Geospatial Data Science with Python: Data Visualization
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Price: 99.99$
Pandas and Geo Pandas within a Jupyter Notebook environment provide a powerful alternative to traditional desktop GIS methods for geospatial analysis. The ability to incorporate visualizations of both tabular data and geospatial data from within your analysis workflow is one of the big advantages of this approach. This course provides detail on how to create beautiful tabular and geospatial visualizations using Matplotlib, Pandas, Geo Pandas, Rasterio, Contextily, Seaborn, Plotly, Bokeh and other Python packages within a Jupyter Notebook environment. We start with Matplotlib because it is the core upon which all of the other static plotting methods are based. Pandas, Geo Pandas, Rasterio, Contextily, and Seaborn all produce Matplotlib objects as output. If you understand Matplotlib you can use that knowledge to modify the lots put out by any of these other packages. You can control the labels and titles, place annotation on the maps, include ticks and gridlines, place legends, set the x and y limits and more. And you can control every detail of those outputs, even when the higher level plotting API’s make it easy to produce the base output with a single line of code. Plotly and Bokeh produce dynamic output that is based on Java Script and are able to respond to mouse movements, clicks, etc.
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