🐍 Python For Cybersecurity

1.1 What is Python? (Beginner-friendly explanation)

Python is a high-level, simple, and powerful programming language used for web development, automation, AI, cybersecurity, data science, and more.

📌 Key Points

• ✔ Beginner-friendly & readable syntax
• ✔ Works on Windows, Linux, and Mac
• ✔ Huge community & libraries
• ✔ Used in AI, ML, Cybersecurity, Web Dev, Automation

🆚 Python vs Other Languages (Quick Comparison)

1.2 Features of Python (Simple, Powerful & Flexible)

Python is popular because it is flexible and easy to use. Let’s understand Python’s main features.

1. Readable & Simple Syntax

   Python looks like English → easy to learn.

2. Platform Independent

   Works on Windows, Mac, Linux without changes.

3. Huge Libraries

   AI, ML, Automation, Web → all possible with ready-made libraries.

4. Object-Oriented

   Supports classes, objects, inheritance, etc.

5. Open Source

   Free to use forever!

1.3 How to Install Python & Write Your First Program

🖥️ Step 1: Download Python

Go to python.org → Download → Install.

🛠️ Step 2: Verify Installation

python --version

🐍 Step 3: Write Your First Python Program

print("Hello, Python!")

1.4 Python IDEs (Which one should beginners use?)

Python can be written in many editors. Here are the best ones:

1.5 Real-World Uses of Python (From Web Dev to AI)

Python is everywhere. Here are the most popular real-world uses:

• 🤖 Artificial Intelligence
• 📊 Data Science & Data Analysis
• 🕸 Web Development (Django, Flask)
• 🔐 Cybersecurity & Automation
• ⚙️ DevOps & Scripting
• 📱 App Development
• 🧪 Machine Learning

2.1 Python vs C – Speed, Purpose & Code Simplicity

C is a **low-level, powerful, fast language**, while Python is **high-level and easy**. Both are useful but built for different purposes.

📌 Key Differences

🧪 Example Comparison

Python:

C:

2.2 Python vs C++ — Object-Oriented & Performance Differences

C++ is powerful for games and high-performance apps, whereas Python is great for rapid development.

📌 Comparison Table

2.3 Python vs Java – Ease of Use & Application Areas

Java is widely used in enterprise development, while Python dominates AI and automation.

🔍 Quick Comparison

2.4 Python vs JavaScript – Web, Backend & Scripting

Python and JavaScript are both powerful but built for different environments.

📌 Comparison

2.5 Which Language Should You Learn First?

If you are a beginner, Python is the best starting point. It builds strong logic skills and is used almost everywhere.

🎓 Recommended Learning Order

• 1️⃣ Start with Python → Easy & powerful
• 2️⃣ Learn JavaScript → Web development
• 3️⃣ Learn C/C++ → Improve speed & logic

3.1 What is a Variable?

A variable is like a container where Python stores data. You give the container a name, and Python stores a value inside it.

📌 Real-Life Example

• A water bottle → variable
• Water inside → value
• Changing water → assigning new value

3.2 Rules & Naming Conventions

Variable names must follow Python rules. Here's the simplest explanation:

✔ Allowed

• Starts with a letter → name
• Can contain numbers → age1
• Can contain underscore → user_name

❌ Not Allowed

• Starting with a number → 1name
• Using spaces → user name
• Using symbols → user$name

3.3 Basic Data Types with Real-Life Examples

Python has several built-in data types. Here are the most common ones:

3.4 Mutable vs Immutable (Very Simple Explanation)

"Mutable" means can be changed, "Immutable" means cannot be changed.

📌 Comparison Table

3.5 How to Check Data Types using type()

Python provides a built-in function type() that tells you the data type of any variable.

📘 Output

4.1 Arithmetic Operators (Daily-Life Examples)

Arithmetic operators help us perform mathematical operations like addition, subtraction, etc.

4.2 Assignment Operators Explained Simply

Assignment operators help you store values in variables or update existing ones.

4.3 Comparison Operators (==, >, <, !=)

These operators compare two values and return True or False.

4.4 Logical Operators (and, or, not)

Used to combine multiple conditions.

4.5 Bitwise & Ternary Operators (Simple Explanation)

🔹 Ternary Operator

Shortcut for writing if-else in one line.

🔹 Bitwise Operators (Easy Table)

5.1 Understanding the If Condition (Beginner-Friendly)

The if statement checks a condition. If the condition is True, the code inside it will run.

temperature = 32
if temperature > 30:
    print("It's hot today!")
                             

5.2 If-Else with Real Examples (Age, Login, etc.)

The else block runs when the if condition is False.

🔹 Example 1: Age Check

age = 17
if age >= 18:
    print("You are an adult")
else:
    print("You are a minor")
                             

🔹 Example 2: Simple Login

username = "admin"
password = "1234"

if username == "admin" and password == "1234":
    print("Login successful!")
else:
    print("Invalid username or password")
                             

5.3 Nested Conditions (Explained Slowly)

Nested conditions mean using an if inside another if.

age = 20
citizen = True

if age >= 18:
    if citizen:
        print("Eligible to vote")
    else:
        print("You must be a citizen")
else:
    print("You must be 18 or older")
                             

5.4 Elif Statements (Multiple Conditions)

elif allows you to check multiple conditions one by one.

🔹 Example: Student Grade System

marks = 85

if marks >= 90:
    print("Grade A")
elif marks >= 75:
    print("Grade B")
elif marks >= 60:
    print("Grade C")
else:
    print("Grade D")
                             

5.5 Short-Hand If & Ternary Usage

Python allows short versions of if-else statements.

🔹 One-line If

if 5 > 2: print("5 is greater")
                             

🔹 Ternary Operator (One-line If-Else)

age = 18
status = "Adult" if age >= 18 else "Minor"
print(status)
                             

6.1 While Loop – Step-by-Step Explanation

The while loop repeats a block of code as long as a condition is True. Use it when the number of iterations is not known in advance.

i = 1
while i <= 5:
    print(i)
    i += 1
                             

6.2 For Loop – Iterating Easily

The for loop iterates over items of a sequence (list, tuple, string, range, etc.). Use it when you know the collection to iterate.

fruits = ["apple", "banana", "mango"]
for fruit in fruits:
    print(fruit)
                             

🔹 Looping with range()

# print numbers 0 to 4
for i in range(5):
    print(i)

# start and stop
for i in range(1, 6):  # 1..5
    print(i)

# step
for i in range(1, 10, 2):  # 1,3,5,7,9
    print(i)
                             

6.3 Loop with else (Hidden Python Feature)

Both for and while support an else block that runs when the loop completes normally (no break).

items = [2, 4, 6, 8]
target = 5

for x in items:
    if x == target:
        print("Found")
        break
else:
    print("Not found")
                             

6.4 Nested Loops (Loops inside Loops)

You can put a loop inside another loop. This is useful for tables, matrices, or combinations.

for i in range(1, 4):        # rows
    for j in range(1, 4):    # columns
        print(i * j, end=" ")
    print()  # new line after each row
                             

1 2 3
2 4 6
3 6 9
                                 

6.5 Understanding range() Function

range() generates a sequence of numbers. It's commonly used with for loops.

7.1 break Statement – Stop the Loop Immediately

The break statement is used to exit a loop instantly, even if the loop condition is still True.

🔹 Example: Stop when number found

numbers = [1, 3, 5, 7, 9]

for n in numbers:
    if n == 5:
        print("Found 5!")
        break
    print("Checking:", n)
                             

7.2 continue Statement – Skip to Next Iteration

The continue statement skips the current loop step and jumps to the next iteration.

🔹 Example: Skip even numbers

for i in range(1, 8):
    if i % 2 == 0:
        continue   # skip even numbers
    print("Odd number:", i)
                             

7.3 pass Statement – Do Nothing Placeholder

The pass statement literally does nothing. It is used when a statement is required syntactically, but you don’t want to write code yet.

🔹 Example: Empty function

def future_feature():
    pass  # function will be implemented later
                             

🔹 Example: Loop placeholder

for i in range(5):
    pass   # do nothing but loop will run
                             

7.4 Real-Life Loop Control Examples

Let's understand all three control statements with real-life-inspired examples.

🔹 Example: Password attempt system

password = "1234"

for attempt in ["1111", "2222", "1234", "9999"]:
    if attempt == "":
        continue   # skip if input is empty

    if attempt == password:
        print("Access granted")
        break
    else:
        print("Wrong password")
else:
    print("All attempts failed")
                             

7.5 When & Why to Use These Statements

8.1 Implicit Casting – Python Converts Automatically

Python automatically converts smaller data types into larger compatible types. This is known as implicit type conversion.

a = 5        # int
b = 3.2      # float

result = a + b
print(result)
print(type(result))
                             

8.2 Explicit Casting – Using int(), float(), str()

Explicit casting means manually converting a value into another type using functions like:

🔹 Example: Converting a float to int

value = 9.7
print(int(value))
                             

8.3 Casting Functions with Simple Examples

Here are quick examples that explain casting clearly.

🔹 Convert string → int

num = "25"
print(int(num) + 5)   # output → 30
                             

🔹 Convert int → float

a = 10
print(float(a))   # output → 10.0
                             

🔹 Convert number → string

value = 50
print("Value is " + str(value))
                             

8.4 String to Number Conversion

You can convert numerical strings into int or float easily — but only if the string contains valid digits.

🔹 Valid Conversion

num1 = "100"
print(int(num1))   # 100
                             

🔹 Decimal string → float

num2 = "12.55"
print(float(num2))   # 12.55
                             

8.5 Common Casting Mistakes Beginners Make

• ❌ Trying to convert non-numeric strings to int/float
• ❌ Forgetting to use str() when joining numbers with text
• ❌ Assuming int() rounds numbers (it truncates instead)
• ❌ Casting without checking user input format

9.1 Integer – Whole Numbers

Integers (int) are whole numbers without decimals. They can be positive, negative, or zero.

a = 10
b = -5
c = 0

print(type(a))   # 
                             

✔ Why int is useful?

• 📌 Used for counting (students, items, loops)
• 📌 No size limit (Python auto handles big numbers)
• 📌 Best for whole number calculations

9.2 Float – Decimal Numbers

A float represents numbers with decimal points.

x = 10.5
y = -3.14

print(type(x))   # 
                             

✔ Why float is used?

• 📌 Billing calculations (e.g., price * quantity)
• 📌 Scientific values (speed, weight, temperature)
• 📌 Accurate mathematical operations

result = 5 + 2.5
print(result)        # 7.5
print(type(result))  # float
                             

9.3 Complex Numbers – Real + Imaginary (a + bj)

Python supports complex numbers using the format: a + bj (where j represents √(-1), the imaginary part).

z = 5 + 3j
print(z.real)    # 5.0
print(z.imag)    # 3.0
print(type(z))   # 
                             

✔ Where are complex numbers used?

• 📡 Signal Processing
• 🔊 Audio & Sound Analysis
• ⚡ Electrical Engineering
• 🤖 Machine Learning (advanced)

9.4 Useful Number Functions in Python

Python provides built-in number functions for operations:

Example:

print(abs(-9))        # 9
print(round(4.67))    # 5
print(pow(3, 2))      # 9
                             

9.5 Math Module – Easy Mathematical Operations

Python’s math module provides advanced mathematical functions.

import math

print(math.sqrt(16))      # 4.0
print(math.factorial(5))  # 120
print(math.pi)            # 3.14159265
                             

Popular math functions:

• 📌 math.sqrt() – Square root
• 📌 math.ceil() – Round up
• 📌 math.floor() – Round down
• 📌 math.pow() – Exponent power
• 📌 math.pi – π constant

10.1 What is a String?

A string is text enclosed in: ✔ Single quotes ' ' ✔ Double quotes " " ✔ Triple quotes ''' ''' or """ """ (for multi-line strings)

name = "Python"
message = 'Hello World'
para = """This is
a multi-line string"""

print(type(name))   # 
        

10.2 Common String Functions

Python provides many built-in string functions to modify and analyze text:

Example:

text = "  welcome to python  "
print(text.strip())
print(text.upper())
print(text.replace("python", "programming"))
        

10.3 Slicing Strings (Extracting Parts)

String slicing helps you extract a part of a string using index numbers.

word = "PYTHON"

print(word[0])     # P
print(word[1:4])   # YTH
print(word[:3])    # PYT
print(word[3:])    # HON
print(word[-1])    # N
        

Slicing Table

10.4 String Formatting (f-string, format())

String formatting lets you insert variables inside strings easily.

✔ Using f-strings (most modern & simple)

name = "John"
age = 25

print(f"My name is {name} and I am {age} years old.")
        

✔ Using format()

print("My name is {} and I am {} years old.".format(name, age))
        

10.5 Escape Characters Explained

Escape characters allow you to insert special symbols inside strings.

print("Hello\nWorld")
print("Python\tProgramming")
print("He said \"Hello\"")
        

11.1 What is a List?

A list is created using square brackets [ ].

fruits = ["apple", "banana", "mango"]
numbers = [10, 20, 30, 40]
mixed = ["John", 25, True, 12.5]

print(type(fruits))   # 
        

📌 List Indexing

fruits = ["apple", "banana", "mango"]

print(fruits[0])   # apple
print(fruits[2])   # mango
print(fruits[-1])  # mango (last item)
        

11.2 Common List Methods

Lists have many built-in methods. Here are the most useful ones:

Example:

fruits = ["apple", "banana", "mango"]
fruits.append("orange")
fruits.insert(1, "grape")
print(fruits)
        

11.3 List Slicing

You can extract parts of a list using slicing.

numbers = [10, 20, 30, 40, 50, 60]

print(numbers[1:4])  # [20, 30, 40]
print(numbers[:3])   # [10, 20, 30]
print(numbers[3:])   # [40, 50, 60]
print(numbers[::-1]) # Reverse list
        

📌 Slicing Table

11.4 Nested Lists

A list can contain another list inside it.

matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

print(matrix[0][1])  # 2
print(matrix[2][2])  # 9
        

11.5 List Comprehension (Simple & Powerful)

List comprehension provides a clean and short way to create lists.

✔ Example 1: Square of numbers

squares = [x*x for x in range(5)]
print(squares)   # [0, 1, 4, 9, 16]
        

✔ Example 2: Even numbers

evens = [x for x in range(10) if x % 2 == 0]
print(evens)   # [0, 2, 4, 6, 8]
        

12.1 What is a Tuple?

A tuple is created using ( ) parentheses.

fruits = ("apple", "banana", "mango")
numbers = (10, 20, 30, 40)
mixed   = ("John", 25, True, 12.5)

print(type(fruits))  # 
        

📌 Tuple Indexing

fruits = ("apple", "banana", "mango")
print(fruits[0])   # apple
print(fruits[-1])  # mango
        

12.2 Tuple Methods & Operations

Since tuples are immutable, they support only two major methods:

Example:

nums = (10, 20, 30, 20, 10)

print(nums.count(10))  # 2
print(nums.index(30))  # 2
        

12.3 Packing & Unpacking in Tuples

Python allows assigning multiple values at once using tuple packing & unpacking.

📦 Tuple Packing

person = ("John", 25, "Developer")
        

📤 Tuple Unpacking

name, age, role = person

print(name)  # John
print(age)   # 25
print(role)  # Developer
        

12.4 Tuple vs List – Key Differences

12.5 When to Use Tuples (Examples)

Tuples are best used when your data should not change during program execution.

✔ Example 1: Coordinates

point = (10, 20)
        

✔ Example 2: Database Records

student = ("Amit", 101, "BCA")
        

✔ Example 3: Function Returning Multiple Values

def calculator(a, b):
    return (a+b, a-b, a*b)

result = calculator(10, 5)
print(result)  # (15, 5, 50)
        

13.1 Dictionary Basics (Simple Examples)

Dictionaries are created using { } braces.

student = {
    "name": "Amit",
    "age": 21,
    "course": "Python"
}

print(student["name"])   # Amit
        

✔ Key Points

• 🔑 Data stored as key: value
• 📌 Keys must be unique
• 📌 Values can be anything (int, string, list, dict)
• ⚡ Fast lookup — dictionaries are optimized for speed

13.2 Useful Dictionary Methods

Python dictionaries support many powerful functions:

student = {"name": "Amit", "age": 21}

print(student.get("name"))     # Amit
print(student.keys())          # dict_keys(['name', 'age'])
print(student.values())        # dict_values(['Amit', 21])
        

13.3 Nested Dictionary (Dictionary inside Dictionary)

A dictionary can store another dictionary inside it (like JSON).

students = {
    "101": {"name": "Amit", "age": 21},
    "102": {"name": "Neha", "age": 20}
}

print(students["101"]["name"])  # Amit
        

Real Example: JSON-like Structure

product = {
    "id": 1,
    "name": "Laptop",
    "price": 55000,
    "specs": {
        "ram": "8GB",
        "storage": "512GB SSD",
        "processor": "i5"
    }
}

print(product["specs"]["storage"])   # 512GB SSD
        

13.4 Looping through Keys & Values

You can loop through keys, values, or both.

✔ Loop through Keys

for key in student:
    print(key)
        

✔ Loop through Values

for value in student.values():
    print(value)
        

✔ Loop through Both (MOST USEFUL)

for key, value in student.items():
    print(key, ":", value)
        

13.5 Real Use Cases (JSON, APIs, Configurations)

Dictionaries are everywhere in Python development.

✔ Example 1: JSON Response from an API

user = {
    "id": 101,
    "username": "infomark",
    "followers": 2500
}
        

✔ Example 2: Counting Frequency of Words

message = "hello world hello python"
words = message.split()

freq = {}

for w in words:
    freq[w] = freq.get(w, 0) + 1

print(freq)  # {'hello': 2, 'world': 1, 'python': 1}
        

✔ Example 3: Configuration Settings

config = {
    "theme": "dark",
    "font_size": 14,
    "show_line_numbers": True
}
        

14.1 Introduction to Arrays

A Python array is created using the array module:

from array import array

numbers = array('i', [10, 20, 30, 40])
print(numbers)   # array('i', [10, 20, 30, 40])
        

✔ Meaning of type codes:

14.2 Array Operations (Insert, Update, Delete)

Arrays allow modifying values just like lists.

✔ Insert

numbers.insert(1, 99)
print(numbers)   # array('i', [10, 99, 20, 30, 40])
        

✔ Update

numbers[2] = 55
print(numbers)   # array('i', [10, 99, 55, 30, 40])
        

✔ Delete

numbers.remove(55)
print(numbers)   # array('i', [10, 99, 30, 40])
        

14.3 Array Methods (Most Useful)

Arrays support methods similar to lists:

arr.append(50)
arr.extend([60, 70])
print(arr)
        

14.4 Array vs List (Easy Comparison)

14.5 When to Use Arrays in Python

Arrays are extremely useful when working with numeric data.

✔ Example 1: Sensor Data

temperatures = array('f', [36.5, 37.0, 36.8])
        

✔ Example 2: Mathematical Calculations

values = array('i', [1, 2, 3, 4, 5])
print(sum(values))  # 15
        

✔ Example 3: Better Performance Than Lists

big = array('i', range(1, 50000))
        

15.1 datetime Module Explained Easily

Import datetime like this:

import datetime
        

✔ Get Current Date & Time

import datetime

now = datetime.datetime.now()
print(now)
        

✔ Get Only Today's Date

today = datetime.date.today()
print(today)
        

✔ Get Only Current Time

time_now = datetime.datetime.now().time()
print(time_now)
        

15.2 Formatting Dates (strftime)

strftime() converts date/time into readable string formats.

✔ Example of Formatting

import datetime

now = datetime.datetime.now()

print(now.strftime("%d-%m-%Y"))   # 05-12-2025
print(now.strftime("%H:%M:%S"))   # 14:45:22
print(now.strftime("%A"))         # Monday
        

15.3 Time Calculations

You can extract individual parts of the date/time.

now = datetime.datetime.now()

print(now.day)     # 5
print(now.month)   # 12
print(now.year)    # 2025
print(now.hour)    # 14
print(now.minute)  # 47
        

✔ Add Time

future = now + datetime.timedelta(days=10)
print(future)
        

✔ Subtract Time

past = now - datetime.timedelta(hours=5)
print(past)
        

15.4 timedelta – Working with Date Differences

timedelta is used to calculate the difference between two dates.

date1 = datetime.date(2025, 5, 1)
date2 = datetime.date(2025, 5, 20)

difference = date2 - date1
print(difference.days)   # 19
        

15.5 Real Use Cases (Timers, Logs, Scheduling)

✔ Example 1: Timestamp for Logs

log_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print("Log entry at:", log_time)
        

✔ Example 2: Countdown Timer

event = datetime.datetime(2025, 12, 31)
today = datetime.datetime.now()

remaining = event - today
print("Days remaining:", remaining.days)
        

✔ Example 3: Auto-Generated Date in Reports

report_date = datetime.date.today()
print("Report generated on:", report_date)
        

16.1 Reading Files (open, read, readline)

To work with files, Python uses the open() function.

✔ Read Entire File

f = open("demo.txt", "r")
print(f.read())
f.close()
        

✔ Read First Line

f = open("demo.txt", "r")
print(f.readline())
f.close()
        

16.2 Writing Files Safely

"w" will overwrite entire file, so use carefully!

✔ Writing Text into File

f = open("notes.txt", "w")
f.write("Hello, this is new content!")
f.close()
        

✔ Append (Add without removing old data)

f = open("notes.txt", "a")
f.write("\nThis line is added later.")
f.close()
        

16.3 File Modes Explained Simply

16.4 Working with Directories (os module)

Python’s os module helps manage folders, filenames, and file paths.

import os

print(os.getcwd())            # Current directory
os.mkdir("myfolder")          # Create folder
os.listdir()                  # List files/folders
os.rename("old.txt", "new.txt")  # Rename file
        

16.5 Exception Handling in File Input/Output

Always handle missing files safely.

✔ Example with Try–Except

try:
    f = open("unknown.txt", "r")
    print(f.read())
except FileNotFoundError:
    print("File does not exist!")
        

✔ Best Practice: Use "with" (auto-closes file)

with open("data.txt", "r") as f:
    print(f.read())
        

17.1 What is Multithreading?

Multithreading means running multiple operations concurrently within a program.

✔ Why Use Multithreading?

• 🚀 Improves performance for I/O tasks
• 📥 Helps download multiple files at once
• 📩 Useful for sending many emails in background
• 🔄 Keeps applications responsive

✔ Importing the Thread Module

from threading import Thread
        

17.2 How to Create Threads

There are two common ways to create threads:

➡️ Method 1: Create a Thread Using a Function

from threading import Thread
import time

def task():
    print("Task is running...")
    time.sleep(2)

t = Thread(target=task)
t.start()

print("Main program continues...")
        

➡️ Method 2: Create a Thread Using a Class

from threading import Thread

class MyThread(Thread):
    def run(self):
        print("Thread running using class method")

t = MyThread()
t.start()
        

17.3 Race Conditions Explained Simply

A race condition happens when two threads try to access or modify the same data at the same time.

✔ Problem Example

counter = 0

def increment():
    global counter
    for i in range(100000):
        counter += 1
        

Multiple threads running this at once will produce incorrect results.

17.4 Locks & Synchronization

Locks prevent multiple threads from accessing the same resource at the same time. They help avoid race conditions.

✔ Using Lock in Python

from threading import Thread, Lock

lock = Lock()
counter = 0

def safe_increment():
    global counter
    for i in range(100000):
        with lock:        # Only one thread allowed here
            counter += 1

t1 = Thread(target=safe_increment)
t2 = Thread(target=safe_increment)

t1.start()
t2.start()

t1.join()
t2.join()

print(counter)
        

17.5 Multithreading Use Cases (Easy Examples)

Multithreading is useful in many real-life scenarios:

1. Downloading Multiple Files

   Thread(target=download_file, args=("file1.jpg",)).start()
   Thread(target=download_file, args=("file2.jpg",)).start()
                   

2. Sending Multiple Emails

   for email in email_list:
       Thread(target=send_mail, args=(email,)).start()
                   

3. Real-Time Applications
   • Video games 🎮
   • Chat apps 💬
   • Live data processing 📊

18.1 What is SMTP – How Email Works?

SMTP is the protocol used to send emails across the internet. Python includes a built-in module called smtplib that allows you to send emails programmatically.

✔ How Email Sending Works

• ✉ You write an email in Python
• 🔐 Python connects to the SMTP server (Gmail, Yahoo, Outlook, etc.)
• 📤 The server sends your email to the recipient

Common SMTP Servers

18.2 Sending Simple Emails

You can send a basic email using only a few lines of code using smtplib and email.mime modules.

import smtplib
from email.mime.text import MIMEText

sender = "yourmail@gmail.com"
password = "your-app-password"
receiver = "receiver@example.com"

message = MIMEText("Hello, this is a test email sent using Python!")
message["Subject"] = "Test Email"
message["From"] = sender
message["To"] = receiver

server = smtplib.SMTP("smtp.gmail.com", 587)
server.starttls()
server.login(sender, password)
server.send_message(message)
server.quit()

print("Email sent successfully!")
        

18.3 Sending Attachments (Easy Explanation)

You can attach images, PDFs, Word files, etc., using MIMEBase.

import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email.mime.text import MIMEText
from email import encoders

sender = "yourmail@gmail.com"
receiver = "receiver@example.com"
password = "your-app-password"

msg = MIMEMultipart()
msg["Subject"] = "Email with Attachment"
msg["From"] = sender
msg["To"] = receiver

# Email body
msg.attach(MIMEText("Please find the attached file.", "plain"))

# Attachment
file = "report.pdf"
attachment = open(file, "rb")

mime = MIMEBase("application", "octet-stream")
mime.set_payload(attachment.read())
attachment.close()

encoders.encode_base64(mime)
mime.add_header("Content-Disposition", f"attachment; filename={file}")
msg.attach(mime)

server = smtplib.SMTP("smtp.gmail.com", 587)
server.starttls()
server.login(sender, password)
server.sendmail(sender, receiver, msg.as_string())
server.quit()

print("Email with attachment sent!")
        

18.4 Sending HTML Emails

HTML emails allow you to include colors, buttons, images, and styled content.

import smtplib
from email.mime.text import MIMEText

html = """
Welcome!
This is an HTML Email sent using Python.
"""

message = MIMEText(html, "html")
message["Subject"] = "HTML Email Example"
message["From"] = "yourmail@gmail.com"
message["To"] = "receiver@example.com"

server = smtplib.SMTP("smtp.gmail.com", 587)
server.starttls()
server.login("yourmail@gmail.com", "your-app-password")
server.send_message(message)
server.quit()

print("HTML email sent!")
        

18.5 Automating Emails with Python

Python can send emails automatically — useful for reminders, notifications, marketing, or system alerts.

✔ Example: Send Email Every Morning

import schedule
import time
import smtplib
from email.mime.text import MIMEText

def send_daily_reminder():
    msg = MIMEText("This is your daily reminder!")
    msg["Subject"] = "Daily Reminder"
    msg["From"] = "yourmail@gmail.com"
    msg["To"] = "receiver@example.com"

    server = smtplib.SMTP("smtp.gmail.com", 587)
    server.starttls()
    server.login("yourmail@gmail.com", "your-app-password")
    server.send_message(msg)
    server.quit()

schedule.every().day.at("09:00").do(send_daily_reminder)

while True:
    schedule.run_pending()
    time.sleep(1)
        

19.1 How to Connect to MySQL

To connect Python to MySQL, we use the package mysql-connector-python.

pip install mysql-connector-python

✔ Connecting Python to MySQL

import mysql.connector

connection = mysql.connector.connect(
    host="localhost",
    user="root",
    password="yourpassword",
    database="testdb"
)

print("MySQL Connected Successfully!")
connection.close()
        

MySQL Connection Parameters

19.2 CRUD Operations (Create, Read, Update, Delete)

CRUD operations are the foundation of all database work. Let's learn them step-by-step with clear examples.

🟢 CREATE – Insert Data

cursor = connection.cursor()
query = "INSERT INTO students (name, age) VALUES (%s, %s)"
data = ("John", 21)

cursor.execute(query, data)
connection.commit()

print("Data inserted successfully!")
        

🔵 READ – Fetch Data

cursor.execute("SELECT * FROM students")
rows = cursor.fetchall()

for row in rows:
    print(row)
        

🟡 UPDATE – Modify Data

query = "UPDATE students SET age = %s WHERE name = %s"
cursor.execute(query, (22, "John"))
connection.commit()

print("Record updated!")
        

🔴 DELETE – Remove Data

query = "DELETE FROM students WHERE name = %s"
cursor.execute(query, ("John",))
connection.commit()

print("Record deleted!")
        

19.3 Handling Database Errors

Errors can happen—wrong credentials, wrong query, server not running, etc. Python provides clean error handling using try-except.

import mysql.connector
from mysql.connector import Error

try:
    connection = mysql.connector.connect(
        host="localhost",
        user="root",
        password="wrongpass",
        database="testdb"
    )
except Error as e:
    print("Error:", e)
        

Common MySQL Errors

• ❌ Wrong username/password
• ❌ Database does not exist
• ❌ MySQL server not running
• ❌ Incorrect SQL query syntax

19.4 Introduction to SQLite

SQLite is a lightweight, file-based database used in mobile apps, small projects, and local storage. It requires no installation.

✔ Connect to SQLite

import sqlite3

connection = sqlite3.connect("mydatabase.db")
cursor = connection.cursor()

print("SQLite connected!")
        

CRUD Example in SQLite

cursor.execute("CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT)")

cursor.execute("INSERT INTO users (name) VALUES ('Alice')")
connection.commit()

cursor.execute("SELECT * FROM users")
print(cursor.fetchall())
        

19.5 Database Best Practices

To build professional applications, follow these recommended practices:

• 🔐 Always use secure credentials
• 📁 Close connections after use
• ⚡ Use prepared statements to avoid SQL injection
• 📝 Validate all input before using in queries
• 🚀 Use connection pooling for large applications

20.1 What are Classes & Objects?

OOP revolves around two main ideas: Class and Object.

✔ Class (Blueprint)

A class is a template or blueprint that defines how an object should be created.

✔ Object (Instance)

An object is a real instance of a class that contains data (variables) and behaviour (functions).

✔ Python Example

class Car:
    brand = "Toyota"
    color = "Red"

# Creating object
my_car = Car()

print(my_car.brand)
print(my_car.color)
        

20.2 Inheritance – Reuse Code Easily

Inheritance allows one class to use the properties and methods of another class. It increases reusability and reduces code repetition.

✔ Parent Class → Child Class

class Animal:
    def sound(self):
        print("This animal makes a sound")

class Dog(Animal):
    def bark(self):
        print("Dog barks!")

pet = Dog()
pet.sound()
pet.bark()
        

Types of Inheritance

20.3 Polymorphism – One Function, Many Forms

Polymorphism allows the same function name to behave differently for different objects.

✔ Example in Python

class Bird:
    def make_sound(self):
        print("Bird chirps")

class Dog:
    def make_sound(self):
        print("Dog barks")

def play_sound(animal):
    animal.make_sound()

play_sound(Bird())
play_sound(Dog())
        

Real-Life Example

• 📱 Pressing a button → Camera opens on phone
• 💻 Pressing same button → Takes screenshot on laptop

20.4 Encapsulation – Hide Internal Details

Encapsulation means protecting data by restricting access through methods only.

✔ Private Variables

class BankAccount:
    def __init__(self):
        self.__balance = 1000   # Private variable

    def deposit(self, amount):
        self.__balance += amount

    def show_balance(self):
        print("Balance:", self.__balance)

account = BankAccount()
account.deposit(500)
account.show_balance()
        

20.5 Abstraction – Only Show What’s Needed

Abstraction hides complex details and exposes only necessary functions.

✔ Example

from abc import ABC, abstractmethod

class Vehicle(ABC):
    @abstractmethod
    def start(self):
        pass

class Car(Vehicle):
    def start(self):
        print("Car engine started")

car = Car()
car.start()
        

21.1 Understanding Sockets (Very Simple)

A socket is the connection point between two computers. It allows them to send and receive data just like phone lines.

✔ Types of Sockets

✔ Simple TCP Client (Connect to Server)

import socket

client = socket.socket()
client.connect(("127.0.0.1", 9000))

client.send("Hello Server".encode())
data = client.recv(1024)

print("Received:", data.decode())
client.close()
        

✔ Simple TCP Server

import socket

server = socket.socket()
server.bind(("127.0.0.1", 9000))
server.listen(1)

print("Server running...")

conn, addr = server.accept()
print("Connected:", addr)

data = conn.recv(1024).decode()
print("Client says:", data)

conn.send("Welcome Client!".encode())
conn.close()
        

21.2 HTTP Requests (Concept Only)

HTTP is the protocol used by websites. Python can send GET and POST requests to interact with web servers.

✔ GET Request Example

import requests

response = requests.get("https://api.github.com")
print("Status:", response.status_code)
print("Data:", response.json())
        

✔ POST Request Example

import requests

data = {"name": "John", "age": 25}
response = requests.post("https://example.com/api", json=data)

print("Server Response:", response.text)
        

21.3 APIs – How Python Talks to Websites

API stands for Application Programming Interface. It allows apps to talk to each other — like your phone app fetching weather or sending messages.

✔ Fetch Weather via API (Example)

import requests

api = "https://api.weatherapi.com/v1/current.json"
params = {
    "key": "your_api_key",
    "q": "London"
}

response = requests.get(api, params=params)
data = response.json()

print("Temperature:", data["current"]["temp_c"])
        

Real-Life API Uses

• 📱 Login with Google
• 📦 Track courier packages
• 💳 Online payments (Razorpay, Stripe)
• 📸 Upload photos to Instagram

21.4 Basic Networking Tools in Python

Python provides many built-in modules for networking tasks.

✔ Example: Check if Website is Online

import requests

try:
    requests.get("https://google.com")
    print("Website is Online!")
except:
    print("Website is Offline!")
        

21.5 Safe & Ethical Network Use

Networking tools are powerful — but must always be used responsibly.

• ✔ Access only authorized systems
• ✔ Never perform scanning without permission
• ✔ Don’t overload servers with too many requests
• ✔ Always use HTTPS for secure communication

22.1 Introduction to Tkinter (Easy GUI)

Tkinter is Python’s built-in library for creating desktop applications such as calculators, forms, tools, dashboards, and more.

✔ How to Import Tkinter

import tkinter as tk
        

✔ Create Your First GUI Window

import tkinter as tk

window = tk.Tk()
window.title("My First GUI")
window.geometry("300x200")

window.mainloop()
        

22.2 Basic GUI Components

Tkinter provides many simple components called widgets to build GUI apps.

✔ Example: Adding Widgets

import tkinter as tk

win = tk.Tk()
win.title("Widgets Example")

tk.Label(win, text="Enter Name:").pack()
tk.Entry(win).pack()
tk.Button(win, text="Submit").pack()

win.mainloop()
        

22.3 Handling Buttons & Events

Buttons become useful when they perform an action using a function.

✔ Example: Button Click Event

import tkinter as tk

def say_hello():
    print("Hello User!")

win = tk.Tk()
win.title("Event Example")

tk.Button(win, text="Click Me", command=say_hello).pack()

win.mainloop()
        

22.4 Build a Simple GUI App

Let’s build a simple name greeting application.

import tkinter as tk

def greet():
    name = entry.get()
    label_result.config(text="Hello, " + name)

win = tk.Tk()
win.title("Greeting App")
win.geometry("300x200")

tk.Label(win, text="Your Name:").pack()

entry = tk.Entry(win)
entry.pack()

tk.Button(win, text="Greet Me", command=greet).pack()

label_result = tk.Label(win, text="")
label_result.pack()

win.mainloop()
        

22.5 GUI Best Practices

• ✔ Keep the interface simple and clean
• ✔ Use frames to group widgets
• ✔ Avoid too many colors or fonts
• ✔ Use meaningful button names (Save, Submit, Upload)
• ✔ Test the GUI on different screen sizes

23.1 What is Web Scraping?

Web scraping means extracting useful information from websites automatically using a program.

✔ Common Uses of Web Scraping

• ✔ Price comparison websites
• ✔ Job listing aggregators
• ✔ Data collection for research
• ✔ Social media analytics
• ✔ Competitor monitoring

23.2 Understanding HTML Structure

Web scraping requires understanding how HTML pages are built. Websites are made up of tags such as:

<div class="product">
    <h2>Apple iPhone 15</h2>
    <span class="price">$799</span>
</div>
        

23.3 BeautifulSoup (Concept Only)

BeautifulSoup is a Python package that helps extract content from HTML pages easily.

✔ Step-by-Step Scraping Flow

1. Send request to website using requests
2. Receive HTML source code
3. Parse HTML using BeautifulSoup
4. Find required tags
5. Extract text or attributes

✔ Simple Conceptual Example

from bs4 import BeautifulSoup

html = "<h1>Hello World</h1>"
soup = BeautifulSoup(html, "html.parser")

print(soup.h1.text)   # Output: Hello World
        

23.4 Extracting Data Safely

Here is an example of scraping product titles from a sample web page.

import requests
from bs4 import BeautifulSoup

url = "https://example.com/products"
response = requests.get(url)

soup = BeautifulSoup(response.text, "html.parser")

products = soup.find_all("h2", class_="title")

for p in products:
    print(p.text)
        

❗ Avoid Heavy Scraping

• ❌ Sending too many requests
• ❌ Using fake identities
• ❌ Scraping personal/private data

23.5 Legal & Ethical Guidelines

Web scraping is powerful, but it must follow rules and respect privacy.

• ✔ Always check website robots.txt
• ✔ Scrape only publicly available data
• ✔ Never overload a website with requests
• ✔ Do not scrape login-protected areas
• ✔ Cite or credit website sources when needed

24.1 What is Image Processing?

Image processing is the technique of performing operations on images to enhance them, extract useful information, or modify them for specific use cases.

✔ Where Image Processing is Used

• ✔ Face detection in cameras
• ✔ Filters in Instagram/Snapchat
• ✔ Medical image analysis (X-ray, MRI)
• ✔ Number plate recognition
• ✔ Object detection in AI

24.2 Opening & Reading Images Conceptually

To work with images in Python, install Pillow first:

pip install pillow
        

✔ Load & Display an Image

from PIL import Image

img = Image.open("nature.jpg")
img.show()
        

Pillow supports formats like JPEG, PNG, BMP, GIF.

24.3 Basic Transformations (Resize, Crop, Rotate)

Image manipulation is extremely easy with Pillow. Here are the essential transformations:

✔ Resize Image

resized = img.resize((300, 300))
resized.show()
        

✔ Rotate Image

rotated = img.rotate(45)
rotated.show()
        

✔ Crop Part of Image

cropped = img.crop((50, 50, 200, 200))  # (left, top, right, bottom)
cropped.show()
        

24.4 Filters & Enhancements (Easy Guide)

Pillow has built-in filters that help you modify image brightness, sharpness, contrast, etc.

✔ Apply Built-In Filters

from PIL import ImageFilter

blurred = img.filter(ImageFilter.BLUR)
blurred.show()

edge_image = img.filter(ImageFilter.FIND_EDGES)
edge_image.show()
        

✔ Enhance Image Quality

from PIL import ImageEnhance

b = ImageEnhance.Brightness(img)
bright_img = b.enhance(1.5)   # Increase brightness
bright_img.show()

c = ImageEnhance.Contrast(img)
high_contrast = c.enhance(2)
high_contrast.show()
        

24.5 Real Use Cases in Daily Life

Python image processing is used everywhere. Here are real examples:

• ✔ Auto-resizing images for websites
• ✔ Converting images from PNG → JPG
• ✔ Blur faces for privacy (YouTube vlogs)
• ✔ Creating thumbnails automatically
• ✔ Applying artistic filters

✔ Saving the Edited Image

edited = img.resize((400, 400))
edited.save("edited_image.jpg")
        

25.1 What is Data Science?

Data Science is the field of turning raw data into meaningful insights using statistics, programming, and visualization techniques.

🎯 Why Data Science is Important

• ✔ Helps companies make data-driven decisions
• ✔ Predicts customer behavior
• ✔ Improves business processes
• ✔ Powers AI, ML, and automation

🧠 Data Science Workflow (Simple Explanation)

25.2 Data Cleaning Concepts

Data cleaning is the most important step in Data Science. 70–80% of a data scientist’s time is spent cleaning and preparing data.

✔ Common Data Problems

• ❌ Missing values
• ❌ Duplicate rows
• ❌ Incorrect data types
• ❌ Outliers (extreme values)

✔ How Data is Cleaned (Concept Only)

• ✔ Filling missing values (mean/median)
• ✔ Removing duplicates
• ✔ Standardizing formats (date, number, text)
• ✔ Handling outliers

25.3 Data Visualization Explanation

Data visualization helps convert numbers into visual stories. Python uses tools like Matplotlib, Seaborn, Plotly for charts.

✔ Why Visualization is Important

• ✔ Makes data easy to understand
• ✔ Shows patterns, trends & insights
• ✔ Helps in decision-making

📊 Common Types of Charts

25.4 Basic Statistics for Beginners

Statistics is the backbone of Data Science. You don't need deep math — just the basics.

✔ Key Statistical Concepts (Simple)

• Mean – Average value
• Median – Middle value
• Mode – Most common value
• Range – Highest − lowest
• Standard Deviation – Spread of data

25.5 Real-Life Data Science Projects

Here are simple real-world applications of Data Science:

• ✔ Predicting house prices
• ✔ Recommending movies (Netflix algorithm)
• ✔ Analyzing customer purchase patterns
• ✔ Detecting spam emails
• ✔ Forecasting sales for businesses

💬 Example Scenario

26.1 What is Machine Learning?

Machine Learning (ML) is a branch of Artificial Intelligence that allows computers to learn patterns from data and make decisions without being explicitly programmed.

🎯 Why ML is Popular?

• ✔ Automates tasks
• ✔ Identifies complex patterns
• ✔ Makes predictions (future trends)
• ✔ Used in almost every industry

🧠 How ML Works (Simple Workflow)

26.2 Data Preparation Concepts

Machine Learning works only when the data is clean and organized. This step is called Data Preprocessing.

✔ Key Steps in Data Preparation

• ✔ Handling missing values
• ✔ Converting text to numbers
• ✔ Splitting data into training & testing
• ✔ Normalizing or scaling values

26.3 Supervised Learning (Simple Explanation)

In Supervised Learning, the model learns using labeled data, meaning the input already has correct answers.

📘 Example

✔ Types of Supervised Learning

• Regression – Predict numbers
  (House price, temperature)
• Classification – Predict categories
  (Spam or Not Spam, Yes or No)

26.4 Unsupervised Learning (Easy Examples)

In Unsupervised Learning, the model is given data without labels. It tries to find hidden patterns on its own.

✔ Most Common Unsupervised Technique

• Clustering – Grouping similar items

✔ Where It Is Used?

• ✔ Market segmentation
• ✔ Product recommendation
• ✔ Fraud detection

26.5 Real-World ML Use Cases

Machine Learning is everywhere. Here are simple, real-world examples you see daily:

• ✔ YouTube video recommendations
• ✔ Face unlock on mobile phones
• ✔ Self-driving car route decisions
• ✔ Detecting fake transactions in banks
• ✔ Voice assistants (Siri, Alexa)

💬 Simple Example

27.1 What is Artificial Intelligence?

Artificial Intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence. AI systems can think, learn, decide, and solve problems.

🎯 What Can AI Do?

• ✔ Recognize images & faces
• ✔ Predict outcomes (sales, weather, prices)
• ✔ Understand and generate human language
• ✔ Drive cars autonomously
• ✔ Recommend videos, songs, and products

27.2 Categories of AI (ANI, AGI, ASI)

AI is divided into three major categories based on intelligence level:

27.3 Python's Role in AI

Python is the most popular language for Artificial Intelligence because it is:

• ✔ Easy to learn and write
• ✔ Has powerful AI & ML libraries
• ✔ Huge community support
• ✔ Ideal for rapid prototyping

🔬 Popular Python Libraries for AI

• TensorFlow – Deep Learning
• Keras – Easy Neural Networks
• PyTorch – Advanced ML models
• Scikit-Learn – Simple ML algorithms
• NLTK / spaCy – Natural Language Processing

27.4 Real-Life AI Applications

Artificial Intelligence is used almost everywhere in the modern world.

• ✔ Self-driving cars analyzing road conditions
• ✔ Medical diagnosis systems predicting diseases
• ✔ Virtual assistants answering questions
• ✔ Chatbots providing customer support
• ✔ E-commerce recommendations based on past purchases
• ✔ Banking fraud detection systems

27.5 Ethics of AI (Simple Explanation)

As AI becomes powerful, ethical considerations are extremely important to ensure AI is used responsibly and safely.

⚖️ Key Ethical Concerns

• ❌ Bias in AI decisions
• ❌ Privacy issues
• ❌ Job displacement due to automation
• ❌ Misuse of AI for harmful activities

✔ Ethical AI Practices

• ✔ Transparent algorithms
• ✔ Fair and unbiased datasets
• ✔ Privacy protection
• ✔ Human oversight and accountability

28.1 What is a Function?

A function is a block of code that runs only when called. Functions help reduce repetition and make programs cleaner.

✔ Basic Function Example

def greet():
    print("Hello, welcome to Python!")

greet()  
        

28.2 Defining Your Own Functions

You can create a function using the def keyword.

✔ Example: Simple Addition Function

def add_numbers():
    print(10 + 20)

add_numbers()
        

28.3 Arguments & Parameters (Simple Explanation)

Functions can take inputs called arguments. These allow functions to work with different values.

🎯 Example: Function with Arguments

def greet(name):
    print("Hello", name)

greet("John")
greet("Ayesha")
        

✔ Return Statement Explained

def add(a, b):
    return a + b

result = add(5, 7)
print(result)
        

28.4 Lambda Functions (One-line Functions)

A lambda function is a small, anonymous (no name) function written in one line.

✔ Example: One-line Add Function

add = lambda x, y: x + y
print(add(5, 3))
        

✔ Use Cases of Lambda Functions

• ✔ Small calculations
• ✔ Sorting lists
• ✔ Used with map(), filter(), reduce()

28.5 Recursion Explained Slowly

Recursion means a function calling itself. It's used to solve problems that can be broken into smaller sub-problems.

✔ Example: Simple Recursive Countdown

def countdown(n):
    if n == 0:
        print("Blast Off!")
    else:
        print(n)
        countdown(n - 1)

countdown(5)