Erik Marsja

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 of the book that I have slightly adapted so it’s easier to read without having read the first chapter.

In this OpenSesame tutorial we will learn how to use images as stimuli and how to load the trials; including filenames, correct responses, and conditions from a pre-generated CSV file. To follow this tutorial you don’t need to know Python programming. However, we are going to generate the CSV file using a short Python script. This can be done manually, of course. See also this OpenSesame tutorial.

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 observations in each group, and many more useful things.

First of all we are going to import pandas as pd, and read a CSV file, using the read_csv method, to a dataframe. In the example below, we use index_col=0 because the first row in the dataset is the index column.

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 specifically, we will learn how to count missing values, group data to calculate the mean, and then visualize relationships between two variables, among other things.

In previous posts we have used Pandas to import data from Excel and CSV files. Here we are going to use Pandas read_html because it has support for reading data from HTML from URLs (https or http). To read HTML Pandas use one of the Python libraries LXML, Html5Lib, or BeautifulSoup4. This means that you have to make sure that at least one of these libraries are installed. In the specific Pandas read_html example here, we use BeautifulSoup4 to parse the html tables from the Wikipedia article.

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 file, how to read specific columns from a CSV, how to read multiple CSV files and combine them to one dataframe, and, finally, how to convert data according to specific datatypes (e.g., using Pandas read_csv dtypes). In the last section we will continue by learning how to write CSV files. That is, we will learn how to export dataframes to CSV files.

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 when carrying out bootstrapping or cross-validation. Here we will learn how to; select rows at random, set a random seed, sample by group, using weights, and conditions, among other useful things.

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 Pandas. You’ll then see how the inplace parameter impacts a method execution’s end result. You’ll also execute methods with and without the inplace parameter to demonstrate the effect of inplace.

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 get these Python packages installed is to install a Python distribituion such as Anaconda (see this YouTube Video on how to install Anaconda). However, if you already have Python installed you can of course use Pip.

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 to subset and group our dataframe. If you are not familiar with installing Python packages I have recorded a YouTube video explaining how to install Pandas. There’s also a playlist with videos towards the end of the post with videos of all topics covered in this post.