electricsheepafrica/africa-nigeria-current-situation-fewsnet-ipc-classification

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- nga
pretty_name: "Nigeria Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
splits:
- name: train
num_examples: 6645
- name: test
num_examples: 1661
---

# Nigeria Current Situation FEWS NET Acute Food Insecurity Classifications Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/nigeria_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-03

---

## Abstract

Nigeria Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `projection_start`, `projection_end` column(s). Geographic scope: **NGA**.

*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)** | 8,307 |
| **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) |
| **Train split** | 6,645 rows |
| **Test split** | 1,661 rows |
| **Geographic scope** | NGA |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-03 |

---

## Variables

**Geographic** — `country` (Nigeria), `country_code` (NG), `fewsnet_region` (West Africa), `unit_type` (fsc_admin_lhz, idp_camp, fsc_admin), `specialization_type` and 2 others.

**Temporal** — `datacollectionperiod` (range 158627.0–378157.0), `reporting_date`.

**Outcome / Measurement** — `value` (range 1.0–4.0).

**Identifier / Metadata** — `source_organization` (FEWS NET, Nigeria), `source_document` (Food Security Outlook, Nigeria), `geographic_unit_full_name` (Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Yola North, Adamawa, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Delta, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Nasarawa, Nigeria), `geographic_unit_name` (Sokoto millet, cowpeas, groundnuts, and livestock, Sokoto-Rima-Kano riverine flood plain rice and fishing, Kano-Katsina sahelian: millet, sorghum, sesame, and livestock), `fnid` (NG2014C3020806, NG2014C3100003, NG2014C3090003) and 8 others.

**Other** — `geographic_group` (Western Africa), `classification_scale`, `is_allowing_for_assistance`, `projection_start`, `projection_end` and 12 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-current-situation-fewsnet-ipc-classification")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `source_organization` | object | 0.0% | FEWS NET, Nigeria |
| `source_document` | object | 0.0% | Food Security Outlook, Nigeria |
| `country` | object | 0.0% | Nigeria |
| `country_code` | object | 0.0% | NG |
| `geographic_group` | object | 0.0% | Western Africa |
| `fewsnet_region` | object | 0.0% | West Africa |
| `geographic_unit_full_name` | object | 0.0% | Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Yola North, Adamawa, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Delta, Nigeria, Niger and Benue rivers flood plain rice with maize, vegetables, and livestock, Nasarawa, Nigeria |
| `geographic_unit_name` | object | 0.0% | Sokoto millet, cowpeas, groundnuts, and livestock, Sokoto-Rima-Kano riverine flood plain rice and fishing, Kano-Katsina sahelian: millet, sorghum, sesame, and livestock |
| `unit_type` | object | 0.0% | fsc_admin_lhz, idp_camp, fsc_admin |
| `fnid` | object | 0.0% | NG2014C3020806, NG2014C3100003, NG2014C3090003 |
| `classification_scale` | object | 0.0% | |
| `scenario_name` | object | 0.0% | |
| `preference_rating` | int64 | 0.0% | 90.0 – 90.0 (mean 90.0) |
| `is_allowing_for_assistance` | bool | 0.0% | |
| `projection_start` | datetime64[ns] | 0.0% | |
| `projection_end` | datetime64[ns] | 0.0% | |
| `status` | object | 0.0% | |
| `value` | float64 | 0.1% | 1.0 – 4.0 (mean 1.8507) |
| `description` | object | 0.1% | |
| `id` | int64 | 0.0% | 24370752.0 – 41453119.0 (mean 28815044.581) |
| `datacollectionperiod` | int64 | 0.0% | 158627.0 – 378157.0 (mean 235566.5855) |
| `datacollection` | int64 | 0.0% | 168346.0 – 388949.0 (mean 247534.1567) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 25361.0 – 208448.0 (mean 129100.4666) |
| `datasourceorganization` | int64 | 0.0% | 2033.0 – 2033.0 (mean 2033.0) |
| `datasourcedocument` | int64 | 0.0% | 6573.0 – 6573.0 (mean 6573.0) |
| `dataseries` | int64 | 0.0% | 6481912.0 – 7226045.0 (mean 6639518.3507) |
| `dataseries_name` | object | 0.0% | |
| `specialization_type` | object | 0.0% | |
| `dataseries_specialization_type` | object | 0.0% | |
| `data_usage_policy` | object | 0.0% | |
| `created` | datetime64[ns] | 0.0% | |
| `modified` | datetime64[ns] | 0.0% | |
| `status_changed` | datetime64[ns] | 0.0% | |
| `collection_status` | object | 0.0% | |
| `collection_status_changed` | datetime64[ns] | 0.0% | |
| `collection_schedule` | object | 0.0% | |
| `reporting_date` | datetime64[ns] | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 |
| `value` | 1.0 | 4.0 | 1.8507 | 2.0 |
| `id` | 24370752.0 | 41453119.0 | 28815044.581 | 26094482.0 |
| `datacollectionperiod` | 158627.0 | 378157.0 | 235566.5855 | 222028.0 |
| `datacollection` | 168346.0 | 388949.0 | 247534.1567 | 235103.0 |
| `geographic_unit` | 25361.0 | 208448.0 | 129100.4666 | 174655.0 |
| `datasourceorganization` | 2033.0 | 2033.0 | 2033.0 | 2033.0 |
| `datasourcedocument` | 6573.0 | 6573.0 | 6573.0 | 6573.0 |
| `dataseries` | 6481912.0 | 7226045.0 | 6639518.3507 | 6504314.0 |

---

## 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`. 3 column(s) with >80% missing values were removed: `pct_phase3`, `pct_phase4`, `pct_phase5`. 7 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 FEWS NET 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](https://data.humdata.org/dataset/nigeria_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_current_situation_fewsnet_ipc_classification,
title = {Nigeria Current Situation FEWS NET Acute Food Insecurity Classifications Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/nigeria_current_situation_fewsnet_ipc_classification},
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.*