18 Mar 2018
In this tutorial we will cover how to use the Pandas DataFrame groupby function. The data set we will be analysing is the Current Employee Names, Salaries, and Position Titles from the City of Chicago, which is listing all their employees with full names, departments, positions, and salaries. Let’s get into it!
04 Mar 2018
Have you ever wondered where most Biergarten in Germany are or how many banks are hidden in Switzerland? OpenStreetMap is a great open source map of the world which can give us some insight into these and similar questions. There is a lot of data hidden in this data set, full of useful labels and geographic information, but how do we get our hands on the data?
06 Dec 2017
There are times being stuck with a load of images that need to be cropped, resized or converted, but doing this by hand in an image editor is tedious work. One tool I commonly use in these desperate situations is ImageMagick, which is a powerful tool when automating raster and vector image processing. Here I’ll introduce a few common commands I had to look up multiple times.
29 Nov 2017
You have a list of addresses, but you need to get GPS coordinates to crunch some numbers. Don’t despair, there is geocoding for this and Python provides some simple means to help dealing with the APIs out there.
30 Jun 2017
This article explores an efficient way on how to create tubes, ribbons and moving camera orientations based on parametric curves with the help of moving coordinate frames.
24 Jun 2017
One of the crucial tasks when working with data is to load data properly. The common way the data is formated is CSV, which comes in different flavors and varying difficulties to parse. This article shows three common approaches in Python.
07 Jun 2017
Time conversions can be tedious, but Python offers some relief for the frustration. Here are some quick recipes which are quite useful when juggling with time.
02 Mar 2017
The covariance matrix is a confusing yet powerful matrix, which frequently shows up while delving into statistics and probability theory. Here I’ll show a more intuitive geometric explanation of the covariance matrix and the way it describes the shape of a data set.