electricsheepafrica/africa-south-sudan-litpop-humanitarian-response-plan-hrp-countries-exposure-d-be7561e9

Litpop Humanitarian Response Plan Hrp Countries Exposure D | Africa (ETH Zürich - Weather and Climate Risks)

12,992 rows - 1 Africa country/area - detected - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 12,992 rows from ETH Zürich - Weather and Climate Risks, covering Litpop Humanitarian Response Plan Hrp Countries Exposure D. 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: Gridded LitPop data for Burkina Faso with admin1 name column

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: source_period_start_year.
• Time coverage basis: source_period_start_year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	12,992
Countries/areas	1
First period	detected
Last period	detected
Indicators	0
Columns	21
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
SSD	12,992	detected	detected	South Sudan

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.	7bd5d7a5-5537-46bd-9a99-7184fd1316ed:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	SSD
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	South Sudan
country_name_2	string	Source column from the original resource.	#country
admin1_name	string	Source column from the original resource.	#adm1+name
latitude	double	Source column from the original resource.	``
longitude	double	Source column from the original resource.	``
aggregation	string	Source column from the original resource.	``
indicator	string	Source column from the original resource.	#indicator+name
value	double	Numeric observation value.	``
source_period_start_year	int64	Start year inferred from source metadata.	``
source_period_end_year	int64	End year inferred from source metadata.	``
source_period_label	string	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	ETH Zürich - Weather and Climate Risks
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	LitPop: Humanitarian Response Plan (HRP) Countries Exposure Data for ...
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	burkina-faso-admin1-litpop.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	3527869c-8fe9-4289-9d57-1811e789bf60
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	7bd5d7a5-5537-46bd-9a99-7184fd1316ed
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.humdata.org/dataset/3527869c-8fe9-4289-9d57-1811e789bf60...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-10T18:18:23Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-litpop-humanitarian-response-plan-hrp-countries-exposure-d-be7561e9")
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 "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_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

• Canonical time field: source_period_start_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: ETH Zürich - Weather and Climate Risks
• Publisher: ETH Zürich - Weather and Climate Risks
• Portal: https://data.humdata.org
• Resource: burkina-faso-admin1-litpop.csv
• License: CC BY 4.0
• Retrieved/generated: 2026-08-10T19:25:42Z
• Hugging Face repo: electricsheepafrica/africa-south-sudan-litpop-humanitarian-response-plan-hrp-countries-exposure-d-be7561e9

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

@misc{electric_sheep_africa_africa_south_sudan_litpop_humanitarian_response_plan_hrp_countries_exposure_d_be_2026,
  title        = {Litpop Humanitarian Response Plan Hrp Countries Exposure D | Africa (ETH Zürich - Weather and Climate Risks)},
  author       = {ETH Zürich - Weather and Climate Risks},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/climada-litpop-dataset},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-litpop-humanitarian-response-plan-hrp-countries-exposure-d-be7561e9}}
}

License

Released under CC BY 4.0.

Original data is published by ETH Zürich - Weather and Climate Risks. 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.

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Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/climada-litpop-dataset