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africa-zmb-rainfall-subnational

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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - climate-weather
  - environment
  - zmb
pretty_name: 'Zambia: Rainfall Indicators at Subnational Level'
dataset_info:
  splits:
    - name: train
      num_examples: 147352
    - name: test
      num_examples: 36838

Zambia: Rainfall Indicators at Subnational Level

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by · Updated: 2026-04-03

Abstract

This dataset contains dekadal rainfall indicators, computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2 and the CHIRPS-GEFS short term rainfall forecasts, aggregated by subnational administrative units.

Included indicators are (for each dekad):

  • 10 day rainfall [mm] (rfh)
  • rainfall 1-month rolling aggregation [mm] (r1h)
  • rainfall 3-month rolling aggregation [mm] (r3h)
  • rainfall long term average [mm] (rfh_avg)
  • rainfall 1-month rolling aggregation long term average [mm] (r1h_avg)
  • rainfall 3-month rolling aggregation long term average [mm] (r3h_avg)
  • rainfall anomaly [%] (rfq)
  • rainfall 1-month anomaly [%] (r1q)
  • rainfall 3-month anomaly [%] (r3q)

The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the n_pixels column. Finally, the type column indicates if the value is based on a forecast, a preliminary or a final product.

Forecasts are issued on the 6th, 16th, and 26th of each month for the upcoming 10-day period (dekad), then updated with improved versions on the 1st, 11th, and 21st. Preliminary observations replace the previous dekad’s forecast on the 3rd, 13th, and 23rd, and are later replaced by final observations—published mid-month (13th or 23rd)—covering all three dekads of the prior month. Please find a summary below:

Publication Day: Forecast type, Covers (Dekad)

  • 1st: Updated forecast, 1–10 of the same month
  • 6th: Initial forecast, 11–20 of the same month
  • 11th: Updated forecast, 1–10 of the same month
  • 16th: Initial forecast, 21–end of the same month
  • 21st: Updated forecast, 11–20 of the same month
  • 26th: Initial forecast, 1–10 of the following month

For more on CHIRPS-GEFS forecasts, see: https://www.chc.ucsb.edu/data/chirps-gefs

For further details, please see the methodology section.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the date column(s). Geographic scope: ZMB.

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

Dataset Characteristics

Domain Climate and environment
Unit of observation Time-series observations
Rows (total) 184,190
Columns 17 (12 numeric, 4 categorical, 1 datetime)
Train split 147,352 rows
Test split 36,838 rows
Geographic scope ZMB
Publisher WFP - World Food Programme
HDX last updated 2026-04-03

Variables

Geographic — n_pixels (range 16.0–4287.0).

Temporal — date.

Identifier / Metadata — adm_id (range 900844.0–1009334.0), pcode (ZM101, ZM104007, ZM105002), esa_source (HDX), esa_processed (2026-04-07).

Other — adm_level (range 1.0–2.0), rfh (range 0.0–255.9512), rfh_avg (range 0.0–126.8143), r1h (range 0.0–575.973), r1h_avg (range 0.0–330.1505) and 6 others.

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-zmb-rainfall-subnational")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
date datetime64[ns] 0.0%
adm_level int64 0.0% 1.0 – 2.0 (mean 1.9115)
adm_id int64 0.0% 900844.0 – 1009334.0 (mean 999687.0265)
pcode object 0.0% ZM101, ZM104007, ZM105002
n_pixels float64 0.0% 16.0 – 4287.0 (mean 436.9027)
rfh float64 0.0% 0.0 – 255.9512 (mean 27.2744)
rfh_avg float64 0.0% 0.0 – 126.8143 (mean 27.7041)
r1h float64 0.1% 0.0 – 575.973 (mean 81.7575)
r1h_avg float64 0.1% 0.0 – 330.1505 (mean 83.0071)
r3h float64 0.5% 0.0 – 1153.1052 (mean 243.7905)
r3h_avg float64 0.5% 0.0 – 844.0457 (mean 247.8506)
rfq float64 0.0% 9.9511 – 617.6783 (mean 100.0855)
r1q float64 0.1% 9.1487 – 520.4519 (mean 100.2781)
r3q float64 0.5% 17.7114 – 495.4942 (mean 100.1718)
version object 0.0% final, prelim, forecast
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-07

Numeric Summary

Column Min Max Mean Median
adm_level 1.0 2.0 1.9115 2.0
adm_id 900844.0 1009334.0 999687.0265 1009278.0
n_pixels 16.0 4287.0 436.9027 214.0
rfh 0.0 255.9512 27.2744 5.8488
rfh_avg 0.0 126.8143 27.7041 8.5587
r1h 0.0 575.973 81.7575 26.3316
r1h_avg 0.0 330.1505 83.0071 29.3096
r3h 0.0 1153.1052 243.7905 146.9144
r3h_avg 0.0 844.0457 247.8506 158.035
rfq 9.9511 617.6783 100.0855 99.9874
r1q 9.1487 520.4519 100.2781 99.9037
r3q 17.7114 495.4942 100.1718 98.7447

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. 1 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.

Limitations

  • Data originates from WFP - World Food Programme 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.

Citation 

@dataset{hdx_africa_zmb_rainfall_subnational,
  title     = {Zambia: Rainfall Indicators at Subnational Level},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/zmb-rainfall-subnational},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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