# Programming

## Programming related posts

In this category you will find posts that are related to programming and should be interesting for psychologists, cognitive scientists, and neuroscientists. Well, almost every researcher would probably find some of the information useful at some time!

Every research psychologist, cognitive scientist, and neuroscientist, should know how to program.. Knowing how to program and write scripts will make many of a researchers everyday tasks much easier. For instance, instead of going through line after line of raw data you can write a Python script that runs through each cell in each column. Furthermore, you get the possibility to use more advanced, and cutting edge, statistical techniques by using R statistical programming environment.

Another example might be to create experiments using PsychoPy (either by coding using Python or using the drag-and-drop interface) and the cheap and open-source Arduino microcontroller. Also, coding is fun and relaxing!

## Three ways to do a two-way ANOVA with Python

In an earlier post, I showed four different techniques that enable a one-way analysis of variance (ANOVA) using Python. Now, in this Python data analysis tutorial, we are going to learn how to do two-way ANOVA for independent measures using Python. First, we are going to learn how to calculate the ANOVA table “by hand”. […]

## Four Ways to Conduct One-Way ANOVA with Python

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

## Repeated measures ANOVA using Python

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

## Descriptive Statistics in Python using Pandas

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

## Six Ways to Reverse Pandas dataframe

Learn how to reverse pandas dataframe by rows or columns.

## Why Spyder is the Best Python IDE for Science

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

## Every Psychologist Should Learn Programming

This post aims to show why you, as a psychology student or researcher (or any other kind of researcher or student) should learn to program. The post is structured as follows. First, I start by discussing why you should learn to program and then give some examples of when programming skills are helpful. I continue

## R Resources for Psychologists

Good resources for learning R as a Psychologist are hard to find. By that, I mean that there are so many great sites and blogs to learn R. Thus, it may be hard to find learning resources that target Psychology researchers. Outline The structure of this post is as follows. First, we start by getting

## Installing Rodeo IDE on Linux

I recently wrote a post on the RStudio like Python IDE Rodeo (RStudio-like Python IDEs – Rodeo and Spyder). In that post, I installed and tested Rodeo 0.44. However, Rodeo 1.0 was released in October. Rodeo 1.0 cannot be installed using Pip. Therefore, I wrote a bash script for downloading and unzipping Rodeo. Note, the

## RStudio-like Python IDEs – Rodeo and Spyder

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

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