electricsheepafrica/africa-south-sudan-eastern-and-southern-africa-refugees-and-idps-situation-an-c8b0b1c9

Eastern and Southern Africa Refugees and Idps Situation an | Africa (UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive))

261 rows - 1 Africa country/area - 2019-2022 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 261 rows from UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive), covering Eastern and Southern Africa Refugees and Idps Situation an. 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: UNICEF Eastern and Southern Africa Refugees and IDPs Situation and Response, Dec 2019

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: not detected.
• —Time coverage basis: source metadata.
• —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Geographic Coverage

Top areas shown below, sorted by row count when available:

Indicators, Variables, Or Resource Contents

• —This repo preserves one source tabular resource with its usable columns kept together.

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-eastern-and-southern-africa-refugees-and-idps-situation-an-c8b0b1c9")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "SSD"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

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

• —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
• —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)
• —Publisher: UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)
• —Portal: https://data.humdata.org
• —Resource: UNICEF ESARO Regional refugee and idp db 2019 January 22.2020.xlsx
• —License: CC BY 4.0
• —Retrieved/generated: 2026-08-10T19:54:32Z
• —Hugging Face repo: electricsheepafrica/africa-south-sudan-eastern-and-southern-africa-refugees-and-idps-situation-an-c8b0b1c9

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
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_south_sudan_eastern_and_southern_africa_refugees_and_idps_situation_an_c8_2022,
  title        = {Eastern and Southern Africa Refugees and Idps Situation an | Africa (UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive))},
  author       = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)},
  year         = {2022},
  url          = {https://data.humdata.org/dataset/eastern-and-southern-africa-refugees-and-idps-situation-and-response},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-eastern-and-southern-africa-refugees-and-idps-situation-an-c8b0b1c9}}
}

License

Released under CC 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-refugees-and-idps-situation-and-response