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

Geospatial Data Processing with NumPy

Data processing tools are often limited to the pre-built tools discussed in other chapters, or open-source tools such as Shapely, Rasterio, or GDAL. These tools can be limited in terms of processing speed and flexibility. When creating geospatial data workflows, you will often have to create custom tools to process data quickly, and those other libraries can be limiting.

NumPy offers a third way. Used for scientific computing, it is an incredibly fast and powerful module written in C, with a Python code “wrapper” so it can be used in your existing Python environment. It is built to read, analyze, and write multidimensional arrays of data.

Esri has included easy tools to convert rasters into NumPy arrays and back, which makes it easy to add custom NumPy functions into your existing pipelines. Selecting or clipping areas of a raster, performing array math, creating new arrays and populating them with data, processing specific...