---
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
- food-security
- dji
pretty_name: "Djibouti Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
  splits:
    - name: train
      num_examples: 320
    - name: test
      num_examples: 80
---

# Djibouti Current Situation FEWS NET Acute Food Insecurity Classifications Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/djibouti_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-01

---

## Abstract

Djibouti 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: **DJI**.

*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)** | 401 |
| **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) |
| **Train split** | 320 rows |
| **Test split** | 80 rows |
| **Geographic scope** | DJI |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `country` (Djibouti), `country_code` (DJ), `fewsnet_region` (East Africa), `unit_type` (fsc_admin_lhz, fsc_lhz), `specialization_type` and 2 others.

**Temporal** — `datacollectionperiod` (range 159830.0–159881.0), `reporting_date`.

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

**Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Security Outlook, Djibouti), `geographic_unit_full_name` (Central Pastoral - Highland, Balha, Tadjourah, Djibouti, Southeast Pastoral - Border, Ali Sabieh, Ali Sabieh, Djibouti, Market Gardening, Dikhil, Dikhil, Djibouti), `geographic_unit_name` (Central Pastoral - Lowland, Market Gardening, Southeast Pastoral - Roadside), `fnid` (DJ2012C305012B, DJ2012C301013B, DJ2012C3020204) and 8 others.

**Other** — `geographic_group` (Eastern 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-djibouti-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 |
| `source_document` | object | 0.0% | Food Security Outlook, Djibouti |
| `country` | object | 0.0% | Djibouti |
| `country_code` | object | 0.0% | DJ |
| `geographic_group` | object | 0.0% | Eastern Africa |
| `fewsnet_region` | object | 0.0% | East Africa |
| `geographic_unit_full_name` | object | 0.0% | Central Pastoral - Highland, Balha, Tadjourah, Djibouti, Southeast Pastoral - Border, Ali Sabieh, Ali Sabieh, Djibouti, Market Gardening, Dikhil, Dikhil, Djibouti |
| `geographic_unit_name` | object | 0.0% | Central Pastoral - Lowland, Market Gardening, Southeast Pastoral - Roadside |
| `unit_type` | object | 0.0% | fsc_admin_lhz, fsc_lhz |
| `fnid` | object | 0.0% | DJ2012C305012B, DJ2012C301013B, DJ2012C3020204 |
| `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 | 11.5% | 1.0 – 3.0 (mean 2.0592) |
| `description` | object | 11.5% |  |
| `id` | int64 | 0.0% | 24538359.0 – 24539559.0 (mean 24538959.0) |
| `datacollectionperiod` | int64 | 0.0% | 159830.0 – 159881.0 (mean 159862.7007) |
| `datacollection` | int64 | 0.0% | 168806.0 – 168823.0 (mean 168816.9002) |
| `scenario` | object | 0.0% |  |
| `geographic_unit` | int64 | 0.0% | 20943.0 – 20981.0 (mean 20963.1172) |
| `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `datasourcedocument` | int64 | 0.0% | 6621.0 – 6621.0 (mean 6621.0) |
| `dataseries` | int64 | 0.0% | 6516268.0 – 6516529.0 (mean 6516395.8953) |
| `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 | 2.0592 | 2.0 |
| `id` | 24538359.0 | 24539559.0 | 24538959.0 | 24538959.0 |
| `datacollectionperiod` | 159830.0 | 159881.0 | 159862.7007 | 159863.0 |
| `datacollection` | 168806.0 | 168823.0 | 168816.9002 | 168817.0 |
| `geographic_unit` | 20943.0 | 20981.0 | 20963.1172 | 20963.0 |
| `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 |
| `datasourcedocument` | 6621.0 | 6621.0 | 6621.0 | 6621.0 |
| `dataseries` | 6516268.0 | 6516529.0 | 6516395.8953 | 6516396.0 |

---

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

---

## Citation

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