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.
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.
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
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
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
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
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
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
In this section we will learn how to access and work with MySQL and MongoDB databases
- Python MongoDB Tutorial
- Python MySQL Tutorial
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
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.





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