This repository contains code examples and exercises for learning intermediate-level Python programming. The materials are aimed at learners who have already mastered the basics of Python and want to further develop their skills.
To use the materials in this repository, you should have Python 3 installed on your computer. You can download the latest version of Python from the official website.
Once you have Python installed, you can clone this repository to your local machine using Git:
git clone https://github.com/farhadlotfii/python-basics.git
Alternatively, you can download a ZIP archive of the repository and extract it to a local folder.
The repository contains the following directories:
notebooks: Jupyter notebooks with code examples and exercises.data: Sample datasets used in the notebooks.
You can run the Jupyter notebooks by starting a Jupyter server in the repository directory:
jupyter notebook
This should open a web browser with the Jupyter interface. You can then navigate to the notebooks directory and open the desired notebook.
The notebooks contain code examples and exercises that demonstrate various machine learning concepts and techniques. You can run the code cells in the notebooks by clicking on them and pressing Shift+Enter.
If you find a bug or have a suggestion for improving the materials, you can create an issue on the GitHub repository page. You can also submit a pull request if you want to contribute code changes.
This repository covers the following intermediate-level topics:
- Regular Expression
- Decorators
- Logging
- Iterable Objects
- Data Structure
- Comprehension
- Itertools
- Decorator
- Magic Functions
- Object Oriented Programming (OOP)
Each of these topics is explained in detail through code examples and exercises in the examples and exercises directories. The examples demonstrate how to use the various Python concepts and features, while the exercises provide hands-on practice for applying the concepts in different scenarios.
By completing the examples and exercises, you should gain a deeper understanding of the Python language and be able to write more complex and efficient Python programs for various tasks.