In this introductory chapter, we will explore the aspects of Python in order to judge its suitability as a programming language in finance. Notably, Python is widely practiced in various financial sectors, such as banking, investment management, insurance, and even in real estate for building tools that help in financial modeling, risk management, and trading. To help you get the most from the multitude of features that Python has to offer, we will introduce the IPython Notebook as a beneficial tool to help you visualize data and to perform scientific computing for presentation to end users.
In this chapter, we will cover the following topics:
Benefits of Python over other programming languages for financial studies
Features of Python for financial applications
Implementing object-oriented design and functional design in Python
Overview of IPython
Getting IPython and IPython Notebook started
Creating and saving notebook documents
Various formats to export a notebook document
Notebook document user interface
Inserting Markdown language into a notebook document
Performing calculations in Python in a notebook document
Creating plots in a notebook document
Various ways of displaying mathematical equations in a notebook document
Inserting images and videos into a notebook document
Working with HTML and pandas DataFrame in a notebook document