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.
This post explains how to use pandas to drop columns by name from one or multiple DataFrames. We demonstrate dropping single and multiple columns, as well as how to conditionally remove columns based on name patterns. These techniques are essential for data cleaning and preparation in Python using Pandas.
Pandas: Drop Columns By Name in DataFrames Read More »
Where did the USAID nutrition pack go? Using Python, we tracked down its exact location by extracting GPS coordinates from a photo. In this post, you’ll learn how to pull location data from images and plot it on a map—step by step. A small mystery solved with code and a curious eye.
How to Extract GPS Coordinates from a Photo: The USAID Mystery Read More »
When working with data in Python, using Pandas to read JSON from URL is an excellent tool that lets you directly load JSON data from a web source into a Pandas dataframe. This tutorial focuses on the steps to accomplish this task, building upon our previous discussions on reading JSON with Python more generally.
Using Pandas to Read JSON from URL Read More »
Here you will learn by examples how to use Pandas to calculate cumulative sum by group.
Pandas: Cumulative Sum by Group Read More »
In this comprehensive tutorial, explore the powerful methods to convert all columns to strings in Pandas, ensuring data consistency and optimal analysis. Learn to harness the versatility of Pandas with ease.
Pandas Convert All Columns to String: A Comprehensive Guide Read More »
Learn all about multicollinearity with Python! This guide explores Variance Inflation Factor (VIF) using statsmodels and scikit-learn. Break down the complexity of real-world data analysis, and elevate your regression skills to the next level.
Variance Inflation Factor in Python: Ace Multicollinearity Easily Read More »
Unlock the power of Pandas! Discover the art of combining year and month columns in your data. Seamlessly organize, analyze, and visualize your time-based datasets. Elevate your data manipulation skills and supercharge your insights. Dive into our Pandas tutorial to become a data wizard!
Combine Year and Month Columns in Pandas Read More »
Discover Seaborn’s power in creating insightful confusion matrix plots. Unleash your data visualization skills and assess model performance effectively.
Seaborn Confusion Matrix: How to Plot and Visualize in Python Read More »
Learn how to use Python to check if a file is empty. Here we use the os, glob, zipfile, and rarfile modules to check if 1) a file is empty, 2) many files are empty, and 3) files contained in Zip and Rar files are empty.
Python Check if File is Empty: Data Integrity with OS Module Read More »
Discover the Coefficient of Variation in Python using NumPy and Pandas. Pearn to find data variability in your data effortlessly!
Coefficient of Variation in Python with Pandas & NumPy Read More »