Book Image

Mastering Geospatial Analysis with Python

By : Silas Toms, Paul Crickard, Eric van Rees
Book Image

Mastering Geospatial Analysis with Python

By: Silas Toms, Paul Crickard, Eric van Rees

Overview of this book

Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
7
Geoprocessing with Geodatabases
Index

GeoPandas


GeoPandas is a Python library for working with vector data. It is based on the pandas library that is part of the SciPy stack. SciPy is a popular library for data inspection and analysis, but unfortunately, it cannot read spatial data. GeoPandas was created to fill this gap, taking pandas data objects as a starting point. The library also adds functionality from geographical Python packages. 

GeoPandas offers two data objects—a GeoSeries object that is based on a pandas Series object and a GeoDataFrame, based on a pandas DataFrame object, but adding a geometry column for each row. Both GeoSeries and GeoDataFrame objects can be used for spatial data processing, similar to spatial databases. Read and write functionality is provided for almost every vector data format. Also, because both Series and DataFrame objects are subclasses from pandas data objects, you can use the same properties to select or subset data, for example .loc or .iloc.

GeoPandas is a library that employs the capabilities...