From the course: Geospatial Data Analytics Essential Training
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Visualizing synthetic data with GeoPandas - Python Tutorial
From the course: Geospatial Data Analytics Essential Training
Visualizing synthetic data with GeoPandas
- [Instructor] After creating and manipulating our first GeoDataFrame, we will dive into one of the most exciting parts of geospatial data, visualizing it and taking our first steps to draw colorful and insightful maps. For this, we will use the previously defined GeoDataFrame, built from synthetic data consisting of a point, a line, a circle, a triangle, and a square, where we also computed the area and length of each geometry. In the next cell, let's import geopandas as usual, and also display the prepared GeoDataFrame. As you can see here on the output of this cell, we indeed have five geometries in this DataFrame, each of them having a name column, a geometry attribute, its type, length, and area as well. First, the most simple plotting variation comes when we use the built-in plot command of the GeoPandas package as follows. This command simply draws up each geometry on the canvas using a predefined canvas size and a nice simple blue color. As you can see here on this figure, we…
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