A Professional’s Tutorial to Simple Python Functions

Functional programming, Python terms and concepts

Adam Ross Nelson
5 min readSep 17, 2023

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For an overview of these tutorials, click here (or click the image).

Welcome to sixth, in a series of tutorials that teach beginner Python specifically for aspiring data scientists. For an overview of these tutorials, click here.

Introduction

The vibrant world of data science with its vast set of algorithms, tools, and statistical techniques, relies on the elegance of Python’s simplicity.

Some feel however that Python’s charm is deceptive — behind the façade of its straightforward (readable) syntax lies an engine that powers complex analyses. There are layers and layers of complexity. In this tutorial we peel another layer for a look at functions.

A pencil that has drawn a sin-like function on an x and y coordinate system.
Image Credit: Author’s illustration created in Canva.com.

At, or very nearly at, the heart of Python is the def statement. This statement is the syntax that defines a function. This unassuming three-letter word unlocks Python's potential, allowing you to encapsulate logic, streamline repetitive tasks, and breathe life into your code.

Through this tutorial on Python functions, you'll discover how this foundational tool can not only transform your coding experience but also become an indispensable ally in your data science journey.

Abstract

Python stands as a predominant language in the realm of data science, owing to its simplicity and vast library ecosystem. This tutorial continues a sequence of tutorials aimed at helping aspiring data scientists learn Python. For professionals versed in data but new to Python, understanding the fundamental aspect of functions is crucial.

This tutorial helps learners examine the foundational concepts surrounding Python functions, from their definition, invocation, and use of parameters. Also examined here are advanced features including anonymous lambda expressions.

By grasping these basics, data experts (including aspiring data scientists) can streamline data analysis tasks and enhance the efficiency and readability of their code. All of these benefits of functional programming ultimately lead…

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Adam Ross Nelson

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