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Mastering Geospatial Analysis with Python

You're reading from   Mastering Geospatial Analysis with Python Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Length 440 pages
Edition 1st Edition
Languages
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Authors (3):
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Silas Toms Silas Toms
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Silas Toms
Paul Crickard Paul Crickard
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Paul Crickard
Eric van Rees Eric van Rees
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Eric van Rees
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Table of Contents (18) Chapters Close

Preface 1. Package Installation and Management 2. Introduction to Geospatial Code Libraries FREE CHAPTER 3. Introduction to Geospatial Databases 4. Data Types, Storage, and Conversion 5. Vector Data Analysis 6. Raster Data Processing 7. Geoprocessing with Geodatabases 8. Automating QGIS Analysis 9. ArcGIS API for Python and ArcGIS Online 10. Geoprocessing with a GPU Database 11. Flask and GeoAlchemy2 12. GeoDjango 13. Geospatial REST API 14. Cloud Geodatabase Analysis and Visualization 15. Automating Cloud Cartography 16. Python Geoprocessing with Hadoop 17. Other Books You May Enjoy

GeoPandas

GeoPandas has been introduced in the GeoPandas section of  Chapter 2, Introduction to Geospatial Code Libraries, where its data structures and methods have also been covered.

Geospatial analysis with GeoPandas

GeoPandas was created to offer data to scientists who want to work with spatial data similar to pandas, and this means giving access to geospatial attribute data through data structures not available through pandas. Combine this with a set of geometric operations, data overlay capabilities, geocoding and plotting capabilities and you have an idea of this library's capabilities. In the examples mentioned as we proceed, we'll cover GeoPandas' plotting methods, explain how to access...

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