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Python for ArcGIS Pro

Python for ArcGIS Pro

By : Toms, Parker
4.8 (52)
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Python for ArcGIS Pro

Python for ArcGIS Pro

4.8 (52)
By: Toms, 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)
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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

Basics of NumPy for rasters

Using NumPy for rasters is very straightforward. Rasters are data organized into regular rows and columns, and may have multiple bands of data. These data behaviors can be precisely recreated using NumPy arrays, which can have any number of rows or columns, as well as multiple dimensions.

Creating an array 

Often in GIS you must create rasters for analyses. These arrays may need to be blank, allowing you to accumulate values from inputs to a continuous surface based on location; all one value to create a constant raster; or merged with vector data inputs such as GeoJSON files or shapefiles. All of these are possible with NumPy arrays.

There are many ways to create a NumPy array. Some of these are built-in tools, and some are methods to derive an array from an existing dataset such as a raster, CSV file, or JSON data, as seen in Chapter 8’s exploration of Pandas. Data can also be read from a vector file such as a shapefile or feature...

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Python for ArcGIS Pro
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