Welcome to the Python Course! This free repository is designed to provide a comprehensive Python programming course for beginners. Whether you're new to programming or have some experience, this course will help you learn and improve your Python skills.
Happy coding and enjoy the Python course!
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Python is a versatile and widely used programming language that bears a close resemblance to human language. Its similarity to natural language makes it easy to learn and use, appealing to beginners and experienced developers alike. Python finds applications in various industries and is employed for web development, desktop software, system administration, and the creation of machine learning libraries. It has gained significant popularity in the data science and machine learning community.
Python is a high-level, interpreted programming language with cross-platform capabilities. Guido van Rossum developed it in 1989, and since then, it has become a staple for diverse software development tasks. Python enables the creation of web applications, desktop software, data analysis tools, artificial intelligence implementations, and automation scripts. Its appeal lies in its simple and readable syntax, extensive library ecosystem, and the wealth of available resources for learning.
As an interpreted language, Python requires an interpreter to read, interpret, and execute the source code. In contrast to compiled languages like C or C++, which transform code into machine language using a compiler, Python's interpreter provides flexibility. However, interpreted languages generally tend to be slower and less secure than their compiled counterparts. Python comes with its own default interpreter known as the "Python interpreter."
Python code can be executed from the command line, and many integrated development environments (IDEs) utilize the default Python interpreter. Additionally, alternative interpreters like IPython and Jupyter offer enhanced interactivity and environments resembling a command-line shell or a notebook interface. Popular IDEs for Python development include PyCharm, Spyder, Eclipse with the PyDev plugin, Visual Studio Code, and Jupyter Notebook. Notable libraries in the Python ecosystem include NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras, and PyTorch.
In this course, you have the flexibility to choose between using JupyterLab or an IDE with a Python installation, or opting for the convenience of Google Colab without any local installations. Both options provide powerful environments for writing, executing, and collaborating on code.
If you already have Python installed and prefer to work locally, JupyterLab or an IDE will offer a seamless coding experience. On the other hand, if you prefer a cloud-based environment, Google Colab will provide you with the necessary tools and resources.
Feel free to choose the option that best suits your preferences and needs. Throughout the course, we will provide instructions and examples that are compatible with both setups.
Now that you have the necessary information to get started, let's embark on this exciting learning journey together!
If you prefer using an IDE or JupyterLab on your local machine, it's essential to have Python installed. Python serves as the foundation for running JupyterLab and executing Python code.
Follow these steps to install Python:
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Check if Python is already installed by running
python --versioncommand. If Python is already installed, you will see the installed version. If not, continue to the next step. -
Install Python To install Python, visit the official Python website at python.org. Choose the appropriate installer for your operating system (Windows, macOS, or Linux) and follow the installation instructions.
During the installation, make sure to select the option to add Python to your system's PATH variable. This will make it easier to access Python from the command prompt or terminal.
Integrated Development Environments (IDEs) provide a comprehensive environment for writing, editing, and running Python code. IDEs offer features such as code completion, debugging tools, and project management capabilities, making them popular among developers for efficient and productive coding.
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Visual Studio Code (VS Code)
Visual Studio Code, commonly referred to as VS Code, is a popular open-source code editor developed by Microsoft. It offers a wide range of extensions and a big community, making it a powerful choice for Python development.
Install VS Code from the official website (https://code.visualstudio.com/).
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PyCharm
PyCharm is a professional Python IDE developed by JetBrains. It offers a rich set of features specifically designed for Python development, including intelligent code completion, debugging tools, and support for various frameworks.
Download and install PyCharm Community Edition (free) or PyCharm Professional Edition (commercial) from the JetBrains website (https://www.jetbrains.com/pycharm/).
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Atom
Atom is a customizable text editor developed by GitHub. It offers a vast ecosystem of packages and themes, making it a popular choice for Python developers who prefer flexibility and extensibility.
Install Atom from the official website (https://atom.io/).
These are just a few examples of the many IDEs available for Python development. Each IDE has its own set of features and workflows, so feel free to explore and choose the one that best fits your needs and preferences.
By using an IDE, you can streamline your Python development process, benefit from advanced code editing capabilities, and boost your productivity as a Python programmer. You will need to install extensions to work with .ipynb files used in this course. On the other hand, you can use JupyterLab or Google Cloab.
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JupyterLab
JupyterLab is a powerful interactive development environment that allows you to create and run Jupyter notebooks, code, and markdown files.
Install JupyterLab with
pip install jupyterlabcommand. Wait for the installation to complete. Once done, JupyterLab should be installed on your system.Launch JupyterLab by running the following command:
jupyter lab. This will open JupyterLab in your default web browser. -
Google Colab (No Installations Required)
Alternatively, if you prefer a hassle-free setup without any installations on your local machine, you can utilize Google Colab. Google Colab provides a cloud-based environment where you can create, run, and collaborate on Jupyter notebooks without the need for local installations. Google Colab provides pre-installed libraries, including Python and popular data science packages like NumPy and Pandas. You can directly write and execute code in the notebook cells.
To access Google Colab:
- Open Google Colab: Visit the Google Colab website using any web browser.
- Create a New Notebook: Click on "New Notebook" to create a new blank notebook or choose from existing templates.
In this course, we have chosen .ipynb files as the primary format to provide an interactive and comprehensive learning experience. The course material, including code examples, exercises, and project templates, will be provided as Jupyter Notebooks (.ipynb files).
You can open and run the .ipynb files using JupyterLab, Jupyter Notebook, or other compatible environments. These tools offer an interactive interface where you can execute code cells, modify the code, add your own notes, and visualize the results.
By using .ipynb files, we aim to provide an engaging and hands-on learning experience that combines code, documentation, and visualization, allowing you to grasp concepts more effectively and apply them in practical scenarios.
This Python course is designed to introduce you to the fundamentals of programming using the Python programming language. The course covers a wide range of topics, starting from the basics and gradually progressing to more advanced concepts.
Here are some of the key topics covered in this course:
- Introduction to Python
- Basic variables and data types
- Basic operators
- Control flow statements
- Loops
- Methods and functions
- Object-oriented programming (OOP)
- Exception handling
- Modules and packages
- File handling
- Additional Python modules and libraries
- Advanced concepts
- Testing and debugging
The course is structured in a way that each topic builds upon the previous ones, providing a logical progression of learning. Each topic includes theory explanations, practical examples, and exercises to reinforce your understanding.
The goal of this course is to cover foundational aspects of Python programming with an emphasis on script writing, basic data manipulation, and program organization. By the end of this course, students should be able to start writing useful Python programs on their own or be able to understand and modify Python code written by their coworkers.
The course is organized into different modules, each covering a specific topic.
You can start with the first module and progress through the course at your own pace. Make sure to read the lecture notes, try out the examples, and complete the exercises to solidify your knowledge.
Inside the repository, you'll find the following structure:
Module 0: Introduction to Python
- Introduction to Python
- Running Python programs
- Basic syntax and indentation
- Printing and commenting code
- User input
- Exercises
- Solutions
Module 1: Variables and Data Types
- Variables and assignments
- Numeric data types (int, float)
- Strings and string manipulation
- Boolean data type
- Type conversion and casting
- Working with collections (lists, tuples, sets, dictionaries)
- Exercises
- Solutions
- Arithmetic operators
- Comparison operators
- Logical operators
- Assignment operators
- Bitwise operators
- Operator precedence
- Exercises
- Solutions
Module 3: Control Flow Statements
- Conditional statements (if, else, elif)
- Nested conditionals
- Logical operators in conditionals
- The ternary operator
- Exercises
- Solutions
- While loop
- For loop
- Loop control statements (break, continue)
- Nested loops
- Looping through collections
- Exercises
- Solutions
Module 5: Methods and Functions
- Defining and calling functions
- Variable scope and global keyword
- High order functions
- Lambda functions
- Exercises
- Solutions
- Milestone project grades
- Solution milestone project grades
- Milestone project calculator
- Solution milestone project calculator
Module 6: Object-Oriented Programming
- Introduction to OOP
- Classes and Objects
- Encapsulation and Abstraction
- Inheritance
- Polymorphism
- Exercises
- Solutions
- Milestone project bank
- Solution milestone project bank
- Handling common errors
- Understanding exceptions
- Handling exceptions using try-except blocks
- Raising exceptions
- Exercises
- Solutions
Module 8: Modules, Packages and Libraries
- Reading and writing text files
- Working with file objects
- File modes and file paths
- CSV and JSON file processing
- Error handling with files
- Exercises
- Solutions
Module 10: Additional Python Modules
- Dates and times manipulation (datetime module)
- Mathematical operations (math module)
- Generate random numbers (random module)
- Working with images (Pillow module)
- Zip files (zipfile module)
- Regular expressions (re module)
- Object serialization (pickle module)
- Exercises
- Solutions
- Milestone project online store
- Solution milestone project online store