How to prepare your data for geographical display : we will use pandas to read the dataset from file, and have a first look at the data before display.Get a Google Map API key : this is necessary to be able to display google maps in your applications.Installation: set up python for this exercise.
You'll see and fix bugs in your data processing, and you'll start thinking about ways to extract valuable information from these datasets. As soon as you do that, obvious features will jump at your eyes. To gain insight into such datasets, you need to be able to display or segment them as a function of geographical coordinates. Think about census, real estate, a distributed system of IOT sensors, geological or weather data, etc. In fact, as soon as measurements are done at a given place in the world, the dataset becomes geographical. In real world data science, geographical datasets are everywhere. If you just want to see the prices, you'll find a ready-to-use interactive plot at the end of the post.
In this post, you will learn how to use python to overlay your data on top of a dynamic Google map.Īs an example, we will use a dataset containing all the real-estate sells that occurred in 20 in France, near the swiss town of Geneva.
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