota")
cursor.execute(sql, val)
conn.commit() # ← DML (INSERT/UPDATE/DELETE) में ज़रूरी!
# Multiple INSERT
records = [(105,"Hari",75,"Jaipur"), (106,"Priya",91,"Udaipur")]
cursor.executemany(sql, records)
conn.commit()
# UPDATE
cursor.execute("UPDATE student SET marks=95 WHERE roll_no=102")
conn.commit()
print(cursor.rowcount, "record(s) updated")
# DELETE
cursor.execute("DELETE FROM student WHERE marks < 33")
conn.commit()
# Step 5: Close
cursor.close()
conn.close()
🔹 Dynamic Input Example
# User से input लेकर search करना
roll = int(input("Enter Roll No: "))
cursor.execute("SELECT * FROM student WHERE roll_no = %s", (roll,))
result = cursor.fetchone()
if result:
print("Name:", result[1], "Marks:", result[2])
else:
print("Record not found!")
# Create table from Python
cursor.execute("""CREATE TABLE IF NOT EXISTS marks (
subject VARCHAR(30),
score INT
)""")
conn.commit()
🔑 Key: import mysql.connector | connect(host, user, password, database) | cursor() → execute() → fetch/commit | fetchall()→सभी | fetchone()→एक | commit()→DML में ज़रूरी | %s→placeholder | executemany()→multiple rows | rowcount→affected rows | close()→अंत में
📖 NCERT Reference: Informatics Practices, Chapter 5 — Python-MySQL Interface
🌐🔒 Networking & Cyber Security
NCERT: Informatics Practices | Unit 4 — Society, Law & Ethics
🌐 अध्याय 11: Networking Concepts (नेटवर्किंग) Most Important
▼
🔹 Network Types
PAN: व्यक्तिगत (Bluetooth, USB, 10m)। LAN: भवन/कैम्पस (स्कूल, ऑफिस)। MAN: शहर (Cable TV)। WAN: देश/विश्व (Internet = सबसे बड़ा WAN)।
🔹 Network Topology
Bus: एक केबल, सस्ता, केबल खराब→सब बंद। Star: Hub/Switch से जुड़े, सबसे लोकप्रिय, 1 खराब→बाकी चालू। Ring: वृत्ताकार, no collision। Mesh: सब-से-सब, सबसे विश्वसनीय, महँगा। Tree: Star+Bus hierarchical।
🔹 Transmission Media
Wired: Twisted Pair (LAN, 100m, सस्ता), Coaxial (Cable TV), Fiber Optic (सबसे तेज़, light, EMI-proof, महँगा)। Wireless: Radio (Wi-Fi), Microwave (towers), Infrared (remote), Satellite (VSAT, GPS)।
🔹 Network Devices
Modem: Digital↔Analog। Hub: Broadcast (unintelligent)। Switch: Destination only (intelligent)। Router: Networks जोड़ता (IP routing)। Gateway: अलग protocols जोड़ता। Repeater: Signal amplify।
🔹 Protocols & Internet
TCP/IP: Internet foundation। HTTP (80)/HTTPS (443): Web। FTP (21): File transfer। SMTP (25): Email send। POP3/IMAP: Email receive। DNS: Domain→IP। IPv4: 32-bit (192.168.1.1)। IPv6: 128-bit (hexadecimal)। URL: Protocol+Domain+Path। WWW: Tim Berners-Lee, 1989। भारत में Internet: 15 Aug 1995, VSNL।
🔑 Key: LAN→भवन | WAN→विश्व | Star→popular | Mesh→reliable | Fiber→fastest | Hub→broadcast | Switch→intelligent | Router→network connect | TCP/IP→Internet | HTTP→80 | HTTPS→443 | FTP→21 | SMTP→25 | DNS→name→IP | IPv4→32bit | IPv6→128bit
📖 NCERT Reference: Informatics Practices, Chapter 6 — Computer Networks
🔒 अध्याय 12: Cyber Security & Ethics (साइबर सुरक्षा)
▼
🔹 Cyber Threats (साइबर खतरे)
Virus: Host program से जुड़कर फैलता। Worm: बिना host, network से फैलता। Trojan: उपयोगी दिखता, अंदर malicious। Spyware: गुप्त जासूसी। Ransomware: Data encrypt→फिरौती। Adware: अनचाहे विज्ञापन। Keylogger: Keyboard strokes record।
🔹 Cyber Crimes
Hacking: अनधिकृत access। Phishing: नकली site/email→data चोरी। Cyberstalking: Online पीछा/धमकी। Identity Theft: पहचान चुराना। Cyberbullying: Online उत्पीड़न। Denial of Service (DoS): Server को overload करना।
🔹 सुरक्षा उपाय
Firewall: Network traffic filter। Antivirus: Malware detect/remove। Encryption: Data को coded form में (HTTPS/SSL)। Strong Passwords: 8+ chars, mixed। 2FA: Password + OTP। Regular Updates और Backup अनिवार्य।
🔹 IT Act 2000 & Ethics
IT Act 2000 (2008 संशोधित) — भारत का पहला साइबर कानून। Digital signatures को मान्यता। CERT-In: Indian Computer Emergency Response Team। Digital Footprint: Online गतिविधियों का रिकॉर्ड। IPR: Copyright, Patent, Trademark। Plagiarism: चोरी — अनैतिक। Open Source: Linux, Python, LibreOffice — स्वतंत्र। Creative Commons (CC): कुछ अधिकारों के साथ sharing। Free Software Foundation: Richard Stallman। E-Waste: पुराने electronics का safe disposal ज़रूरी।
🔑 Key: Virus→host | Worm→अकेले | Trojan→छिपा | Ransomware→फिरौती | Phishing→नकली site | Firewall→filter | Encryption→coded | IT Act→2000 | 2FA→Password+OTP | CERT-In→nodal | Open Source→free (Linux, Python) | IPR→Copyright/Patent
📖 NCERT Reference: Informatics Practices, Chapter 7 — Societal Impact & Ethics
❓ अक्सर पूछे जाने वाले प्रश्न (FAQ)
RBSE Code 04 में कौन-कौन से topics हैं? ▼
Informatics Practices (Code 04) में: Python Pandas (Series & DataFrame), Data Visualization (Matplotlib — Line, Bar, Histogram, Pie), SQL (DDL, DML, DQL, Functions, GROUP BY), Python-MySQL Connectivity, Networking Concepts, Cyber Security — कुल 12 अध्याय।
Computer Science (083) और Informatics Practices (04) में क्या अंतर है? ▼
CS में Python advanced (Functions, Recursion, File Handling, Stack, Boolean Algebra) है, जबकि IP में Pandas (Data Analysis) और Matplotlib (Visualization) मुख्य हैं। SQL और Networking दोनों में समान हैं। IP में Boolean Algebra और Stack नहीं है।
PDF कैसे डाउनलोड करें? ▼
'PDF Download करें' बटन क्लिक → नई विंडो → Ctrl+P → 'Save as PDF' चुनें।
Board Exam में Pandas code कैसे लिखें? ▼
import pandas as pd लिखना न भूलें। Code में proper indentation रखें। Output भी लिखें — DataFrame/Series का output tabular form में दिखाएँ। Comments (#) लगाएँ।
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