Python Tutorial

Python Tutorial

Python is one of the most popular programming languages. It’s simple to use, packed with features and supported by a wide range of libraries and frameworks. Its clean syntax makes it beginner-friendly.

• A high-level language, used in web development, data science, automation, AI and more.
• Known for its readability, which means code is easier to write, understand and maintain.
• Backed by library support, so we don’t have to build everything from scratch.

Basic Code Example

The following is a simple program that displays the message “Hello, World!” on the screen.

# Python Program to Print Hello World!
​
print("Hello World!")

Output

Hello World!

To understand working of this code, refer to our article Python Introduction.

Why Learn Python?

• Requires fewer lines of code compared to other programming languages like Java.
• Provides Libraries / Frameworks like Django, Flask and many more for Web Development, and Pandas, Tensorflow, Scikit-learn and many more for, AI/ML, Data Science and Data Analysis
• Cross-platform, works on Windows, Mac and Linux without major changes.
• Used by top tech companies like Google, Netflix and NASA.
• Many Python coding job opportunities in Software Development, Data Science and AI/ML.

Basics

In this section, we’ll cover the basics of Python programming, including installing Python, writing first program, understanding comments and working with variables, keywords and operators.

• Introduction
• Applications
• Input and Output
• Variables
• Operators
• Quiz: Basics, I/O
• Keywords
• Data Types
• Quiz: Data Types, Numbers, Boolean
• Conditional Statements
• Loops
• Quiz: Control Flow, Loops

Functions

In this section of Python 3 tutorial we'll explore Python function syntax, parameter handling, return values and variable scope. Along the way, we'll also introduce versatile functions like range(), map, filter and lambda functions.

• Functions
• Pass Statement in Function
• Global and Local Variables
• Recursion
• *args and **kwargs in Function
• ‘Self’ as Default Argument
• First Class Function
• Lambda Function
• Map, Reduce and Filter Function
• Inner Function
• Decorators
• Quiz: Functions

Data Structures

Python offers versatile collections of data types, including lists, string, tuples, sets, dictionaries and arrays. In this section, we will learn about each data types in detail.

• Strings
• List
• Quiz:List, String
• Tuples
• Dictionary
• Quiz: Tuples, Dictionary
• Sets
• Arrays
• List Comprehension
• Quiz: Sets, Arrays, List Comprehension

Python's collections module offers essential data structures, including the following:

• Counters
• Heapq
• Deque
• OrderedDict
• Defaultdict
• Quiz: Counters, Heapq, Deque, OrderedDict

OOP Concepts

In this section, we'll explore the core principles of object-oriented programming (OOP) in Python. From encapsulation to inheritance, polymorphism, abstract classes and iterators, we'll cover the essential concepts that helps you to build modular, reusable and scalable code.

• Python OOP
• Classes and Objects
• Polymorphism
• Inheritance
• Abstraction
• Encapsulation
• Iterators
• Quiz: OOP

Exception Handling

In this section, we'll explore Python Exception Handling that how Python deals with unexpected errors, enabling us to write fault-tolerant code. We'll cover file handling, including reading from and writing to files.

• Exception Handling
• Built-in Exception
• User defined Exception
• Quiz: Exception Handling

File Handling

In this section, we will cover file handling, including reading from and writing to files.

• File Handling
• Read Files
• Write/Create Files
• OS Module
• pathlib Module
• Directory Management
• Quiz: File Handling

Database Handling

In this section we will learn how to access and work with MySQL and MongoDB databases

• Python MongoDB Tutorial
• Python MySQL Tutorial

Packages or Libraries

Python is a huge collection of Python Packages standard libraries that make development easier. These libraries help with a wide range of tasks and can save you a lot of time by providing ready-to-use tools.

Some commonly used types of libraries in Python include:

• Packages
• Built-in Modules
• DSA Libraries
• GUI Libraries

Data Science

1. Foundational Libraries: These are the libraries that form the base for all data science work. Start here to build a strong foundation.

• NumPy
• Pandas
• Matplotlib

2. Advanced Visualization and Statistical Tools: Once you’re comfortable with basic data handling and visualization, move to creating cleaner visuals and performing statistical analysis.

• Seaborn
• Statsmodel

3. Machine Learning Libraries: After data manipulation and visualization, learn machine learning, starting with simpler models and moving to advanced ones.

• Scikit-learn
• XGBoost /LightGBM

4. Deep Learning Frameworks: If you’re interested in AI and deep learning, these libraries will allow you to build and train neural networks.

• TensorFlow and Keras
• PyTorch
  
  To learn more, you can refer to Python for Data Science.