This GitHub repository contains a number of beginner friendly Python tutorials covering installation and basic use of Python in markdown format. These guides cover the Python data model, Python standard libraries and third-party Python scientific libraries known as the numpy stack.
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These tutorials are in markdown format and GitHub displays the markdown as a webpage. Note some of the guides are screenshot intensive. On slow connections, the browser may timeout before all the images are downloaded. Downloaded images will be cached, refresh the page a couple of times to will continue downloading the remaining images.
The GitHub repository can also be downloaded and the readme.md can be opened in VSCode. Some of the styling in the markdown tables that are ignored on GitHub will render better in VSCode. This styling more closely resembles the Variable Explorer of the Spyder IDE.
The Ubuntu instructions can be modified slightly for another Linux distribution and should closely resemble installation on a Mac:
The following guide covers installation and basic use of the Scientific Python Development Environment (Spyder). Spyder is tailored for scientists and engineers and has the most commonly used packages from the scientific stack preinstalled. This makes it very begineer friendly.
This installation guide will also cover installation of additional packages using Miniconda to create a conda-forge (community channel) environment and additional dependencies such as TeX (commonly used in plots), which do not have a conda-forge package.
The Ubuntu instructions can be modified slightly for another Linux distribution and should closely resemble installation on a Mac:
Preference of IDE is somewhat subjective. The remaining tutorials are in markdown format, and can be used in any other Python IDE that has an ipython console such as JupyterLab and VSCode (when VSCode is configured for Python).
These tutorials cover the object orientated design pattern of builtins classes, focusing on text datatypes, numeric datatypes and collection datatypes. The object orientated design pattern is known as the Python Data Model:
- Object Orientated Programming (OOP) and the Python Data Model
- OOP and Text Datatypes
- OOP and Numeric Datatypes
- OOP and Collection Datatypes
These tutorials cover the numeric Python stack, which bridge a numeric design pattern with a collection design pattern:
- Numeric Python Library (numpy)
- Matrix Plotting Library (matplotlib)
- [Python and Data Analysis Library (pandas)]
- [Data Visualisation Library (seaborn)]
Tutorial on markdown syntax: