electricsheepafrica/africa-ifrc-appeals-data-for-uganda

Uganda - IFRC Appeals

Publisher: International Federation of Red Cross and Red Crescent Societies (IFRC) · Source: HDX · License: cc-by-igo · Updated: 2026-04-13

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Abstract

The International Federation of Red Cross and Red Crescent Societies (IFRC) is the world’s largest humanitarian network. Our secretariat supports local Red Cross and Red Crescent action in more than 192 countries, bringing together almost 15 million volunteers for the good of humanity.

We launch Emergency Appeals for big and complex disasters affecting lots of people who will need long-term support to recover. We also support Red Cross and Red Crescent Societies to respond to lots of small and medium-sized disasters worldwide—through our Disaster Response Emergency Fund (DREF) and in other ways.

There is also a global dataset.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-13. Geographic scope: UGA.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	61
Columns	41 (14 numeric, 19 categorical, 0 datetime)
Train split	48 rows
Test split	12 rows
Geographic scope	UGA
Publisher	International Federation of Red Cross and Red Crescent Societies (IFRC)
HDX last updated	2026-04-13

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Variables

Geographic — dtype_id (range 1.0–27.0), dtype_name (Epidemic, Population Movement, Flood), dtype_translation_module_original_language (en), atype (range 0.0–1.0), atype_display (DREF, Emergency Appeal) and 18 others.

Temporal — start_date, end_date, real_data_update.

Outcome / Measurement — amount_requested (range 0.0–11413603.0), amount_funded (range 0.0–7042851.23).

Identifier / Metadata — aid (range 180.0–18828.0), name (Uganda - Population Movement, Uganda, Uganda - Floods), code (MDRUG056, MDRUG023, MDRUG021), id (range 109.0–4045.0), esa_source and 1 others.

Other — status (range 0.0–1.0), sector (Country cluster for South Sudan, Tanzania and Uganda), created_at, modified_at, event (range 16.0–7448.0) and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ifrc-appeals-data-for-uganda")
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
aid	int64	0.0%	180.0 – 18828.0 (mean 8673.3115)
name	object	0.0%	Uganda - Population Movement, Uganda, Uganda - Floods
dtype_id	int64	0.0%	1.0 – 27.0 (mean 6.5738)
dtype_name	object	0.0%	Epidemic, Population Movement, Flood
dtype_translation_module_original_language	object	0.0%	en
atype	int64	0.0%	0.0 – 1.0 (mean 0.1639)
atype_display	object	0.0%	DREF, Emergency Appeal
status	int64	0.0%	0.0 – 1.0 (mean 0.9016)
status_display	object	0.0%	Closed, Active
code	object	0.0%	MDRUG056, MDRUG023, MDRUG021
sector	object	0.0%	Country cluster for South Sudan, Tanzania and Uganda
amount_requested	float64	0.0%	0.0 – 11413603.0 (mean 723834.2295)
amount_funded	float64	0.0%	0.0 – 7042851.23 (mean 441344.0135)
start_date	datetime64[ns, UTC]	0.0%
end_date	datetime64[ns, UTC]	0.0%
real_data_update	datetime64[ns, UTC]	0.0%
created_at	datetime64[ns, UTC]	0.0%
modified_at	datetime64[ns, UTC]	0.0%
event	float64	3.3%	16.0 – 7448.0 (mean 1987.3051)
needs_confirmation	bool	0.0%
country_iso	object	0.0%	UG
country_iso3	object	0.0%	UGA
country_id	int64	0.0%	176.0 – 176.0 (mean 176.0)
country_record_type	int64	0.0%	1.0 – 1.0 (mean 1.0)
country_record_type_display	object	0.0%	Country
country_region	int64	0.0%	0.0 – 0.0 (mean 0.0)
country_independent	bool	0.0%
country_is_deprecated	bool	0.0%
country_fdrs	object	0.0%
country_name	object	0.0%
country_society_name	object	0.0%
country_translation_module_original_language	object	0.0%
region_name	int64	0.0%	0.0 – 0.0 (mean 0.0)
region_id	int64	0.0%	0.0 – 0.0 (mean 0.0)
region_region_name	object	0.0%
region_label	object	0.0%
region_translation_module_original_language	object	0.0%
id	int64	0.0%	109.0 – 4045.0 (mean 2681.8361)
initial_num_beneficiaries	int64	0.0%	0.0 – 3628390.0 (mean 334034.5246)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
aid	180.0	18828.0	8673.3115	7623.0
dtype_id	1.0	27.0	6.5738	5.0
atype	0.0	1.0	0.1639	0.0
status	0.0	1.0	0.9016	1.0
amount_requested	0.0	11413603.0	723834.2295	185962.0
amount_funded	0.0	7042851.23	441344.0135	178676.0
event	16.0	7448.0	1987.3051	1059.0
country_id	176.0	176.0	176.0	176.0
country_record_type	1.0	1.0	1.0	1.0
country_region	0.0	0.0	0.0	0.0
region_name	0.0	0.0	0.0	0.0
region_id	0.0	0.0	0.0	0.0
id	109.0	4045.0	2681.8361	2717.0
initial_num_beneficiaries	0.0	3628390.0	334034.5246	18000.0

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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. 2 column(s) with >80% missing values were removed: dtype_summary, country_average_household_size. 5 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 International Federation of Red Cross and Red Crescent Societies (IFRC) 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_ifrc_appeals_data_for_uganda,
  title     = {Uganda - IFRC Appeals},
  author    = {International Federation of Red Cross and Red Crescent Societies (IFRC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ifrc-appeals-data-for-uganda},
  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.