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Exploring International Debt Statistics using the World Bank API in Python and R

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With the increasing demand for accurate and transparent debt data, understanding what data is available and how to access it has never been more important. That is why we’re sharing Part 2 of our Python and R step-by-step guides, which show users how to get and explore data from the International Debt Statistics (IDS) database using the World Bank API.

This is Part 2 of series of step-by-step guides helping you access, explore, and analyze IDS data. In Part 1, we showed how to use the API to get the IDS database and then select indicators and locations of interest. In Part 2, we will use those parameters to retrieve the data, create a table, and visualize the data in an interactive line graph. The guides are available in Python and R on GitHub.

Part 2 – Get and explore the data

Python Guide

R guide

By using the guides above you can make an interactive visualization similar to the one below (except with Python or R). We encourage you to use and customize the guides and then share your IDS visualizations with us!


To learn more about World Bank Debt Statistics, visit the Debt Portal, now featuring a report on what the Debtor Reporting System (DRS) measures and Edition II of the quarterly Debt Report.


Parul Agarwal

Statistical Analyst, Development Data Group, World Bank

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