electricsheepafrica/africa-global-hpc-hno

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification - tabular-regression task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - humanitarian-needs-overview-hno - hxl - people-in-need-pin - afg - bfa - cmr - caf - tcd pretty_name: "Global Humanitarian Programme Cycle, Humanitarian Needs" dataset_info: splits: - name: train num_examples: 107 - name: test num_examples: 26 --- # Global Humanitarian Programme Cycle, Humanitarian Needs **Publisher:** OCHA Humanitarian Programme Cycle Tools (HPC Tools) · **Source:** [HDX](https://data.humdata.org/dataset/global-hpc-hno) · **License:** `cc-by-igo` · **Updated:** 2026-02-13 --- ## Abstract This dataset contains standardised Humanitarian Needs Overview data taken from the OCHA HPC Tools system which is under active development. For more detailed but less standardized data on humanitarian needs, see the [Humanitarian Needs Overview data series](https://data.humdata.org/dataset/?dataseries_name=Humanitarian+Needs+Overview). Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-02-13. Geographic scope: **AFG, BFA, CMR, CAF, TCD, COL, COD, SLV, and 16 others**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Humanitarian and development data | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 134 | | **Columns** | 7 (2 numeric, 5 categorical, 0 datetime) | | **Train split** | 107 rows | | **Test split** | 26 rows | | **Geographic scope** | AFG, BFA, CMR, CAF, TCD, COL, COD, SLV, and 16 others | | **Publisher** | OCHA Humanitarian Programme Cycle Tools (HPC Tools) | | **HDX last updated** | 2026-02-13 | --- ## Variables **Geographic** — `country_iso3` (COD, CAF, TCD). **Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-07). **Other** — `description` (Education, Nutrition, GHO Estimates), `cluster` (ALL, PRO, EDU), `in_need` (range 73000.0–33699770.0), `targeted` (range 33445.0–20419163.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-global-hpc-hno") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_iso3` | object | 0.0% | COD, CAF, TCD | | `description` | object | 0.0% | Education, Nutrition, GHO Estimates | | `cluster` | object | 0.0% | ALL, PRO, EDU | | `in_need` | float64 | 4.5% | 73000.0 – 33699770.0 (mean 4712172.7266) | | `targeted` | int64 | 0.0% | 33445.0 – 20419163.0 (mean 2097727.9552) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-07 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `in_need` | 73000.0 | 33699770.0 | 4712172.7266 | 3233415.5 | | `targeted` | 33445.0 | 20419163.0 | 2097727.9552 | 1236533.0 | --- ## 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`. 5 column(s) with >80% missing values were removed: `category`, `population`, `affected`, `reached`, `info`. 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 OCHA Humanitarian Programme Cycle Tools (HPC Tools) 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 24 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/global-hpc-hno) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_global_hpc_hno, title = {Global Humanitarian Programme Cycle, Humanitarian Needs}, author = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)}, year = {2026}, url = {https://data.humdata.org/dataset/global-hpc-hno}, 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.*