electricsheepafrica/africa-sudan-2021-hrp-response-monitoring-4ws

Sudan: 2021 HRP-Response Monitoring (4Ws)

Publisher: OCHA Sudan · Source: HDX · License: cc-by-igo · Updated: 2025-05-05

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Abstract

The data set contains the number of people in need, people targeted and people reached by humanitarian assistance in Sudan during the period January-December 2021.It contains the overall reached and the sector breakdowns too. The data is provided by admin 2 level.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: SDN.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Subnational administrative unit observations
Rows (total)	191
Columns	43 (37 numeric, 6 categorical, 0 datetime)
Train split	152 rows
Test split	38 rows
Geographic scope	SDN
Publisher	OCHA Sudan
HDX last updated	2025-05-05

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Variables

Geographic — priority (range 1.0–3.0), p_code_admin1 (SD03, SD02, SD07), state (South Darfur, North Darfur, South Kordofan), pcode_admin2 (#adm2+code, SD03144, SD03141), locality (#adm2+name, Kas, Al Radoum).

Outcome / Measurement — total_reached_jan_dec_2021.

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-18).

Other — gbv_pin (range 0.0–204515.0), gbv_target (range 0.0–81806.0), gbv_jan_dec (range 0.0–290636.0), nut_pin (range 615.0–136624.0), nut_target (range 0.0–71044.0) and 30 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sudan-2021-hrp-response-monitoring-4ws")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
priority	float64	0.5%	1.0 – 3.0 (mean 2.1684)
p_code_admin1	object	0.0%	SD03, SD02, SD07
state	object	0.0%	South Darfur, North Darfur, South Kordofan
pcode_admin2	object	0.5%	#adm2+code, SD03144, SD03141
locality	object	0.0%	#adm2+name, Kas, Al Radoum
gbv_pin	float64	0.5%	0.0 – 204515.0 (mean 12039.3895)
gbv_target	float64	0.5%	0.0 – 81806.0 (mean 5095.3895)
gbv_jan_dec	float64	0.5%	0.0 – 290636.0 (mean 3656.3421)
nut_pin	float64	1.0%	615.0 – 136624.0 (mean 18995.8677)
nut_target	float64	0.5%	0.0 – 71044.0 (mean 9867.8526)
nutrition_jan_dec	float64	0.5%	0.0 – 52144.0 (mean 6379.0842)
snfi_pin	float64	0.5%	0.0 – 293901.0 (mean 10389.7053)
snfi_target	float64	8.4%	0.0 – 205731.0 (mean 6869.5143)
es_nfi_jan_dec	float64	0.5%	0.0 – 199166.8 (mean 3817.9116)
wash_pin	float64	0.5%	0.0 – 356969.0 (mean 41872.7474)
wash_target	float64	0.5%	0.0 – 249879.0 (mean 25056.9053)
wash_jan_dec	float64	0.5%	0.0 – 307498.0 (mean 16803.6211)
edu_pin	float64	0.5%	0.0 – 95097.0 (mean 14097.6737)
edu_target	float64	0.5%	0.0 – 77599.0 (mean 11538.8263)
education_jan_dec	float64	0.5%	0.0 – 78995.0 (mean 3100.4844)
cp_pin	float64	0.5%	0.0 – 154071.0 (mean 13477.8421)
cp_target	float64	0.5%	0.0 – 146916.0 (mean 5889.3684)
cp_jan_dec	float64	0.5%	0.0 – 57805.0 (mean 1671.9211)
fsl_pin	float64	0.5%	0.0 – 242165.0 (mean 37577.4842)
fsl_target	float64	0.5%
fsl_jan_dec	float64	0.5%
hea_pin	float64	0.5%
hea_target	float64	0.5%
health_jan_dec	float64	0.5%
prt_pin	float64	0.5%
prt_tar	float64	0.5%
prt_jan_dec	float64	0.5%
ma_pin	float64	0.5%
ma_tar	float64	0.5%
ma_jan_dec	float64	0.5%
rcf_pin	float64	0.5%
rcf_target	float64	0.5%
rcf_jan_dec	float64	0.5%
2021_pin	float64	0.5%
2021_target	float64	0.5%
total_reached_jan_dec_2021	float64	0.5%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-18

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Numeric Summary

Column	Min	Max	Mean	Median
priority	1.0	3.0	2.1684	2.0
gbv_pin	0.0	204515.0	12039.3895	5078.0
gbv_target	0.0	81806.0	5095.3895	1851.5
gbv_jan_dec	0.0	290636.0	3656.3421	0.0
nut_pin	615.0	136624.0	18995.8677	13217.0
nut_target	0.0	71044.0	9867.8526	6893.5
nutrition_jan_dec	0.0	52144.0	6379.0842	4570.5
snfi_pin	0.0	293901.0	10389.7053	1880.0
snfi_target	0.0	205731.0	6869.5143	997.0
es_nfi_jan_dec	0.0	199166.8	3817.9116	0.0
wash_pin	0.0	356969.0	41872.7474	28254.0
wash_target	0.0	249879.0	25056.9053	15574.0
wash_jan_dec	0.0	307498.0	16803.6211	0.0
edu_pin	0.0	95097.0	14097.6737	9111.5
edu_target	0.0	77599.0	11538.8263	7464.5

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 36 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from OCHA Sudan and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_sudan_2021_hrp_response_monitoring_4ws,
  title     = {Sudan: 2021 HRP-Response Monitoring (4Ws)},
  author    = {OCHA Sudan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/sudan-2021-hrp-response-monitoring-4ws},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.