Book Image

ArcPy and ArcGIS - Second Edition

By : Silas Toms, Dara OBeirne
Book Image

ArcPy and ArcGIS - Second Edition

By: Silas Toms, Dara OBeirne

Overview of this book

ArcGIS allows for complex analyses of geographic information. The ArcPy module is used to script these ArcGIS analyses, providing a productive way to perform geo-analyses and automate map production. The second edition of the book focuses on new Python tools, such as the ArcGIS API for Python. Using Python, this book will guide you from basic Python scripting to advanced ArcPy script tools. This book starts off with setting up your Python environment for ArcGIS automation. Then you will learn how to output maps using ArcPy in MXD and update feature class in a geodatabase using arcpy and ArcGIS Online. Next, you will be introduced to ArcREST library followed by examples on querying, updating and manipulating ArcGIS Online feature services. Further, you will be enabling your scripts in the browser and directly interacting with ArcGIS Online using Jupyter notebook. Finally, you can learn ways to use of ArcPy to control ArcGIS Enterprise and explore topics on deployments, data quality assurances, data updates, version control, and editing safeguards. By the end of the book, you will be equipped with the knowledge required to create automated analysis with administration reducing the time-consuming nature of GIS.
Table of Contents (13 chapters)
8
Introduction to ArcGIS Online

Summary

In this chapter, we covered the basics of programming using Python, and introduced important Python modules. We covered how Python executes scripts and commands, and touched on the development environments used to craft scripts. In particular, we discussed Python basics including data types, containers and looping, how a Python script is executed by the Python interpreter, where the Python interpreter is located within the Python folder structure, and what the different Python script extensions mean (.py, .pyc, .pyw). We also covered Integrated Development Environments, and how they compare and contrast.

In the next chapter, we will explain how to use ModelBuilder to convert a modeled analysis into a Python script, and how to add more functionality to the exported script.