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)
Introduction to ArcGIS Online

Importing a CSV with pandas

As a GIS professional, you encounter many data types that you have to manage. Much of the data you will manage is commonly in a comma-separated value (CSV) format. Later, I will show you how to add a CSV as an item to your ArcGIS Online organization. In addition, if that CSV has a Lat/Long or X/Y, you can also add it as a spatial feature layer to you GIS.

To work with CSVs, we will be using an open source Python library called pandas. First, we import pandas and call it pd and then we build a data frame using the read_csv function from pandas. A data frame is a structured way of viewing data; you can think of it as a spreadsheet or an SQL table. In the next figure, you can see what a data frame looks like when we build it from our SFPF_2016.csv. For the purposes of this exercise, I have reduced SFPD_2016.csv from its original size of 150,000 records...