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?

Creating Slides with Jupyter Notebook

Jupyter notebook is a powerful tool to interactively code in web-based notebooks with a whole plethora of programming languages. With it, it is also possible to create web-based slideshows with reveal.js.

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.

Using the Blender Interactive Console from the Command-Line

Blender is a wonderful and free 3D modelling tool, but sometimes you need to work with it without the GUI. Luckily Blender supports that and you can work there and do your thing very easily with Python.

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?

Working with Missing Values in Pandas

Here we’ll see how to work with missing values in a Pandas DataFrame on a data set from the World Bank Open Data of the Global Economic Monitor.