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Author: Erik Marsja

PhD Student in Psychology, Umeå University. Main interest is experimental and cognitive psychology. Enjoy programming in Python and R.

9 Data Visualization Techniques You Should Learn in Python

With ever increasing volume of data, it is impossible to tell stories without visualizations. Data visualization is an art of how to turn numbers into useful knowledge. Using Python we can learn how to create data visualizations and present data in Python using the Seaborn package.

In this post we are going to learn how to use the following 9 plots:

  1. Scatter Plot
  2. Histogram
  3. Bar Plot
  4. Time Series Plot
  5. Box Plot
  6. Heat Map
  7. Correlogram
  8. Violin Plot
  9. Raincloud Plot

Python Data Visualization Tutorial: Seaborn

As previously mentioned in this Python Data Visualization tutorial we are mainly going to use Seaborn but also Pandas,  and Numpy. However, to create the Raincloud Plot we are going to have to use the Python package pititprince.

Python Raincloud Plot using the ptitprince package

Probabilistic Programming in Python

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.

OpenSesame Tutorial: Using Image Stimuli

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.

Python Pandas Groupby 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.

Exploratory Data Analysis in Python Using Pandas, SciPy, and Seaborn

9 Data Visualization Techniques You Should Learn in PythonIn this post we are going to learn how  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 using Python Pandas. Furthermore, we are going to explore the scraped data by grouping it and by Python 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. In this post, however, 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.

Pandas Read CSV Tutorial

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.

How to use Pandas Sample to Select Rows and Columns

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.

Data Manipulation with Pandas: A Brief Tutorial

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.

Repeated Measures ANOVA in Python using Statsmodels

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.