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

Pandas, Data Frames, and Vector Data

Data analysis is a popular use of Python. Since Python can read and write lots of data formats, has powerful built-in mathematical functionality, and has a large number of third-party modules written for specific analytical and statistical realms, it has gained wide popularity among analysts and scientists.

One of the most popular data analysis modules is Pandas. It has become a standard tool for data analysis and data science, and has extended into geospatial analysis and geodata science.

In this chapter, we will cover the following topics:

  • What a DataFrame is
  • The basics of using Pandas, including reading and writing files
  • Performing data analysis and manipulation with Pandas
  • Using Spatially Enabled DataFrames (SEDFs)

To complete the exercises in this chapter, please download and unzip the Chapter8.zip folder from the GitHub repository for this book: https://github.com/PacktPublishing/Python...