In this brief Python tutorial, we are going to learn how to read Excel (xlsx) files using Python. Specifically, we will read xlsx files in…
Category: Programming
Programming related posts
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 short post, we will learn 6 methods to get the column names from Pandas dataframe. One of the nice things about Pandas dataframes is that each column will have a name (i.e., the variables in the dataset). Now, we can use these names to access specific columns by name without having to know which column number it is.
To access the names of a Pandas dataframe, we can the method columns(). For example, if our dataframe is called df we just type print(df.columns) to get all the columns of the pandas dataframe.
In this post, we are going to learn how to plot histograms with Pandas in Python. Specifically, we are going to learn 3 simple steps to make a histogram with Pandas. Now, plotting a histogram is a good way to explore the distribution of our data.
Note, in the end of this post there’s a YouTube tutorial explaining the simple steps to plot a Histogram with Pandas.
Prerequisites
First of all, and quite obvious, we need to have Python 3.x and Pandas installed to be able to create a histogram with Pandas. Now, Python and Pandas will be installed if we have a scientific Python distribution, such as Anaconda or ActivePython, installed. On the other hand, Pandas can be installed, as many Python packages, using Pip: pip install pandas.
In this post, we are going to learn how to read Stata (.dta) files in R statistical environment. Specifically, we will learn 1) who to import .dta files in R using Haven, and 2) how to write dataframes to .dta file.
Data Import in R: Reading Stata Files
Now, R is, as we all know, a superb statistical programming environment. When it comes to importing and storing data, we can store our data in the native .rda format. However, if we have a collaborator that uses other statistical software (e.g., Stata) and/or that are storing their data in different formats (e.g., .dta files).
Now, this is when R shows us its brilliance; as an R user we can load data from a range of file formats; e.g., SAS (.7bdat), Stata (.dta), Excel (e.g., .xlsx), and CSV (.csv). On this site there are other tutorials on how to import data from (some) of these formats:
- How to Import SAS files in R
- Reading and writing SPSS files in R
- How to read, and write, Excel (.xslx) files in R – e.g., multiple sheets
Before we go on and learn how to read SAS files in R, we will answer the questions:
In this guest post, you will learn by example how to do two popular machine learning techniques called random forest and extremely random forests. In fact, this post is an excerpt (adapted to the blog format) from the forthcoming Artificial Intelligence with Python – Second Edition: Your Complete Guide to Building Intelligent Apps using Python 3.x and TensorFlow 2. Now, before you will learn how to carry out random forests in Python with scikit-learn, you will find some brief information about the book.
In this post, we are going to work with Python 3 to rename files. Specifically, we will use the Python module os to rename a file and rename multiple files.
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).
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 remove a column in R tutorial, we are going to work with dplyr to delete a column. Here, we are going to learn how to remove columns in R using the select() function. Specifically, we are going to remove columns by name and by index.
Finally, we will also learn how to remove columns from R dataframes that start with a letter, or a word ends with a letter, or word, or contains a character (like the underscore).
