electricsheepafrica/africa-east-africa-chirps-seasonal-rainfall-accumulation-anomaly-by-pentad

East Africa - CHIRPS Seasonal Rainfall Accumulation Anomaly by Pentad

Publisher: HDX · Source: HDX · License: cc-by · Updated: 2025-09-12

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Abstract

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. It is a gridded rainfall time series for trend analysis and seasonal drought monitoring, spans 50°S-50°N (and all longitudes) and ranges from 1981 to near-present. The anomaly refers to the difference between current rainfall and the average rainfall that occurred between 1981 and 2010 in millimeters. For more information visit the CHIRPS site.

This dataset contains the latest available CHIRPS anomaly data. The full list of data available is available from USGS for Mar-May data, Oct-Dec data, and others.

Additionally, subnational statistics (mean, min, max) have been calculated for Ethiopia, Kenya, and Somalia and are available in the csv resource.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-09-12. Geographic scope: ETH, KEN, SOM.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Tabular records
Rows (total)	2,652
Columns	18 (4 numeric, 14 categorical, 0 datetime)
Train split	2,121 rows
Test split	530 rows
Geographic scope	ETH, KEN, SOM
Publisher	HDX
HDX last updated	2025-09-12

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Variables

Geographic — chirps_max (range -148.0575–1502.7864).

Temporal — season.

Identifier / Metadata — adm0_pcode (ET, SO, KE), adm0_ref (Ethiopia, Somalia, Kenya), adm1_pcode (ET04, ET03, ET07), adm1_ref (Oromia, Amhara, SNNP), adm2_pcode (ET1600, ET0305, ET0304) and 7 others.

Other — alpha_3 (ETH, SOM, KEN), adm_level (range 1.0–3.0), chirps_mean (range -148.0575–837.3773), chirps_min (range -284.2439–544.8214).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-east-africa-chirps-seasonal-rainfall-accumulation-anomaly-by-pentad")
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
alpha_3	object	0.0%	ETH, SOM, KEN
adm0_pcode	object	0.0%	ET, SO, KE
adm0_ref	object	0.0%	Ethiopia, Somalia, Kenya
adm1_pcode	object	0.0%	ET04, ET03, ET07
adm1_ref	object	0.0%	Oromia, Amhara, SNNP
adm2_pcode	object	5.9%	ET1600, ET0305, ET0304
adm2_ref	object	5.9%	Sidama, North Shewa (AM), South Wello
adm3_pcode	object	18.4%	ET010101, ET050503, ET050392
adm3_ref	object	18.4%	Tahtay Adiyabo, Shilabo, Daror
adm_level	int64	0.0%	1.0 – 3.0 (mean 2.7572)
adm_pcode	object	0.0%	ET010101, ET070706, ET070704
adm_ref	object	0.0%
season	object	0.0%
chirps_mean	float64	10.5%	-148.0575 – 837.3773 (mean 69.3937)
chirps_min	float64	10.5%	-284.2439 – 544.8214 (mean 32.4136)
chirps_max	float64	10.5%	-148.0575 – 1502.7864 (mean 113.4136)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
adm_level	1.0	3.0	2.7572	3.0
chirps_mean	-148.0575	837.3773	69.3937	29.6887
chirps_min	-284.2439	544.8214	32.4136	5.2211
chirps_max	-148.0575	1502.7864	113.4136	64.4659

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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. 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 HDX and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 3 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_east_africa_chirps_seasonal_rainfall_accumulation_anomaly_by_pentad,
  title     = {East Africa - CHIRPS Seasonal Rainfall Accumulation Anomaly by Pentad},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/east-africa-chirps-seasonal-rainfall-accumulation-anomaly-by-pentad},
  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.