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!
Here you will learn how to conduct repeated measures Analysis of Variance (ANOVA) in R and Python. More specifically, you will learn how to use the r-package afex and the Python package pingouin to carry out ANOVA.
Repeated Measures ANOVA in R and Python using afex & pingouin Read More »
Data visualization is a big part of the process of data analysis. In this post, we will learn how to make a scatter plot using Python and the package Seaborn. In detail, we will learn how to use the Seaborn methods scatterplot, regplot, lmplot, and pairplot to create scatter plots in Python. More specifically, we
How to Make a Scatter Plot in Python using Seaborn Read More »
In this Python tutorial, we will learn how to read and write JSON files using Python. In the first part, we are going to use the Python package json to create and read a JSON file as well as write a JSON file. After that, we are going to use Pandas read_json method to load
How to Read and Write JSON Files using Python and Pandas Read More »
In this post, we will learn how to create a binder so that our data analysis, for instance, can be fully reproduced by other researchers. That is, here we will learn how to use a tool called Binder for reproducible research. In previous posts, we have learned how to carry out data analysis (e.g., ANOVA)
How to Use Binder and Python for Reproducible Research Read More »
With the ever-increasing volume of data, it is impossible to tell stories without visualizations. Data visualization is an art of how to turn numbers into useful knowledge. Using Python we can learn how to create data visualizations and present data in Python using the Seaborn package. In this post we are going to learn how
9 Data Visualization Techniques You Should Learn in Python Read More »
Learn about probabilistic programming in this guest post by Osvaldo Martin, a researcher at The National Scientific and Technical Research Council of Argentina (CONICET) and author of Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition. This post is based on an excerpt from the second chapter
Probabilistic Programming in Python Read More »
Are you looking to learn how to manipulate and analyze data using Pandas? Then you have come to the right place! In this blog post, we will explore the power of the groupby method in Pandas allows us to group data based on one or more columns, and then perform various operations on each group.
Python Pandas Groupby Tutorial Read More »
In this Python data analysis tutorial, we are going to learn how to carry out exploratory data analysis using Python, Pandas, and Seaborn. The data we are going to explore is data from a Wikipedia article. In this post, we are actually going to learn how to parse data from a URL using Python Pandas.
Exploratory Data Analysis in Python Using Pandas, SciPy, and Seaborn Read More »
This Pandas tutorial will show you, by examples, how to use Pandas read_csv() method to import data from .csv files. In the first section, we will go through how to read a CSV file, how to read specific columns from a CSV, how to read multiple CSV files and combine them to one dataframe. Finally,
Pandas Read CSV Tutorial: How to Read and Write Read More »
In this Pandas tutorial, we will learn how to use Pandas to randomly select rows and columns from a dataframe. There are some reasons for randomly sampling our data; for instance, we may have a very large dataset and want to build our models on a smaller sample. Other examples are when carrying out bootstrapping
How to use Pandas Sample to Select Rows and Columns Read More »