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 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) […]
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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
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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
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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.
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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.
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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,
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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
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In this Pandas tutorial, we will learn how to work with Excel files (e.g., xls) in Python. It will provide an overview of how to use Pandas to load xlsx files and write spreadsheets to Excel. In the first section, we will go through, with examples, how to use Pandas read_excel to; 1) read an
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Learn three data manipulation techniques with Pandas in this guest post by Harish Garg, a software developer and data analyst, and the author of Mastering Exploratory Analysis with pandas. Modifying a Pandas DataFrame Using the inplace Parameter In this section, you’ll learn how to modify a DataFrame using the inplace parameter. You’ll first read a real dataset into
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In this brief Python data analysis tutorial, we will learn how to carry out repeated measures ANOVA in Python using Statsmodels. More specifically, we will learn how to use the AnovaRM class from statsmodels anova module.
Repeated Measures ANOVA in Python using Statsmodels Read More »