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

Cleaning up and combining the data

Before merging all the data into one dataset, some cleanup needs to take place to ensure the merge is viable. In order to merge datasets, there needs to be a column or multiple columns that match in both data frames. In this case, the merge will occur on the year and the country name columns.

The year columns have no variations, but the name of the country columns may differ slightly in spelling or if abbreviations are used. The data frame from the World Bank data, df_wb, only has an abbreviation to represent countries and contains data for regions, in addition to country names. The actual country names will need to be added and the rows containing region data will need to be removed.

Luckily, the world_bank_data API has a readily available dataset containing all the IDs, country names, and information about region data; specifically, the column 'region', which specifies whether an entry is a combination of countries.

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