This repository contains a series of Jupyter notebooks designed to help people (including me) familiar with other programming languages, such as MATLAB, learn Python. The examples and explanations focus on making Python accessible for learners by drawing comparisons with concepts they might already know from these languages.
(TBD)
Each example is explained in a step-by-step manner, with detailed comments to make the code more accessible to Python beginners.
- Comparisons to MATLAB: The notebooks provide explanations of how Python's syntax and functions differ from those in MATLAB, making it easier for learners to relate.
- Hands-on practice: Users can modify and run the provided code in a Jupyter Notebook environment, gaining experience with Python by exploring practical examples.
- Accessible explanations: Focuses on making Python’s concepts approachable for those coming from other programming backgrounds.
- Clone this repository or download the notebooks.
- Run the notebooks using Jupyter Notebook, or use any online platform such as Google Colab to open
.ipynbfiles. - Explore the examples, modify the code, and get a feel for Python's syntax and functions.
This project is aimed at learners who are new to Python but already have experience with MATLAB. It provides practical coding examples while pointing out similarities and differences between these languages and Python.
- Factorials (exact and approximate calculations)
- Binomial coefficients
- Probability mass functions (PMF)
- Using logarithmic functions for handling large values
- NumPy introduction