Learning how to interact with the operating system using a command-line interface (or Terminal) is a required skill in interactive computing and data analysis. We will use a command-line interface in most of the recipes in this book. IPython and the Jupyter Notebook are typically launched from a Terminal. Installing Python packages is typically done from a Terminal.

In this recipe, we will show the very basics of the Unix shell, which is natively available in Linux distributions (such as Debian, Ubuntu, and so on) and macOS. On Windows 10, one can install the Windows Subsystem for Linux, a command-line interface to a Unix subsystem integrated with the Windows operating system (see https://docs.microsoft.com/windows/wsl/about).

The latest version of the Python 2.x branch, Python 2.7, was released in 2010. It will reach its end of life in 2020. On the other hand, the first version of the Python 3.x branch, Python 3.0, was released in 2008. The decade-long transition period between Python 2 and Python 3, which are slightly incompatible, has been somewhat chaotic.

Choosing between Python 2 (also known as Legacy Python) and Python 3 used to be tricky since many Python users had not transitioned to Python 3 yet, and many libraries were only compatible with Python 2. Those times are gone and it is now safe to stick with Python 3 in virtually all cases. The only exceptions are when you have to support old unmaintained libraries, or when your users cannot transition to Python 3 for whatever reason.

In addition to fixing the bugs and annoyances of Python 2 (for example, related to Unicode support), Python 3 brings many interesting features in terms of syntax, capabilities of the language...

Using a version control system is an absolute requirement in programming and research. This is the tool that makes it just about impossible to lose one's work. In this recipe, we will cover the basics of Git.

A distributed version control system such as Git is designed for the complex and nonlinear workflows that are typical in interactive computing and exploratory research. A central concept is branching, which we will discuss in this recipe.

There are multiple ways of using IPython for interactive computing. Some of them are better in terms of flexibility, modularity, reusability, and reproducibility. We will review and discuss them in this recipe.

Any interactive computing workflow is based on the following cycle:

This fundamental loop (also known as Read-Eval-Print Loop (REPL)) is particularly useful when doing exploratory research on data or model simulations, or when building a complex algorithm step by step. A more classical workflow (the edit-compile-run-debug loop) would consist of writing a full-blown program, and then performing a complete analysis. This is generally more tedious. It is more common to build an algorithmic solution iteratively, by doing small-scale experiments and tweaking the parameters, and this is precisely what interactive computing is about.

Integrated Development Environments (IDEs), providing...

In this recipe, we present ten tips that can help you conduct efficient and reproducible interactive computing experiments. These are more guidelines than absolute rules.

First, we will show how you can improve your productivity by minimizing the time spent doing repetitive tasks and maximizing the time spent thinking about your core work.

Second, we will demonstrate how you can achieve more reproducibility in your computing work. Notably, academic research requires experiments to be reproducible so that any result or conclusion can be verified independently by other researchers. It is not uncommon for errors or manipulations in methods to result in erroneous conclusions that can have damaging consequences. For example, in the 2010 research paper in economics Growth in a Time of Debt, by Carmen Reinhart and Kenneth Rogoff, computational errors were partly responsible for a flawed study with global ramifications for policy...