electricsheepafrica/africa-sudan-sudan-people-affected-by-floods-6586bd3f

Sudan: People Affected by Floods | Africa (Sudan official open data)

302 rows - 1 Africa country - 2022 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official XLSX resource from Sudan as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: Sudan: People Affected by Floods
• Publisher: OCHA Sudan
• Resource: 2022-sdn-flood-disaster-monitoring-matrix-may-to-sep-2022.xlsx
• Format: XLSX
• License: CC BY 4.0
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
SDN	302	2022	2022	Sudan

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier for tabular resources.	5fd834bf-cb49-488d-820f-fa77f3cc5f55:admin:0
country_iso3	category	ISO3 country code.	SDN
country_name	category	Country name.	Sudan
source_sheet	string	Workbook sheet name, when the source is a spreadsheet.	Admin
year	Int64	Observation year.	2022
pcode	string	Source column.	SD19001
state	string	Source column.	Abyei PCA
locality	string	Source column.	Abyei PCA area
states	string	Source column.	Abyei PCA
source_period_start_year	Int64	First year inferred from source resource metadata.	2022
source_period_end_year	Int64	Last year inferred from source resource metadata.	2022
source_period_label	category	Human-readable period inferred from source resource metadata.	2022
source_provider	category	Publishing organization.	OCHA Sudan
source_dataset	category	Source package title.	Sudan: People Affected by Floods
source_resource	category	Source resource title.	2022-sdn-flood-disaster-monitoring-matrix-may-to-sep-2022.xlsx
source_package_id	category	CKAN package UUID.	2e49de77-72e6-46c3-b0b8-439ee21e6ad5
source_resource_id	category	CKAN resource UUID.	5fd834bf-cb49-488d-820f-fa77f3cc5f55
source_url	category	Original source resource URL.	https://data.humdata.org/dataset/2e49de77-72e6-46c3-b0b8-439ee21e6ad5/re
license_id	category	Source license identifier.	cc-by
retrieved_at	category	UTC retrieval timestamp.	2026-08-19T06:27:19Z
date_of_event_incident	string	Source column.	``
village_name	string	Source column.	``
houses_destroyed	float64	Source column.	``
houses_damaged	float64	Source column.	``
households_affected	float64	Source column.	``
people_killed	float64	Source column.	``
people_injured	float64	Source column.	``
people_affected	float64	Source column.	``
status_verified_unverified	string	Source column.	``
education_buildings_destroyed	float64	Source column.	``
education_buildings_damaged	float64	Source column.	``
health_facilities_destroyed	float64	Source column.	``
health_facilities_damaged	float64	Source column.	``
latrines_destroyed	float64	Source column.	``
latrines_damaged	float64	Source column.	``
water_sources_destroyed	float64	Source column.	``
water_sources_damaged	float64	Source column.	``
livestock_cattle_goats_and_other_killed_or_lost	float64	Source column.	``
agricultural_land	float64	Source column.	``
others	float64	Source column.	``
comments	string	Source column.	``
station	string	Source column.	``
x	float64	Source column.	``
y	float64	Source column.	``
alert_level	float64	Source column.	``
critical_level	float64	Source column.	``
flooding_level	float64	Source column.	``
2022	float64	Source column.	``
2022_2	float64	Source column.	``
2022_3	float64	Source column.	``
2022_4	float64	Source column.	``
2022_5	float64	Source column.	``
2022_6	float64	Source column.	``
2022_7	float64	Source column.	``
2022_8	float64	Source column.	``
2022_9	float64	Source column.	``
2022_10	float64	Source column.	``
2022_11	float64	Source column.	``
2022_12	float64	Source column.	``
2022_13	float64	Source column.	``
2022_14	float64	Source column.	``
2022_15	float64	Source column.	``
2022_16	float64	Source column.	``
2022_17	float64	Source column.	``
2022_18	float64	Source column.	``
2022_19	float64	Source column.	``
2022_20	float64	Source column.	``
2022_21	float64	Source column.	``
2022_22	float64	Source column.	``
2022_23	float64	Source column.	``
2022_24	float64	Source column.	``
2022_25	float64	Source column.	``
2022_26	float64	Source column.	``
2022_27	float64	Source column.	``
2022_28	float64	Source column.	``
2022_29	float64	Source column.	``
2022_30	float64	Source column.	``
2022_31	float64	Source column.	``
2022_32	float64	Source column.	``
2022_33	float64	Source column.	``
2022_34	float64	Source column.	``
2022_35	float64	Source column.	``
2022_36	float64	Source column.	``
2022_37	float64	Source column.	``
2022_38	float64	Source column.	``
2022_39	float64	Source column.	``
2022_40	float64	Source column.	``
2022_41	float64	Source column.	``
2022_42	float64	Source column.	``
2022_43	float64	Source column.	``
2022_44	float64	Source column.	``
2022_45	float64	Source column.	``
2022_46	float64	Source column.	``
2022_47	float64	Source column.	``
2022_48	float64	Source column.	``
2022_49	float64	Source column.	``
2022_50	float64	Source column.	``
2022_51	float64	Source column.	``
2022_52	float64	Source column.	``
2022_53	float64	Source column.	``
2022_54	float64	Source column.	``
2022_55	float64	Source column.	``
2022_56	float64	Source column.	``
2022_57	float64	Source column.	``
2022_58	float64	Source column.	``
2022_59	float64	Source column.	``
2022_60	float64	Source column.	``
2022_61	float64	Source column.	``
2022_62	float64	Source column.	``
2022_63	float64	Source column.	``
2022_64	float64	Source column.	``
2022_65	float64	Source column.	``
2022_66	float64	Source column.	``
2022_67	float64	Source column.	``
2022_68	float64	Source column.	``
2022_69	float64	Source column.	``
2022_70	float64	Source column.	``
2022_71	float64	Source column.	``
2022_72	float64	Source column.	``
2022_73	float64	Source column.	``
2022_74	float64	Source column.	``
2022_75	float64	Source column.	``
2022_76	float64	Source column.	``
2022_77	float64	Source column.	``
2022_78	float64	Source column.	``
2022_79	float64	Source column.	``
2022_80	float64	Source column.	``
2022_81	float64	Source column.	``
2022_82	float64	Source column.	``
2022_83	float64	Source column.	``
2022_84	float64	Source column.	``
2022_85	float64	Source column.	``
2022_86	float64	Source column.	``
2022_87	float64	Source column.	``
2022_88	float64	Source column.	``
2022_89	float64	Source column.	``
2022_90	float64	Source column.	``
2022_91	float64	Source column.	``
2022_92	float64	Source column.	``
2022_93	float64	Source column.	``
2022_94	float64	Source column.	``
2022_95	float64	Source column.	``
2022_96	float64	Source column.	``
2022_97	float64	Source column.	``
2022_98	float64	Source column.	``
2022_99	float64	Source column.	``
2022_100	float64	Source column.	``
2022_101	float64	Source column.	``
2022_102	float64	Source column.	``
2022_103	float64	Source column.	``
2022_104	float64	Source column.	``
2022_105	float64	Source column.	``
2022_106	float64	Source column.	``
2022_107	float64	Source column.	``
2022_108	float64	Source column.	``
2022_109	float64	Source column.	``
2022_110	float64	Source column.	``
2022_111	float64	Source column.	``
2022_112	float64	Source column.	``
2022_113	float64	Source column.	``
2022_114	float64	Source column.	``
2022_115	float64	Source column.	``
2022_116	float64	Source column.	``
2022_117	float64	Source column.	``
2022_118	float64	Source column.	``
2022_119	float64	Source column.	``
2022_120	float64	Source column.	``
2022_121	float64	Source column.	``
2022_122	float64	Source column.	``
2022_123	float64	Source column.	``
2022_124	float64	Source column.	``
2022_125	float64	Source column.	``
2022_126	float64	Source column.	``
2022_127	float64	Source column.	``
2022_128	float64	Source column.	``
2022_129	float64	Source column.	``
2022_130	float64	Source column.	``
2022_131	float64	Source column.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sudan-sudan-people-affected-by-floods-6586bd3f")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "SDN"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_sudan_sudan_people_affected_by_floods_6586bd3f_2022,
  title        = {Sudan: People Affected by Floods | Africa (Sudan official open data)},
  author       = {OCHA Sudan},
  year         = {2022},
  url          = {https://data.humdata.org/dataset/sudan-people-affected-by-floods},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-sudan-sudan-people-affected-by-floods-6586bd3f}}
}

License

Released under CC BY 4.0.

Original data (c) OCHA Sudan. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

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Provenance: ingested 2026-08-19 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/2e49de77-72e6-46c3-b0b8-439ee21e6ad5/resource/5fd834bf-cb49-488d-820f-fa77f3cc5f55/download/2022-sdn-flood-disaster-monitoring-matrix-may-to-sep-2022-1.xlsx