Book Image

Python for ArcGIS Pro

By : Silas Toms, Bill Parker
Book Image

Python for ArcGIS Pro

By: Silas Toms, Bill Parker

Overview of this book

Integrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You’ll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you’ll benefit from this book’s hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity.
Table of Contents (20 chapters)
1
Part I: Introduction to Python Modules for ArcGIS Pro
5
Part II: Applying Python Modules to Common GIS Tasks
10
Part III: Geospatial Data Analysis
14
Part IV: Case Studies
18
Other Books You May Enjoy
19
Index

Summary

Using NumPy to process rasters (or vector data) can offer a unique way to create custom functions or complete custom tools. The ability to process n-dimensional arrays quickly makes NumPy a powerful tool for fast mathematical and statistical operations.

In this chapter, we reviewed many different functions NumPy has, including viewing and changing the properties of arrays and the mathematical operations that can be performed on arrays. Queries on arrays and converting rasters into arrays and back again were also covered. We explained the concatenation of arrays, and wrapped up by demonstrating how to generate charts from statistics using Matplotlib in an end-to-end exercise.

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Up to now, you have learned how to use ArcPy, ArcGIS API for Python, Pandas, Spatially Enabled DataFrames, and NumPy to automate much of your analysis, data management, and map production. The next three chapters will be different, as they will be case studies. In each chapter, you will see...