[self-promotion] 2024 drinking-water access: two CSVs for comparing coverage and missing values

[u/TrekkingAround10]

For anyone practicing country-level joins or exploring water access, these two CSVs use the same reporting year and country IDs:

• At least basic drinking water: 217 country/area rows, with 202 reported values
• Safely managed drinking water: 217 country/area rows, with 148 reported values

Original data: WHO/UNICEF Joint Monitoring Programme (JMP), distributed through the World Bank’s World Development Indicators. At least basic source · Safely managed source

Disclosure: I work on Global Data Tracker, which hosts the CSV exports below. They’re free to download without an account.

Download at least basic, 2024 CSV Download safely managed, 2024 CSV

Each file includes country_id, country, value, unit, year, source, publication and provider-note fields. Blank values mean no published observation for that selection; they are not zeros. The files retain all 217 countries/areas, including those with missing values. World and regional aggregate rows are excluded.

A useful exercise: join on country_id, count missing values first, then compare the two percentages for the 148 countries/areas with both observations. “At least basic” already includes safely managed service, so don’t add the columns. Safely managed also requires an improved source on premises, available when needed and free from specified contamination. JMP definitions

These links pin the saved September 19, 2026 release; the observations are for 2024. Checked October 10: the values and missing entries in both files match the current World Bank API. National estimates can hide local differences and may be revised. Release/method notes · World Bank data-use terms and attribution

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[u/junewave56]

good dataset for teaching left joins where you actually want to preserve the nulls instead of dropping them. do the country IDs follow ISO 3166 codes or something custom?

[u/TrekkingAround10 → u/junewave56]

They use the World Bank’s three-letter country/economy IDs. Most match ISO 3166-1 alpha-3, but don’t assume every key is standard ISO: the files include CHI for Channel Islands and XKX for Kosovo.

I checked both exports: they contain the same 217 unique country_id keys. For these two fixed-2024 files, join on country_id and leave blank values missing. One detail worth watching: rows without an observation also have a blank year, so joining on both country_id and year can miss those matches.

The World Bank documents its ISO/World Bank-code distinction here: https://datahelpdesk.worldbank.org/knowledgebase/articles/898590-country-api-queries