Python Programming: Complete Beginner to Advanced Course

Introduction to Python

Welcome to the world of Python programming! This course is your comprehensive guide to mastering Python, one of the most popular and versatile programming languages in the world. Whether you are a complete beginner with no prior programming experience or a seasoned developer looking to add Python to your skillset, this course is designed to take you from the fundamentals to advanced concepts, equipping you with the knowledge and skills to build real-world applications.

What is Python?

Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects. [1]

Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a “batteries included” language due to its comprehensive standard library.

Why Learn Python?

The popularity of Python has been growing steadily for years, and for good reason. Here are some of the key reasons why you should learn Python:

• Beginner-Friendly: Python has a simple, clean syntax that is easy to learn, making it an ideal language for beginners.
• Versatile: Python is used in a wide range of applications, including web development, data science, machine learning, artificial intelligence, scientific computing, automation, and more.
• Large and Active Community: Python has a massive and supportive community of developers who contribute to its growth and provide help to newcomers.
• Extensive Libraries and Frameworks: Python’s rich ecosystem of libraries and frameworks, such as Django, Flask, NumPy, Pandas, and TensorFlow, makes it easy to build complex applications quickly.
• High Demand and Lucrative Career Opportunities: Python is one of the most in-demand programming languages in the job market, with high salaries for skilled professionals. [2]

Setting up Your Python Environment

Before you can start writing Python code, you need to install the Python interpreter on your computer. The installation process is straightforward and varies slightly depending on your operating system.

Windows

1. Go to the official Python website: https://www.python.org/downloads/
2. Download the latest version of Python for Windows.
3. Run the installer and make sure to check the box that says “Add Python to PATH”.
4. Follow the on-screen instructions to complete the installation.

macOS

macOS comes with a pre-installed version of Python 2, but it is highly recommended to install the latest version of Python 3. The easiest way to do this is by using Homebrew, a popular package manager for macOS.

1. Open the Terminal app.
2. Install Homebrew by running the following command:
   bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
3. Once Homebrew is installed, you can install Python by running the following command:
   bash
brew install python

Linux

Most Linux distributions come with Python pre-installed. You can check the version of Python by running the following command in the terminal:

python --version

If you need to install or upgrade Python, you can use your distribution’s package manager. For example, on Ubuntu, you can install Python by running the following command:

sudo apt-get update
sudo apt-get install python3

Your First Python Program

Now that you have Python installed, let’s write your first Python program. It’s a tradition in the programming world to start with a program that prints “Hello, World!” to the screen.

1. Open a text editor and create a new file called hello.py.
2. Add the following code to the file:
   python
print("Hello, World!")
3. Save the file.
4. Open a terminal or command prompt and navigate to the directory where you saved the file.
5. Run the program by typing the following command:
   bash
python hello.py

You should see the following output on the screen:

Hello, World!

Congratulations! You have successfully written and executed your first Python program.

Python Fundamentals

Now that you have a basic understanding of Python and have written your first program, it’s time to dive deeper into the fundamentals of the language. This section will cover the essential building blocks of Python programming, including variables, data types, operators, and control flow.

Variables and Data Types

In Python, a variable is a named location used to store data in memory. Variables are created when you first assign a value to them. Python is a dynamically-typed language, which means you don’t have to declare the type of a variable when you create it. The interpreter automatically infers the type based on the value assigned.

Python has several built-in data types, including:

• Numeric Types: int (integers), float (floating-point numbers), complex (complex numbers)
• Text Type: str (strings)
• Sequence Types: list, tuple, range
• Mapping Type: dict (dictionaries)
• Set Types: set, frozenset
• Boolean Type: bool (True or False)
• Binary Types: bytes, bytearray, memoryview

Here’s an example of how to create and use variables of different data types:

# Numeric types
x = 10          # int
y = 3.14        # float
z = 1 + 2j      # complex

# Text type
name = "Alice"  # str

# Sequence types
my_list = [1, 2, 3]         # list
my_tuple = (4, 5, 6)        # tuple
my_range = range(10)        # range

# Mapping type
my_dict = {"name": "Bob", "age": 30}  # dict

# Set types
my_set = {1, 2, 3}          # set
my_frozenset = frozenset({4, 5, 6}) # frozenset

# Boolean type
is_active = True            # bool

# Printing variables and their types
print(x, type(x))
print(name, type(name))
print(my_list, type(my_list))
print(my_dict, type(my_dict))
print(is_active, type(is_active))

Operators

Operators are special symbols in Python that carry out arithmetic or logical computation. The value that the operator operates on is called the operand.

Python has a wide range of operators, including:

• Arithmetic Operators: +, -, *, /, %, **, //
• Assignment Operators: =, +=, -=, *=, /=
• Comparison Operators: ==, !=, >, <, >=, <=
• Logical Operators: and, or, not
• Identity Operators: is, is not
• Membership Operators: in, not in
• Bitwise Operators: &, |, ^, ~, <<, >>

Control Flow

Control flow is the order in which statements are executed in a program. Python provides several control flow statements that allow you to control the execution of your code.

Conditional Statements

Conditional statements allow you to execute a block of code only if a certain condition is true. The most common conditional statements in Python are if, elif, and else.

x = 10

if x > 0:
    print("x is positive")
elif x == 0:
    print("x is zero")
else:
    print("x is negative")

Loops

Loops are used to execute a block of code repeatedly. Python has two types of loops: for loops and while loops.

• for loop: A for loop is used to iterate over a sequence (such as a list, tuple, or string).

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

• while loop: A while loop is used to execute a block of code as long as a certain condition is true.

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

Functions and Modules

Functions are reusable blocks of code that perform a specific task. They help to break down large programs into smaller, more manageable pieces, making your code more organized, readable, and reusable.

Defining and Calling Functions

You can define a function in Python using the def keyword, followed by the function name and a set of parentheses. The code block within the function is indented.

# Defining a function
def greet(name):
    """This function greets the person passed in as a parameter."""
    print("Hello, " + name + ". Good morning!")

# Calling a function
greet("Alice")

Function Arguments

Functions can take arguments, which are values passed to the function when it is called. You can define functions with different types of arguments:

• Positional Arguments: Arguments passed to a function in the correct positional order.
• Keyword Arguments: Arguments passed to a function with a keyword and a value.
• Default Arguments: Arguments that take a default value if no value is provided in the function call.
• Variable-Length Arguments: Arguments that can accept an arbitrary number of arguments.

Modules

Modules are Python files with a .py extension that contain Python code. They allow you to logically organize your Python code. Grouping related code into a module makes the code easier to understand and use.

To use the code from a module in your program, you need to import it using the import statement.

# Import the math module
import math

# Use functions from the math module
print(math.pi)
print(math.sqrt(16))

Python has a vast standard library with many built-in modules that you can use in your programs. You can also create your own modules to reuse code across multiple projects.

Data Structures in Python

Data structures are a way of organizing and storing data in a computer so that it can be accessed and modified efficiently. Python has a rich set of built-in data structures that you can use to store and manipulate data.

Lists

A list is a collection of items that are ordered and mutable. Lists are created by placing a comma-separated sequence of items inside square brackets [].

# Creating a list
my_list = [1, "apple", 3.14, True]

# Accessing items in a list
print(my_list[0])  # Output: 1
print(my_list[1])  # Output: apple

# Modifying a list
my_list[0] = 100
print(my_list)  # Output: [100, 'apple', 3.14, True]

# Adding items to a list
my_list.append("banana")
print(my_list)  # Output: [100, 'apple', 3.14, True, 'banana']

Tuples

A tuple is a collection of items that are ordered and immutable. Tuples are created by placing a comma-separated sequence of items inside parentheses ().

# Creating a tuple
my_tuple = (1, "apple", 3.14, True)

# Accessing items in a tuple
print(my_tuple[0])  # Output: 1
print(my_tuple[1])  # Output: apple

# Tuples are immutable, so you cannot modify them
# my_tuple[0] = 100  # This will raise a TypeError

Dictionaries

A dictionary is a collection of key-value pairs that are unordered and mutable. Dictionaries are created by placing a comma-separated sequence of key-value pairs inside curly braces {}.

# Creating a dictionary
my_dict = {"name": "Alice", "age": 30, "city": "New York"}

# Accessing values in a dictionary
print(my_dict["name"])  # Output: Alice
print(my_dict["age"])   # Output: 30

# Modifying a dictionary
my_dict["age"] = 31
print(my_dict)  # Output: {'name': 'Alice', 'age': 31, 'city': 'New York'}

# Adding items to a dictionary
my_dict["country"] = "USA"
print(my_dict)  # Output: {'name': 'Alice', 'age': 31, 'city': 'New York', 'country': 'USA'}

Sets

A set is a collection of unique items that are unordered and mutable. Sets are created by placing a comma-separated sequence of items inside curly braces {}.

# Creating a set
my_set = {1, 2, 3, 4, 5}

# Sets only store unique items
my_set = {1, 2, 2, 3, 3, 3}
print(my_set)  # Output: {1, 2, 3}

# Adding items to a set
my_set.add(4)
print(my_set)  # Output: {1, 2, 3, 4}

# Removing items from a set
my_set.remove(2)
print(my_set)  # Output: {1, 3, 4}

Object-Oriented Programming (OOP) in Python

Object-Oriented Programming (OOP) is a programming paradigm that uses objects and classes to structure a program. It is a powerful way to create reusable and maintainable code. Python is an object-oriented programming language, and everything in Python is an object, with its properties and methods.

Classes and Objects

A class is a blueprint for creating objects. It defines a set of attributes and methods that the created objects will have. An object is an instance of a class.

Here’s how to create a class and an object in Python:

# Creating a class
class Dog:
    # Class attribute
    species = "Canis familiaris"

    # Initializer / Instance attributes
    def __init__(self, name, age):
        self.name = name
        self.age = age

    # Instance method
    def description(self):
        return f"{self.name} is {self.age} years old"

    # Instance method
    def speak(self, sound):
        return f"{self.name} says {sound}"

# Creating an object (instance of the Dog class)
dog1 = Dog("Buddy", 5)

# Accessing attributes and methods
print(dog1.name)          # Output: Buddy
print(dog1.age)           # Output: 5
print(dog1.species)       # Output: Canis familiaris
print(dog1.description()) # Output: Buddy is 5 years old
print(dog1.speak("Woof")) # Output: Buddy says Woof

Inheritance

Inheritance is a mechanism that allows you to create a new class that inherits the attributes and methods of an existing class. The new class is called the child class, and the existing class is called the parent class.

# Parent class
class Animal:
    def __init__(self, name):
        self.name = name

    def speak(self):
        raise NotImplementedError("Subclass must implement abstract method")

# Child class
class Dog(Animal):
    def speak(self):
        return "Woof!"

# Child class
class Cat(Animal):
    def speak(self):
        return "Meow!"

# Creating objects
dog = Dog("Buddy")
cat = Cat("Whiskers")

print(dog.name)
print(dog.speak())

print(cat.name)
print(cat.speak())

Encapsulation

Encapsulation is the bundling of data and methods that operate on the data into a single unit, a class. It is used to hide the internal state of an object from the outside.

In Python, encapsulation is achieved by making the attributes of a class private by prefixing them with a double underscore __.

class Car:
    def __init__(self, make, model):
        self.__make = make  # Private attribute
        self.__model = model # Private attribute

    def get_make(self):
        return self.__make

    def get_model(self):
        return self.__model

my_car = Car("Toyota", "Corolla")

# Accessing private attributes using getter methods
print(my_car.get_make())
print(my_car.get_model())

# Trying to access private attributes directly will result in an error
# print(my_car.__make)

Polymorphism

Polymorphism is the ability of an object to take on many forms. In Python, polymorphism allows you to use a single interface to represent different types of objects.

class Dog:
    def speak(self):
        return "Woof!"

class Cat:
    def speak(self):
        return "Meow!"

# A function that can work with any animal that has a speak method
def animal_sound(animal):
    print(animal.speak())

dog = Dog()
cat = Cat()

animal_sound(dog)  # Output: Woof!
animal_sound(cat)  # Output: Meow!

Web Development with Python

Python has become a popular choice for web development due to its simplicity, readability, and the availability of powerful web frameworks. This section will introduce you to the world of web development with Python and provide an overview of two of the most popular web frameworks: Flask and Django.

Introduction to Web Development

Web development involves creating websites and web applications that run on a web server and are accessed by users through a web browser. The process typically involves a front-end (what the user sees and interacts with) and a back-end (the server-side logic and database).

Python is primarily used for back-end development, handling tasks such as processing user requests, interacting with databases, and implementing business logic.

Flask

Flask is a micro web framework for Python. It is called a “micro” framework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions that can add application features as if they were implemented in Flask itself.

Here’s a simple example of a Flask application:

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

if __name__ == '__main__':
    app.run(debug=True)

Django

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.

Django follows the model-template-views (MTV) architectural pattern. It includes a rich set of features, such as an object-relational mapper (ORM), a powerful templating engine, a built-in admin interface, and a robust security framework.

Here’s a glimpse of what a Django view looks like:

from django.http import HttpResponse
from django.shortcuts import render

def hello(request):
    return HttpResponse("Hello, world. You're at the polls index.")

Data Science and Machine Learning with Python

Python has become the de facto language for data science and machine learning, thanks to its extensive libraries and frameworks that simplify the process of collecting, analyzing, and visualizing data, as well as building and deploying machine learning models.

Introduction to Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

Python provides a rich ecosystem of libraries for data science, including:

• NumPy: A fundamental package for scientific computing with Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
• Pandas: A powerful library for data manipulation and analysis, providing data structures like DataFrames that make it easy to work with structured data.
• Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
• Seaborn: A data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

Machine Learning

Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Python has several popular libraries for machine learning, including:

• Scikit-learn: A simple and efficient tool for data mining and data analysis, built on NumPy, SciPy, and Matplotlib.
• TensorFlow: An open-source platform for machine learning developed by Google, providing a comprehensive ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
• PyTorch: An open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).

Conclusion

This course has provided you with a comprehensive introduction to the Python programming language, from the fundamental concepts to advanced topics like web development, data science, and machine learning. By now, you should have a solid foundation in Python and be able to start building your own applications and projects.

The journey of a programmer is a continuous process of learning and improvement. The world of Python is vast and constantly evolving, so I encourage you to continue exploring new libraries, frameworks, and concepts. The official Python documentation and the vibrant Python community are excellent resources for your continued learning.

Thank you for taking this course, and I wish you the best of luck in your Python programming journey!

References

[1] Python (programming language) – Wikipedia
[2] Top Programming Languages 2026 – IEEE Spectrum