Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success-and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, clearly explaining how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics you need (and that are often missing from introductory data science or coding classes), including how to:Understand data structures and object-oriented programmingClearly and skillfully document your codePackage and share your codeIntegrate data science code with a larger codebaseWrite APIsCreate secure codeApply best practices to common tasks such as testing, error handling, and loggingWork more effectively with software engineersWrite more efficient, maintainable, and robust code in PythonPut your data science projects into productionAnd more
