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!
In this Pandas group by we are going to learn how to organize Pandas dataframes by groups. More specifically, we are going to learn how to group by one and multiple columns. Furthermore, we are going to learn how calculate some basics summary statistics (e.g., mean, median), convert Pandas groupby to dataframe, calculate the percentage of […]
In this post we are going to learn to explore data 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, exploring this data by grouping it and data visualization. More […]
In this tutorial we will learn how to work with comma separated (CSV) files in Python and Pandas. We will get an overview of how to use Pandas to load CSV to dataframes and how to write dataframes to CSV. In the first section, we will go through, with examples, how to read a CSV […]
In this tutorial we will learn how to use Pandas sample to randomly select rows and columns from a Pandas dataframe. There are some reasons for randomly sample our data; for instance, we may have a very large dataset and want to build our models on a smaller sample of the data. Other examples are […]
In this tutorial we will learn how to work with Excel files and Python. It will provide an overview of how to use Pandas to load and write spreadsheets to Excel. In the first section, we will go through, with examples, how to read an Excel file, how to read specific columns from a spreadsheet, […]
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 […]
In this brief Python data analysis tutorial we will learn how to carry out a repeated measures ANOVA using Statsmodels. More specifically, we will learn how to use the AnovaRM class from statsmodels anova module. To follow this guide you will need to have Python, Statsmodels, Pandas, and their dependencies installed. One easy way to […]
In this Pandas tutorial we will learn how to work with Pandas dataframes. More specifically, we will learn how to read and write Excel (i.e., xlsx) and CSV files using Pandas. We will also learn how to add a column to Pandas dataframe object, and how to remove a column. Finally, we will learn how […]
In this short post I will show you a quick fix for the error “unsupported operand type(s) for +: ‘float’ and ‘NoneType’” with Pyvttbl. In earlier posts I have showed how to carry out ANOVA using Pyvttbl (among other packages. See posts 1, 2, 3, and 4 for ANOVA using pyvttbl). However, Pyvttbl is not […]
In this post, PyCharm vs Spyder will be compared. If you have followed my blog you may have noticed that a lot of focus have been put on how to learn programming (particularly in Python). I have also written about Integrated Development Environments (IDEs). I think that an IDE may, in fact, be very useful […]