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In this folder you will find Jupyter Notebooks containing code examples for many of my blogposts (https://www.marsja.se). There are also notebooks containing the code for some of my Videos on my YouTube Channel. The main theme of the blog posts, right now, is data visualization, manipulation, and analysis using Python. For instance, you will find Pandas tutorials, Seaborn tutorials, and Statsmodels tutorials (e.g., Python ANOVA).

Here are different Notebooks containing the code examples for some of the Pandas Tutorials:

• Reversing Pandas Dataframe

• Pandas Dataframe Tutorial: Working with Dataframes

• Python Pandas JSON Tutorial

• Data Cleansing With Pandas and Pyjanitor - In this notebook code examples on how to delete missing values, remove empty columns, clean column names (i.e., rename columns) and so on can be found.

• Read SPSS files in Python - Learn how to load .sav files into Pandas dataframes.

Here you will find Jupyter notebooks, both R and Python right now, that focuses on how to work with data in specific ways.

• How to Reverse Code in R - An R note book for learning how to reverse scores from a questionnaire using R and the package psych.

Note books for different Data Visualization Tutorials:

• Kernel Estimation, Cumulative Distribution Function, Delta Plot, & Conditional Accuracy Function: Different data visualization techniques for response time data

• Python Data Visualization Tutorial: How to Create a Scatter Plot

• How to Make Scatter Plots using Python - Here we work with the following Seaborn methods: scatterplot, regplot, lmpot, and pairplot. We learn how to make scatter plots with regression lines, change the color and markers of Seaborn plots, rotate the ticks labels, and much more.

• Raincloud Plots in Python Here's the example code from the Python data visualization tutorial on how to make raincloud plots in Python (YouTube Video)

• Scatterplots in R using ggplot2 - Notebook containing code examples on how to create scatter plots using ggplot2.

For some data analysis techniques we may want to use R. This because it may not yet be implemented in Python. Here are some Jupyter Notebooks for one Python package called Rpy2:

• Rpy2 & R-plots in Jupyter Notebooks (See the YouTube Video)

• Rpy2 Tutorial: Example

In this section we find the code examples, in form of Jupyter Notebooks, for the Python data analysis tutorials;

Here, there are some Jupyter Notebooks for carrying out summary statistics using Python, Pandas, Scipy, and other useful Python packages

• Descriptive Statistics in Python

Here we have Python ANOVA Notebooks. There is also one Notebook for carrying out ANOVA using R.

• One-Way ANOVA using Pingouin Stats

• One-Way ANOVA using Scipy and Statsmodels

• Two-Way ANOVA using Python and SciPy

• Two-Way ANOVA using Pingouin Stats

• Two-Way ANOVA using Statsmodels

• Repeated Measures ANOVA using Statsmodels
• Repeated Measures ANOVA in R: afex code example

• One-Way MANOVA using Statsmodels - This notebook contains the multivariate data analysis Multivariate Analysis of Variance (MANOVA)

Tutorials, guides, and how tos (both Python and R) that doesn't fit into the above categories

• Convert HTML to Jupyter notebook - Code to convert HTML (e.g., code within code tags) to .ipynb files that can be run