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 4.1.1 of later should be used as this version has a multitude of improvements over earlier versions. I give full instructions on performing a clean installation of Anaconda below:
Object Orientated Programming
Before moving onto the core scientific libraries numpy, pandas and matplotlib it is worthwhile getting your head around object-oriented programming.
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 visualisation and is highly based on matlab/octave.
look at grid spec.
The scientific python Library
This library can be thought of as an extension to the numeric python library numpy.