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The third introductory tutorial of our series \IeC {\textquotedblleft }Python for Researchers in Psychology\IeC {\textquotedblright } aims to teach researchers about the importance of functions and libraries. First, we introduce the concept of functions. We cover the advantages of functions and how to use them with the help of basic examples, including a paired-samples (dependent-samples) t-test. Then, libraries and their included functions are discussed, including how to import them and the functionality of some of the most popular libraries for researchers in psychology. Finally, a longer, more complex example shows how functions and libraries can help accelerate your research, statistical analyses, and data visualization.

This tutorial is the second in our series and covers basic syntax in Python with examples related to psychology. The aim is to teach programming beginners and experts alike the fundamentals required in order to smooth the learning curve and succeed with integrating Python with their research. It starts by covering basic built-in functions and variable creation. Next, different data types and data structures that you will encounter are covered detail, followed by comments and best commenting practices. Finally, indentation, logic, conditional statements, and loops are all explained with simple, illustrative examples. The tutorial ends with a comprehensive example of the same-different task from cognition that ties together everything learned. With this foundation, the reader will gain the confidence to begin practicing Python on their own and think of ways to incorporate it into their own research and daily lives.

In the era of advanced computers and state-of-the-art software, there have been great strides in the domains of research and engineering. These advancements have been made possible through the popularity, accessibility, and integration of computer programming. This has made automating tasks, analysing results through complex statistical analysis, and the development of more advanced artificial intelligence possible. However, in psychology, computer programming is still being underutilized. A major factor contributing to this is the steep learning curve for programming that is compounded by a lack of tutorials and knowledge specifically geared towards psychology. Thus, the purpose of this series is to bridge this gap and lower the learning barrier for researchers. This series invites anyone to send their own tutorials and contribute towards building a comprehensive guide to programming for psychological research in one of the most versatile languages: Python! For submission details, please see the guidelines below.

Python has become one of the most popular programming languages for research in the past decade. Its free, open-source nature and vast online community are some of the reasons behind its success. Countless examples of increased research productivity due to Python can be found across a plethora of domains online, including data science, artificial intelligence and scientific research. This tutorial\IeC {\textquoteright }s goal is to help users get started with Python through the installation and setup of the Anaconda software. The goal is to set users on the path toward using the Python language by preparing them to write their first script. This tutorial is divided in the following fashion: a small introduction to Python, how to download the Anaconda software, the different content that comes with the installation, and a simple example related to implementing a Python script.