electricsheepafrica/africa-niger-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
- ner
pretty_name: "Niger Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
splits:
- name: train
num_examples: 5456
- name: test
num_examples: 1364
---

# Niger Current Situation FEWS NET Acute Food Insecurity Classifications Data

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

---

## Abstract

Niger 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: **NER**.

*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)** | 6,821 |
| **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) |
| **Train split** | 5,456 rows |
| **Test split** | 1,364 rows |
| **Geographic scope** | NER |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-03 |

---

## Variables

**Geographic** — `country` (Niger), `country_code` (NE), `fewsnet_region` (West Africa), `unit_type` (fsc_admin_lhz), `specialization_type` and 2 others.

**Temporal** — `datacollectionperiod` (range 158390.0–377285.0), `reporting_date`.

**Outcome / Measurement** — `value` (range 1.0–3.0).

**Identifier / Metadata** — `source_organization` (FEWS NET, Niger), `source_document` (Food Security Outlook, Niger), `geographic_unit_full_name` (Agropastoral Belt, Abalak, Tahoua, Niger, Southwestern Cereals with Fan-Palm Products, Gaya, Dosso, Niger, Southern Irrigated Cash Crops, Madaoua, Tahoua, Niger), `geographic_unit_name` (Rainfed Millet and Sorghum Belt, Agropastoral Belt, Transhumant and Nomad Pastoralism), `fnid` (NE2012C3050204, NE2012C3070214, NE2012C3060210) 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-niger-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, Niger |
| `source_document` | object | 0.0% | Food Security Outlook, Niger |
| `country` | object | 0.0% | Niger |
| `country_code` | object | 0.0% | NE |
| `geographic_group` | object | 0.0% | Western Africa |
| `fewsnet_region` | object | 0.0% | West Africa |
| `geographic_unit_full_name` | object | 0.0% | Agropastoral Belt, Abalak, Tahoua, Niger, Southwestern Cereals with Fan-Palm Products, Gaya, Dosso, Niger, Southern Irrigated Cash Crops, Madaoua, Tahoua, Niger |
| `geographic_unit_name` | object | 0.0% | Rainfed Millet and Sorghum Belt, Agropastoral Belt, Transhumant and Nomad Pastoralism |
| `unit_type` | object | 0.0% | fsc_admin_lhz |
| `fnid` | object | 0.0% | NE2012C3050204, NE2012C3070214, NE2012C3060210 |
| `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 | 0.2% | 1.0 – 3.0 (mean 1.4152) |
| `description` | object | 0.2% | |
| `id` | int64 | 0.0% | 24350017.0 – 41224446.0 (mean 27552804.3935) |
| `datacollectionperiod` | int64 | 0.0% | 158390.0 – 377285.0 (mean 212533.6596) |
| `datacollection` | int64 | 0.0% | 168123.0 – 388211.0 (mean 223775.4724) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 25144.0 – 257729.0 (mean 79763.6297) |
| `datasourceorganization` | int64 | 0.0% | 2032.0 – 2032.0 (mean 2032.0) |
| `datasourcedocument` | int64 | 0.0% | 6569.0 – 6569.0 (mean 6569.0) |
| `dataseries` | int64 | 0.0% | 6467681.0 – 7843391.0 (mean 6598560.0733) |
| `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 | 3.0 | 1.4152 | 1.0 |
| `id` | 24350017.0 | 41224446.0 | 27552804.3935 | 24472794.0 |
| `datacollectionperiod` | 158390.0 | 377285.0 | 212533.6596 | 159229.0 |
| `datacollection` | 168123.0 | 388211.0 | 223775.4724 | 168604.0 |
| `geographic_unit` | 25144.0 | 257729.0 | 79763.6297 | 25348.0 |
| `datasourceorganization` | 2032.0 | 2032.0 | 2032.0 | 2032.0 |
| `datasourcedocument` | 6569.0 | 6569.0 | 6569.0 | 6569.0 |
| `dataseries` | 6467681.0 | 7843391.0 | 6598560.0733 | 6486884.0 |

---

## 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/niger_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats.

---

## Citation

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