These guides will use the scientific python development environment abbreviated spyder as an Integrated Development Environment (IDE). This IDE has a user interface similar to matrix laboratory matlab and is one of the best IDEs for learning the fundamentals of python and usually the preferred IDE for data science. Spyder version 4.1.3 of later should be used as this version has a multitude of improvements over earlier versions.
The Spyder IDE is packaged alongside Python plus numerous other commonly used other python libraries in a installation package known as Anaconda. Anaconda also contains other IDEs such as Jupyter notebooks (but these guides focus on Spyder as an IDE).
I give full instructions on performing a clean installation of Anaconda below for different OS.
In this guide I discuss the Spyder 4 user interface and how to use the core python library. You should ensure you are comfortable with this before moving onto additional python libraries.
Core Python Libraries for Data Science
The numpy Library
The numeric python library abbreviated numpy is the most commonly used python library. It is used for array manipulation.
The pandas Library
The python and data analysis library abbreviated pandas is one of the most commonly used libraries in data science. This library allows for dataframe or spreadsheet manipulation or in other words is in essence the Excel of python.
The matplotlib Plotting Library
The matplotlib library is the python plotting and data visualization and is highly based on matlab/octave.
General User Interface (GUI)
The Python Quasar toolkit 5 (PyQt5)
The Python Quasar toolkit 5 library abbreviated as PyQt5 is used to create an interactive General User Interface (GUI). In this guide I reinforce the basics behind object orientated programming and then look at creating some basic GUIs.