First, we will rename a single file in 4 easy steps. After that, we will learn how to rename multiple files using Python 3. To be able to change the name of multiple files using Python can come in handy. For example, if we have a bunch of data files (e.g., .csv files) with long, or strange names, we may want to rename them to make working with them easier later in our projects (e.g., when loading the CSV files into a Pandas dataframe).
Python programming related stuff
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 short post, we will learn how to save Seaborn plots to a range of different file formats. More specifically, we will learn how to use the plt.savefig method save plots made with Seaborn to:
- Portable Network Graphics (PNG)
- Portable Document Format (PDF)
- Encapsulated Postscript (EPS)
- Tagged Image File Format (TIFF)
- Scalable Vector Graphics (SVG)
In this post, we will learn how to use Pandas drop_duplicates() to remove duplicate records and combinations of columns from a Pandas dataframe. That is, we will delete duplicate data and only keep the unique values.
This Pandas tutorial will cover the following; what’s needed to follow the tutorial, importing Pandas, and how to create a dataframe fro a dictionary. After this, we will get into how to use Pandas drop_duplicates() to drop duplicate rows and duplicate columns.
In this tutorial, we are going to learn how to read a file in Python 3. After we have learned how to open a file in Python, we are going to learn how to write to the file and save it again. In previous posts, we have learned how to open a range of different files using Python. For instance, we have learned how to open JSON, CSV, Excel, and HTML files using Pandas, and the json library. Here, however, we are going to open plain files (.txt) in Python.
In this post, we will learn how to use Pandas get_dummies() method to create dummy variables in Python. Dummy variables (or binary/indicator variables) are often used in statistical analyses as well as in more simple descriptive statistics. Towards the end of the post, there’s a link to a Jupyter Notebook containing all Pandas get_dummies() examples.
Dummy Coding for Regression Analysis
One statistical analysis in which we may need to create dummy variables in regression analysis. In fact, regression analysis requires numerical variables and this means that when we, whether doing research or just analyzing data, wishes to include a categorical variable in a regression model, supplementary steps are required to make the results interpretable.
In this short tutorial, we will learn how to change the figure size of Seaborn plots. For many reasons, we may need to either increase the size or decrease the size, of our plots created with Seaborn.
When do We Need to Change the Size of a Plot?
One example, for instance, when we might want to change the size of a plot could be when we are going to communicate the results from our data analysis. In this case, we may compile the descriptive statistics, data visualization, and results from data analysis into a report, or manuscript for scientific publication.
Here, we may need to change the size so it fits the way we want to communicate our results. Note, for scientific publication (or printing, in general) we may want to also save the figures as high-resolution images.
In this tutorial, we will learn the basics of installing, working and updating packages in Python. First, we will learn how to install Python packages, then how to use them, and finally, how to update Python packages when needed. More specifically, we are going to learn how to install and upgrade packages using pip, conda, and Anaconda Navigator.
In this post, we are going to learn how to read Stata (.dta) files in Python.
As previously described (in the read .sav files in Python post) Python is a general-purpose language that also can be used for doing data analysis and data visualization. One example of data visualization will be found in this post.
One potential downside, however, is that Python is not really user-friendly for data storage. This has, of course, lead to that our data many times are stored using Excel, SPSS, SAS, or similar software. See, for instance, the posts about reading .sav, and sas files in Python:
When a program becomes very long and complex, it is convenient to divide it into subroutines, each of which implements a specific task. However, subroutines cannot be executed independently, but only at the request of the main program, which is responsible for coordinating the use of subroutines.
In this post, we introduce a generalization of the concept of subroutines, known as coroutines: just like subroutines, coroutines compute a single computational step, but unlike subroutines, there is no main program to coordinate the results. The coroutines link themselves together to form a pipeline without any supervising function responsible for calling them in a particular order.