Python can be run in many ways and common methods include running python scripts using a terminal or using the python shell. With data analysis/science making the news these days, we have ipython based jupyter notebooks that are being used by beginners and experts alike.

Ipython provides a REPL (Read-Evaluate-Print-Loop) shell for interactive Python development. It enables us to visualize the charts and plots using GUI toolkits and provides a kernel for jupyter.

Pandas is a powerful and flexible Python package that allows you to work with labeled and time series data. It also provides statistics methods, enables plotting, and more. One crucial feature of Pandas is its ability to write and read Excel, CSV, and many other types of files. Functions like the Pandas read_csv() method enable you to work with files effectively. You can use them to save the data and labels from Pandas objects to a file and load them later as Pandas Series or DataFrame instances.

A curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.