We continue our series on the Pyomo package and focus on the difference in integral and continuous optimisation problems.

We are taking a look at optimisation problems and especially the two packages Pyomo, for defining the problem, and HiGHS, for solving it.

In this final part of the plotting package series, we are taking a look at pyCirclize and mplsoccer. Both of them can create interesting circular plots. And if you are into soccer, mplsoccer can do quite a bit more.

Continuing our series of plot packages, we take a look at UpSetPlot, which handles membership problems. This essentially tries to visualise how a population with different characteristics fits into different groups with overlaps.

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 are continuing with testing the ultralytics package and train a yolo model on an aquarium dataset.

We are taking a look at the Ultralytics package and a few simple examples on how to set up object detection for images.

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 take a second look at City2Graph, how it works with GTFS and how we can create new edge types and relationships to work with graphs.

We are taking a look at the City2Graph package. In this part we simply focusing on what type of graphs can be easily created and what one might be able to do with them.

We continue our series on the XY package and focus on its interaction with the Reflex web application package.

We look at the plotting package XY and make some comparisons with Matplotlib. This is mainly to understand where they differ and what they are made for and not a full tutorial into XY.

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.