Where do Mayors Come From: Querying Wikidata with Python and SPARQL

In this article, we will be going through building queries for Wikidata with Python and SPARQL by taking a look where mayors in Europe are born. This tutorial is building up the knowledge to collect the data responsible for this interactive visualization from the header image which was done with deck.gl.

Compare Countries and Cities with OpenStreetMap and t-SNE

There are many ways to compare countries and cities and many measurements to choose from. We can see how they perform economically, or how their demographics differ, but what if we take a look at data available in OpenStreetMap? In this article, we explore just that with the help of a procedure called t-SNE.

Predict Economic Indicators with OpenStreetMap

OpenStreetMap (OSM) is a massive collaborative map of the world, built and maintained mostly by volunteers. On the other hand, there exist various indicators to measure economic growth, prosperity, and produce of a country. What if we use OpenStreetMap to predict those economic indicators?

Working with MultiIndex and Pivot Tables in Pandas and Python

Here we’ll take a look at how to work with MultiIndex or also called Hierarchical Indexes in Pandas and Python on real world data. Hierarchical indexing enables you to work with higher dimensional data all while using the regular two-dimensional DataFrames or one-dimensional Series in Pandas.

Working with Pandas Groupby in Python and the Split-Apply-Combine Strategy

In this tutorial we will cover how to use the Pandas DataFrame groupby function while having an excursion to the Split-Apply-Combine Strategy for data analysis. The Split-Apply-Combine strategy is a process that can be described as a process of splitting the data into groups, applying a function to each group and combining the result into a final data structure.

Calculate Distance Between GPS Points in Python

When working with GPS, it is sometimes helpful to calculate distances between points. But simple Euclidean distance doesn’t cut it since we have to deal with a sphere, or an oblate spheroid to be exact. So we have to take a look at geodesic distances.

Loading Data from OpenStreetMap with Python and the Overpass API

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?

Command-Line Image Processing with ImageMagick

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.

Batch Geocoding with Python

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 to deal with the APIs out there.

Framing Parametric Curves

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.

Three ways to get most of your CSV in Python

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.

Working with Time and Time Zones in Python

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.

Understanding the Covariance Matrix

This article is showing a geometric and intuitive explanation of the covariance matrix and the way it describes the shape of a data set. We will describe the geometric relationship of the covariance matrix with the use of linear transformations and eigendecomposition.