--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression multilinguality: multilingual size_categories: - n<1K tags: - "tabular" - "africa" - "open-data" - "official-statistics" - "somalia" - "unicef-eastern-and-southern-africa-regional-office-esaro-inactive" - "ago" - "eth" - "ken" - "moz" - "rwa" - "som" - "zaf" - "ssd" configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Eastern and Southern Africa Covid 19 Unicef Situation and | Africa (UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive))" --- # Eastern and Southern Africa Covid 19 Unicef Situation and | Africa (UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)) **268 rows** - **1 Africa country/area** - **2020** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![rows](https://img.shields.io/badge/rows-268-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![period](https://img.shields.io/badge/period-2020-orange) ![indicators](https://img.shields.io/badge/indicators-0-purple) ![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey) ## TL;DR This dataset contains **268 rows** from **UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)**, covering **Eastern and Southern Africa Covid 19 Unicef Situation and**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. ## What This Dataset Measures Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals. Source-provided context: COVID-19 Situation and Response Dashboard for August 2020 ## How To Read This Dataset - **One row means:** one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available. - **Primary geography column:** `country_iso3`. - **Best time column:** `year`. - **Time coverage basis:** year. - **Recommended join keys:** `country_iso3` where available plus source-specific keys. ## Coverage | Dimension | Value | |---|---:| | Rows | 268 | | Countries/areas | 1 | | First period | 2020 | | Last period | 2020 | | Indicators | 0 | | Columns | 23 | | Source format | XLSX | ## Geographic Coverage Top areas shown below, sorted by row count when available: | Area | Rows | First year | Last year | Name | |------|-----:|-----------:|----------:|------| | `SOM` | 268 | 2020 | 2020 | `Somalia` | ## Indicators, Variables, Or Resource Contents - This repo preserves one source tabular resource with its usable columns kept together. ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `5c2adef0-7eba-4959-ad49-bc25861f15c5:sheet1:0` | | `country_iso3` | `dictionary` | ISO3 country or area code. | `SOM` | | `country_name` | `dictionary` | Country or area name. | `Somalia` | | `source_sheet` | `string` | Source column from the original resource. | `Sheet1` | | `year` | `int64` | Observation year. | `2020` | | `business_area` | `string` | Source column from the original resource. | `Angola` | | `hac_pillar_name` | `string` | Source column from the original resource. | `CV-01 - Risk Communication and Community Engagement (RCCE)` | | `indicator` | `string` | Source column from the original resource. | `1: Number of people reached on COVID-19 through MESSAGING ON PREVENTI...` | | `sum_of_target` | `double` | Source column from the original resource. | `8000000.0` | | `nr1` | `double` | Source column from the original resource. | `11032736.0` | | `achieved` | `double` | Source column from the original resource. | `1.379092` | | `sitrep_round` | `string` | Source column from the original resource. | `SitRep 13` | | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2020` | | `source_period_end_year` | `int64` | End year inferred from source metadata. | `2020` | | `source_period_label` | `dictionary` | Source column from the original resource. | `2020` | | `source_provider` | `dictionary` | Publishing organization. | `UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)` | | `source_dataset` | `dictionary` | Source dataset or package title. | `Eastern and Southern Africa COVID-19 - UNICEF - Situation and Response` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `COVID 2020 Annual data final.xlsx` | | `source_package_id` | `dictionary` | Source package identifier. | `63ab02df-384d-4814-bfe9-73b47ba09f59` | | `source_resource_id` | `dictionary` | Source resource identifier. | `5c2adef0-7eba-4959-ad49-bc25861f15c5` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.humdata.org/dataset/63ab02df-384d-4814-bfe9-73b47ba09f59...` | | `license_id` | `dictionary` | Source license identifier. | `cc-by` | | `retrieved_at` | `dictionary` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-10T14:51:11Z` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-somalia-eastern-and-southern-africa-covid-19-unicef-situation-and-2088f7fe") df = ds["train"].to_pandas() print(df.head()) ``` ### Inspect Columns ```python print(df.info()) print(df.head()) ``` ### Filter By Geography ```python if "country_iso3" in df.columns: sample = df[df["country_iso3"] == "SOM"] ``` ### Time-Series Pattern ```python if "value" in df.columns and "year" in df.columns: trend = df.sort_values("year") ``` ### Pivot For Analysis ```python if {"indicator_id", "year", "value"}.issubset(df.columns): matrix = df.pivot_table(index="year", columns="indicator_id", values="value") print(matrix.tail()) ``` ## Data Quality Notes - Canonical time field: `year`. - Missing values are preserved rather than silently imputed. - Column names are standardized for machine use; source meanings are preserved where known. - Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use. ## Source And Provenance - **Source:** [UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)](https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response) - **Publisher:** UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) - **Portal:** [https://data.humdata.org](https://data.humdata.org) - **Resource:** [COVID 2020 Annual data final.xlsx](https://data.humdata.org/dataset/63ab02df-384d-4814-bfe9-73b47ba09f59/resource/5c2adef0-7eba-4959-ad49-bc25861f15c5/download/covid-2020-annual-data-final.xlsx) - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Retrieved/generated:** `2026-08-10T16:18:14Z` - **Hugging Face repo:** [electricsheepafrica/africa-somalia-eastern-and-southern-africa-covid-19-unicef-situation-and-2088f7fe](https://huggingface.co/datasets/electricsheepafrica/africa-somalia-eastern-and-southern-africa-covid-19-unicef-situation-and-2088f7fe) ## Transformations Applied - Converted the source table to Parquet for efficient analytics and ML workflows. - Added or preserved source provenance columns where available. - Standardized README metadata, dataset loading configuration, schema documentation, and citation format. - Preserved source-reported values without analytical imputation. ## Suggested Analyses - Profile the distribution of values - Compare categories or geographies - Join with complementary public datasets - Build time-series views and period-over-period comparisons - Check missingness before modeling - Use `country_iso3` as the safest geography join key when present ## Citation ```bibtex @misc{electric_sheep_africa_africa_somalia_eastern_and_southern_africa_covid_19_unicef_situation_and_2088f7f_2020, title = {Eastern and Southern Africa Covid 19 Unicef Situation and | Africa (UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive))}, author = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)}, year = {2020}, url = {https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-somalia-eastern-and-southern-africa-covid-19-unicef-situation-and-2088f7fe}} } ``` ## License Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Original data is published by UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive). Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used. ## About Electric Sheep Africa Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face. --- Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response