Python Programming Tutorial

Welcome to Python

Namaste and welcome to Python programming! This tutorial helps you learn Python step by step, from your first program to real-world applications.

Python is a high-level, general-purpose programming language known for readable syntax and a rich ecosystem of libraries.

Why Learn Python?

Python is widely used in software development, data analytics, automation, artificial intelligence, and web applications.

Installation & Environment Setup

Before writing Python programs, install Python and configure your development environment.

Step 1: Install Python

Download Python from the official Python website . On Windows, make sure Python is accessible from your terminal.

Step 2: Verify Installation

python --version

On some systems, use python3 --version.

Step 3: Install VS Code

Install Visual Studio Code and its Python extension.

Step 4: Create a Virtual Environment

python -m venv .venv

Activate on Windows PowerShell

.venv\Scripts\Activate.ps1

Activate on Windows Command Prompt

.venv\Scripts\activate.bat

Activate on macOS or Linux

source .venv/bin/activate

Step 5: Install Packages

python -m pip install pandas numpy

Getting Started with Python

Create a file named main.py and write your first program.

Hello World

print("Hello, World!")
print("Welcome to Python Programming")

Output

Hello, World!
Welcome to Python Programming

The print() function displays output.

Comments

# This is a single-line comment
print("Learning Python")  # Display a message

Indentation

Python uses indentation to define code blocks.

age = 25

if age >= 18:
    print("Eligible to vote")

Variables in Python

Variables are names that refer to values. Python determines the type from the assigned value.

customer_name = "Rahul"
age = 28
premium = 25000.50
is_active = True

print(customer_name)
print(age)
print(premium)
print(is_active)

Variable Naming Rules

• Names can contain letters, numbers, and underscores.
• Names cannot start with a number.
• Variable names are case-sensitive.
• Python keywords cannot be used as variable names.

Formatted Strings

name = "Amit"
premium = 15000

print(f"{name} paid a premium of {premium}")

Output

Amit paid a premium of 15000

Python Data Types

Python provides built-in types for numbers, text, collections, and other values.

Check Data Types

value = 100
name = "Python"

print(type(value))
print(type(name))

Type Conversion

amount = "25000"
premium = int(amount)

print(premium + 5000)

Python Operators

Operators perform calculations, comparisons, and logical operations.

Arithmetic Operators

a = 20
b = 6

print(a + b)   # Addition
print(a - b)   # Subtraction
print(a * b)   # Multiplication
print(a / b)   # Division
print(a // b)  # Floor division
print(a % b)   # Remainder
print(a ** b)  # Exponentiation

Comparison Operators

premium = 25000

print(premium > 20000)
print(premium == 25000)
print(premium != 10000)

Logical Operators

age = 30
salary = 50000

if age >= 18 and salary >= 30000:
    print("Eligible")

Conditional Statements

Conditional statements execute different code depending on whether a condition is true.

if, elif, else

premium = 35000

if premium >= 50000:
    print("High Premium")
elif premium >= 20000:
    print("Medium Premium")
else:
    print("Low Premium")

Output

Medium Premium

Conditional Expression

age = 20

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

Loops in Python

Loops repeat a block of code without writing the same instructions multiple times.

for Loop

customers = ["Rahul", "Amit", "Neha"]

for customer in customers:
    print(customer)

range()

for number in range(1, 6):
    print(number)

while Loop

count = 1

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

enumerate()

names = ["Amit", "Neha", "Ravi"]

for index, name in enumerate(names, start=1):
    print(index, name)

Output

1 Amit
2 Neha
3 Ravi

break and continue

for number in range(1, 10):

    if number == 3:
        continue

    if number == 7:
        break

    print(number)

String Manipulation

Strings represent text and provide methods for cleaning and transforming data.

name = "  rahul kumar  "

print(name.strip())
print(name.upper())
print(name.lower())
print(name.title())

Replace Text

message = "I am learning Java"
updated = message.replace("Java", "Python")

print(updated)

String Slicing

text = "Python Programming"

print(text[0:6])
print(text[-11:])
print(text[::-1])

split() and join()

data = "Amit,Neha,Ravi"

names = data.split(",")
print(names)

result = " | ".join(names)
print(result)

Lists in Python

Lists store ordered collections of values. They are mutable, meaning their contents can change.

premiums = [12000, 25000, 18000, 40000]

print(premiums[0])
print(len(premiums))
print(sum(premiums))
print(max(premiums))

List Methods

names = ["Amit", "Rahul"]

names.append("Neha")
names.insert(1, "Ravi")
names.remove("Amit")

print(names)

List Comprehension

premiums = [10000, 25000, 40000, 15000]

high_premiums = [
    premium
    for premium in premiums
    if premium > 20000
]

print(high_premiums)

Tuples

Tuples are ordered collections that cannot be modified after creation.

customer = ("Rahul", 28, "Mumbai")

print(customer[0])
print(customer[1])

Tuple Unpacking

name, age, city = customer

print(name)
print(age)
print(city)

Sets

A set is an unordered collection of unique elements.

numbers = [1, 2, 2, 3, 4, 4, 5]

unique_numbers = set(numbers)
print(sorted(unique_numbers))

Set Operations

a = {1, 2, 3}
b = {3, 4, 5}

print(a | b)  # Union
print(a & b)  # Intersection
print(a - b)  # Difference

Dictionaries

Dictionaries store information as key-value pairs.

customer = {
    "name": "Rahul",
    "age": 30,
    "premium": 25000,
    "status": "Active"
}

print(customer["name"])
print(customer.get("premium"))

Loop Through a Dictionary

for key, value in customer.items():
    print(key, value)

Real-World Example

employees = {
    "Amit": 45000,
    "Neha": 65000,
    "Ravi": 75000
}

for name, salary in employees.items():
    if salary > 50000:
        print(name, salary)

Functions

Functions organize reusable code into independent blocks.

Defining a Function

def greet(name):
    return f"Hello, {name}"

print(greet("Rahul"))

Function Parameters

def calculate_total(premiums):
    return sum(premiums)

values = [10000, 20000, 30000]

total = calculate_total(values)
print(total)

Default Arguments

def greet(name="Guest"):
    print(f"Welcome, {name}")

greet()
greet("Amit")

Lambda Functions

square = lambda number: number ** 2

print(square(5))

File Handling

Python can create, read, update, and process files.

Write a Text File

with open("customers.txt", "w", encoding="utf-8") as file:
    file.write("Rahul\n")
    file.write("Amit\n")
    file.write("Neha\n")

Read a Text File

with open("customers.txt", "r", encoding="utf-8") as file:
    for line in file:
        print(line.strip())

Read CSV Files

import csv

with open("sales.csv", "r", newline="", encoding="utf-8") as file:
    reader = csv.DictReader(file)

    for row in reader:
        print(row)

Write JSON Files

import json

customer = {
    "name": "Amit",
    "premium": 25000
}

with open("customer.json", "w", encoding="utf-8") as file:
    json.dump(customer, file, indent=4)

Exception Handling

Exception handling allows programs to respond to runtime errors without unexpectedly terminating.

try and except

try:
    age = int(input("Enter your age: "))
    print(f"Your age is {age}")

except ValueError:
    print("Please enter a valid number")

Multiple Exceptions

try:
    a = int(input("Enter first number: "))
    b = int(input("Enter second number: "))

    print(a / b)

except ValueError:
    print("Invalid number")

except ZeroDivisionError:
    print("Cannot divide by zero")

finally:
    print("Execution completed")

Object-Oriented Programming

Object-Oriented Programming organizes software around classes and objects.

Classes and Objects

class Employee:

    def __init__(self, name, salary):
        self.name = name
        self.salary = salary

    def display(self):
        print(self.name, self.salary)


emp1 = Employee("Rahul", 50000)
emp1.display()

Inheritance

class Person:

    def greet(self):
        print("Hello")


class Employee(Person):

    def work(self):
        print("Employee is working")


emp = Employee()

emp.greet()
emp.work()

Encapsulation

class BankAccount:

    def __init__(self, balance):
        self.__balance = balance

    def get_balance(self):
        return self.__balance

    def deposit(self, amount):
        if amount > 0:
            self.__balance += amount


account = BankAccount(10000)
account.deposit(5000)

print(account.get_balance())

Modules and Packages

Modules organize reusable code into files. Packages organize related modules.

Built-in Modules

import math
import random
from datetime import date

print(math.sqrt(64))
print(random.randint(1, 100))
print(date.today())

Installing External Libraries

python -m pip install pandas

Importing Libraries

import pandas as pd

print(pd.__version__)

Advanced Python Concepts

Advanced Python features help developers write efficient, reusable, and maintainable code.

Dictionary Comprehension

numbers = [1, 2, 3, 4]

squares = {
    number: number ** 2
    for number in numbers
}

print(squares)

Generators

def generate_numbers():
    for number in range(1, 6):
        yield number

for value in generate_numbers():
    print(value)

Decorators

from functools import wraps

def logger(func):

    @wraps(func)
    def wrapper(*args, **kwargs):
        print("Function started")
        result = func(*args, **kwargs)
        print("Function finished")
        return result

    return wrapper


@logger
def greet():
    print("Hello Python")


greet()

Python for Data Analytics

Python is used to import, clean, analyze, and visualize business data.

Pandas DataFrame

import pandas as pd

data = {
    "Customer": ["Rahul", "Amit", "Neha"],
    "Premium": [25000, 15000, 40000]
}

df = pd.DataFrame(data)

print(df)

Filter Data

high_premium = df[df["Premium"] > 20000]

print(high_premium)

Aggregate Data

print(df["Premium"].sum())
print(df["Premium"].mean())
print(df["Premium"].max())

Read Excel Data

# Install pandas and openpyxl first.

import pandas as pd

df = pd.read_excel("insurance_data.xlsx")

print(df.head())
print(df.info())

Data Cleaning

df = df.drop_duplicates()

df["Customer"] = (
    df["Customer"]
    .astype("string")
    .str.strip()
    .str.title()
)

df["Premium"] = pd.to_numeric(
    df["Premium"],
    errors="coerce"
)

print(df.isnull().sum())

Practice Projects

Apply your Python knowledge by building small, practical projects.