---
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-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- el-nino-el-nina
- food-security
- geodata
- humanitarian-needs-overview-hno
- nutrition
- ago
- bol
- bwa
- khm
- tcd
pretty_name: "WFP and FAO Overview of Countries Affected by the El Niño"
dataset_info:
  splits:
    - name: train
      num_examples: 29
    - name: test
      num_examples: 7
---

# WFP and FAO Overview of Countries Affected by the El Niño

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/wfp-and-fao-overview-of-countries-affected-by-the-2015-16-el-nino) · **License:** `cc-by-igo` · **Updated:** 2025-06-12

---

## Abstract

This dataset contains a list of the countries affected by the El Niño as at April 21, 2016 as reported jointly by FAO, the Global Food Security Cluster and WFP on 21 April 2016 in the [2015-2016 El Niño: WFP and FAO Overview](http://www.foodsecuritycluster.net/document/2015-2016-el-nino-wfp-and-fao-overview) update. According to the World Bank, El Niño is likely to have a negative impact in more isolated local food markets, and many countries are already facing increased food prices. Food Security Cluster partners have implemented preparedness activities and are responding in countries where the effects of El Niño have materialised, such as Ethiopia, Papua New Guinea, Malawi and throughout Central America. In Southern Africa, many areas have seen the driest October-December
period since at least 1981, and some 14 million people in the region are already facing hunger, which adds to fears of a spike in the numbers of the food insecure later this year through 2017.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-06-12. Geographic scope: **AGO, BOL, BWA, KHM, TCD, COL, PRK, DJI, and 29 others**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 37 |
| **Columns** | 7 (1 numeric, 6 categorical, 0 datetime) |
| **Train split** | 29 rows |
| **Test split** | 7 rows |
| **Geographic scope** | AGO, BOL, BWA, KHM, TCD, COL, PRK, DJI, and 29 others |
| **Publisher** | HDX |
| **HDX last updated** | 2025-06-12 |

---

## Variables

**Geographic** — `country` (Bolivia, Mozambique, Somalia), `iso3` (BOL, MOZ, SOM), `priority` (High Priority, Moderately Affected), `region` (Africa, Latin America and the Caribbean, Asia and the Pacific), `prioritycode` (range 1.0–2.0).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-05).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-and-fao-overview-of-countries-affected-by-the-2015-16-el-nino")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country` | object | 0.0% | Bolivia, Mozambique, Somalia |
| `iso3` | object | 0.0% | BOL, MOZ, SOM |
| `priority` | object | 0.0% | High Priority, Moderately Affected |
| `region` | object | 0.0% | Africa, Latin America and the Caribbean, Asia and the Pacific |
| `prioritycode` | int64 | 0.0% | 1.0 – 2.0 (mean 1.6486) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-05 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `prioritycode` | 1.0 | 2.0 | 1.6486 | 2.0 |

---

## 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`. 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 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 37 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/wfp-and-fao-overview-of-countries-affected-by-the-2015-16-el-nino) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_wfp_and_fao_overview_of_countries_affected_by_the_2015_16_el_nino,
  title     = {WFP and FAO Overview of Countries Affected by the El Niño},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/wfp-and-fao-overview-of-countries-affected-by-the-2015-16-el-nino},
  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.*