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 Pandas tutorial, we will go through the steps on how to use Pandas read_html method for scraping data from HTML. First, in the simplest example, we are going to use Pandas to read HTML from a string. Second, we are going to go through a couple of examples in which we scrape data from Wikipedia tables with Pandas read_html. In a previous post, about exploratory data analysis in Python, we also used Pandas to read data from HTML tables.
In this Python data visualization tutorial, we will work with Pandas scatter_matrix method to explore trends in data. Previously, we have learned how to create scatter plots with Seaborn and histograms with Pandas, for instance. In this post, we’ll focus on scatter matrices (pair plots) using Pandas. Now, Pandas is using Matplotlib to make the scatter matrix.
In this Python tutorial, we will learn how to get the absolute value in Python. First, we will use the function abs() to do this. In this section, we will go through a couple of examples of how to get the absolute value. Second, we will import data with Pandas and use the abs method to get the absolute values in a Pandas dataframe.
For those of us who prefer audio-visual tutorials, there’s also a YouTube video explaining the content of this absolute value in Python tutorial (check the end of the post).
In this short Python Pandas tutorial, we will learn how to convert a Pandas dataframe to a NumPy array. Specifically, we will learn how easy it is to transform a dataframe to an array using the two methods values and to_numpy, respectively. Furthermore, we will also learn how to import data from an Excel file and change this data to an array.
In this brief Python Pandas tutorial, we will go through the steps of creating a dataframe from a dictionary. Specifically, we will learn how to convert a dictionary to a Pandas dataframe in 3 simple steps. First, however, we will just look at the syntax. After we have had a quick look at the syntax on how to create a dataframe from a dictionary we will learn the easy steps and some extra things. In the end, there’s a YouTube Video and a link to the Jupyter Notebook containing all the example code from this post.
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…
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, at the end of this post there’s a YouTube tutorial explaining the simple steps to plot a Histogram with Pandas.
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 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).
