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electricsheepasia/asia-climate-hdx-hapi-kazakhstan

HDX HAPI Data for Kazakhstan Publisher: HDX Humanitarian API Data · Source: HDX · License: hdx-other · Updated: 2026-02-18 Abstract This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-climate-hdx-hapi-kazakhstan.

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Dataset Card

HDX HAPI Data for Kazakhstan

Publisher: HDX Humanitarian API Data · Source: HDX · License: hdx-other · Updated: 2026-02-18


Abstract

This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX HAPI landing page and documentation.

Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the reference_period_start, reference_period_end column(s). Geographic scope: KAZ.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).


Dataset Characteristics

DomainFood security and nutrition
Unit of observationGeolocated point observations
Rows (total)16,757
Columns16 (3 numeric, 7 categorical, 2 datetime)
Train split13,405 rows
Test split3,351 rows
Geographic scopeKAZ
PublisherHDX Humanitarian API Data
HDX last updated2026-02-18

Variables

Geographic — origin_location_code (KAZ, AFG, CHN), asylum_location_code (KAZ, CAN, USA), asylum_has_hrp, asylum_in_gho, population_group (REF, ASY, OOC) and 2 others.

Temporal — reference_period_start, reference_period_end.

Demographic — gender (f, m, all), age_range (all, 0-4, 5-11), min_age (range 0.0–60.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-05).

Other — origin_has_hrp, origin_in_gho.


Quick Start

python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-climate-hdx-hapi-kazakhstan")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

ColumnTypeNull %Range / Sample Values
origin_location_codeobject0.0%KAZ, AFG, CHN
origin_has_hrpbool0.0%
origin_in_ghobool0.0%
asylum_location_codeobject0.0%KAZ, CAN, USA
asylum_has_hrpbool0.0%
asylum_in_ghobool0.0%
population_groupobject0.0%REF, ASY, OOC
genderobject0.0%f, m, all
age_rangeobject0.0%all, 0-4, 5-11
min_agefloat6423.1%0.0 – 60.0 (mean 19.0)
max_agefloat6438.5%4.0 – 59.0 (mean 22.75)
populationint640.0%0.0 – 13695.0 (mean 29.3857)
reference_period_startdatetime64[ns]0.0%
reference_period_enddatetime64[ns]0.0%
esa_sourceobject0.0%HDX
esa_processedobject0.0%2026-05-05

Numeric Summary

ColumnMinMaxMeanMedian
min_age0.060.019.012.0
max_age4.059.022.7514.0
population0.013695.029.38570.0

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) 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 HDX Humanitarian API Data and has not been independently validated by ESA.
  • —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • —The following columns have >20% missing values and should be treated with caution in modelling: min_age, max_age.
  • —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

bibtex
@dataset{hdx_asia_climate_hdx_hapi_kazakhstan,
  title     = {HDX HAPI Data for Kazakhstan},
  author    = {HDX Humanitarian API Data},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hdx-hapi-kaz},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.