We continue in our plots series, this time looking at the packages JoyPy and PtitPrince.

We start a small series on different plotting addons for Matplotlib. In this specific post we consider the two packages PyWaffle and mpltern and what they can produce.

We continue our investigation of the pyhctsa package to understand how its generated feature matrix can be used with different machine learning techniques to understand data.

We are looking at the package pyhctsa and how it can be configured to setup different feature selections.

We are looking at the package popgrid and its ability to produce cartograms. Essentially a way of taking a geometry and morphing it into a diagram that represents the adjacency of areas but scaled to the a certain measurement.

We look at how to produce Kernel Density Mappings in Python. In this example we map density of squirrel sightings in Central Park from 2018 via Scipy and Scikit-learn.

We continue to look at the library Mixle and what it can do. We look at how synthetic data from a probabilistic model can be used to analyse if our fit is good.

We look at the package Mixle. It is still in beta stage. Mixle is a package to build probabilistic models of structured, mixed-type data. This way you do not have to convert non-numerical data.

We take a look at the Open-Meteo weather API and what one can do with it. In this part of the series we create two small data analysis projects based on the data from Open-Meteo

We take a look at the Open-Meteo weather API and what one can do with it. In each part of this series we create one little data analysis project based on Open-Meteo data.