In the posts in this category you will find Python scripts. Python is said to be one of the easiest programming language to learn. Learning one language will also make it easier to learn another, much more advanced, one. As a Bachelor student in the cognitive science programme I got to take Python courses. However, it was not before I started my Ph.D years that I realized how much use I had because I knew some programming.
For a psychology researcher Python might be ideal since it is relatively easy to learn and there is a huge Python community to get help from. How to build experiments using free and open-source tools such as PsychoPy, OpenSesame, Expyriment, and PyEPL is, for instance, something you could find in this category.
In this Python data analysis tutorial, we will focus on how to carry out between-subjects ANOVA in Python. As mentioned in an earlier post (Repeated measures ANOVA with Python) ANOVAs are commonly used in Psychology. We start with a brief introduction to the theory of ANOVA. If you are more interested in the four methods […]
Four Ways to Conduct One-Way ANOVA with Python Read More »
In this post, you will learn how to carry out a repeated measures ANOVA in Python. A standard method in experimental psychology is within-subjects designs. One way to analysis the data collected using within-subjects designs is using repeated measures ANOVA. I recently wrote a post on how to conduct a repeated measures ANOVA using Python
Repeated measures ANOVA using Python Read More »
in this Python and Pandas tutorial, you will learn how to calculate descriptive statistics in Python using Pandas. First, you will get a brief description of what descriptive statistics is. After that, you will get a quick answer to a the question “how can I calculate descriptive statistics in Python”. In the next subsections, we
Descriptive Statistics in Python using Pandas Read More »
Learn how to reverse pandas dataframe by rows or columns.
Six Ways to Reverse Pandas dataframe Read More »
Spyder is the best Python IDE that I have tested so far for doing data analysis, but also for plain programming. In this post I will start to briefly describe the IDE. Following the description of this top IDE the text will continue with a discussion of my favorite features. You will also find out how to
Why Spyder is the Best Python IDE for Science Read More »
In this post I will discuss two Python Integrated Development Environments (IDE); Rodeo and Spyder. Both Python IDEs might be useful for researchers used to work with R and RStudio (a very good and popular IDE for R) because they offer similar functionalities and graphical interfaces as RStudio. That is, Rodeo and Spyder can both
RStudio-like Python IDEs – Rodeo and Spyder Read More »
In this tutorial, you will learn how to use the PsychoPy function TrialHandler to create trials and correct responses to your targets in these trials. PsychoPy is an application for creating experiments for Psychology experiments. The application is written in Python, an easy programming language to learn. You can learn more about PsychoPy in my
TrialHandler – a PsychoPy tutorial Read More »
This tutorial covers the integration between R and Python using the rpy2 package. Discover how to bridge the gap between these two languages, enabling you to harness both strengths for enhanced data analysis and visualization. This rpy2 tutorial guides you through its installation, configuration, and practical usage. Outline In this tutorial, we will learn how
R from Python – an rpy2 tutorial Read More »
In this post you will find some really good Python Blogs and Resources. Most of these really helped me when moving from proprietary software to free and Open Source software (e.g., PsychoPy written in Python). The links are divided into two categories: general and research. In the ‘general’ category you will find good Python resources
Some good Python Blogs and Resources Read More »
In this post, I will describe the existing free Python applications and libraries for creating experiments. So far, I have only used PsychoPy, but I plan to test most of them. At least the ones that seem to be still maintained. All applications and libraries are open-source, which makes it possible to download the source
Python apps and libraries for creating experiments Read More »
