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

Egypt - 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: EGY.

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)	26
Columns	41 (14 numeric, 19 categorical, 0 datetime)
Train split	20 rows
Test split	5 rows
Geographic scope	EGY
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 1.0–54.0), dtype_name (Civil Unrest, Flood, Transport Accident), 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 0.0–1018000.0), amount_funded (range 0.0–500000.0).

Identifier / Metadata — aid (range 165.0–19141.0), name (Egypt - Floods, Egypt, Egypt - Train Crash), code (MDREG022, MDREG020, M97ME039), id (range 126.0–4117.0), esa_source and 1 others.

Other — status (range 0.0–1.0), sector (Middle East and North Africa, Tunis country cluster), created_at, modified_at, event (range 50.0–7659.0) and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ifrc-appeals-data-for-egypt")
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%	165.0 – 19141.0 (mean 7959.9231)
name	object	0.0%	Egypt - Floods, Egypt, Egypt - Train Crash
dtype_id	int64	0.0%	1.0 – 54.0 (mean 15.3846)
dtype_name	object	0.0%	Civil Unrest, Flood, Transport Accident
dtype_translation_module_original_language	object	0.0%	en
atype	int64	0.0%	0.0 – 1.0 (mean 0.0769)
atype_display	object	0.0%	DREF, Emergency Appeal
status	int64	0.0%	0.0 – 1.0 (mean 0.9231)
status_display	object	0.0%	Closed, Active
code	object	0.0%	MDREG022, MDREG020, M97ME039
sector	object	0.0%	Middle East and North Africa, Tunis country cluster
amount_requested	float64	0.0%	0.0 – 1018000.0 (mean 198106.1154)
amount_funded	float64	0.0%	0.0 – 500000.0 (mean 158952.2692)
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	int64	0.0%	50.0 – 7659.0 (mean 2329.6154)
needs_confirmation	bool	0.0%
country_iso	object	0.0%	EG
country_iso3	object	0.0%	EGY
country_id	int64	0.0%	61.0 – 61.0 (mean 61.0)
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%	4.0 – 4.0 (mean 4.0)
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%	4.0 – 4.0 (mean 4.0)
region_id	int64	0.0%	4.0 – 4.0 (mean 4.0)
region_region_name	object	0.0%
region_label	object	0.0%
region_translation_module_original_language	object	0.0%
id	int64	0.0%	126.0 – 4117.0 (mean 2210.7308)
initial_num_beneficiaries	int64	0.0%	0.0 – 40000.0 (mean 3997.6923)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
aid	165.0	19141.0	7959.9231	6630.5
dtype_id	1.0	54.0	15.3846	9.5
atype	0.0	1.0	0.0769	0.0
status	0.0	1.0	0.9231	1.0
amount_requested	0.0	1018000.0	198106.1154	105768.0
amount_funded	0.0	500000.0	158952.2692	101932.0
event	50.0	7659.0	2329.6154	1323.0
country_id	61.0	61.0	61.0	61.0
country_record_type	1.0	1.0	1.0	1.0
country_region	4.0	4.0	4.0	4.0
region_name	4.0	4.0	4.0	4.0
region_id	4.0	4.0	4.0	4.0
id	126.0	4117.0	2210.7308	1907.5
initial_num_beneficiaries	0.0	40000.0	3997.6923	305.0

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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_egypt,
  title     = {Egypt - 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-egypt},
  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.