electricsheepafrica/africa-chad-current-situation-fewsnet-fipe

---
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
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- tcd
pretty_name: "Chad Current Situation FEWS NET Acutely Food Insecure Population Estimates Data"
dataset_info:
splits:
- name: train
num_examples: 60
- name: test
num_examples: 15
---

# Chad Current Situation FEWS NET Acutely Food Insecure Population Estimates Data

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

---

## Abstract

Chad Current Situation FEWS NET Acutely Food Insecure Population Estimates Data from 2019

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: **TCD**.

*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)** | 75 |
| **Columns** | 44 (10 numeric, 27 categorical, 7 datetime) |
| **Train split** | 60 rows |
| **Test split** | 15 rows |
| **Geographic scope** | TCD |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `country` (Chad), `country_code` (TD), `fewsnet_region` (West Africa), `admin_0` (Chad), `specialization_type` and 3 others.

**Temporal** — `datacollectionperiod` (range 310322.0–373072.0), `reporting_date`.

**Outcome / Measurement** — `phase`, `low_value` (range 100000.0–2000000.0), `high_value` (range 499999.0–2499999.0), `value` (range 100000.0–2000000.0), `phase_name`.

**Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Assistance Outlook Brief), `geographic_unit_full_name` (Chad), `geographic_unit_name` (Chad), `fnid` (TD) and 8 others.

**Other** — `geographic_group` (Middle Africa), `indicator_abbreviation`, `projection_start`, `projection_end`, `status` and 11 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-chad-current-situation-fewsnet-fipe")
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 Assistance Outlook Brief |
| `country` | object | 0.0% | Chad |
| `country_code` | object | 0.0% | TD |
| `geographic_group` | object | 0.0% | Middle Africa |
| `fewsnet_region` | object | 0.0% | West Africa |
| `geographic_unit_full_name` | object | 0.0% | Chad |
| `geographic_unit_name` | object | 0.0% | Chad |
| `fnid` | object | 0.0% | TD |
| `admin_0` | object | 0.0% | Chad |
| `phase` | object | 0.0% | |
| `scenario_name` | object | 0.0% | |
| `indicator_name` | object | 0.0% | |
| `indicator_abbreviation` | object | 0.0% | |
| `projection_start` | datetime64[ns] | 0.0% | |
| `projection_end` | datetime64[ns] | 0.0% | |
| `status` | object | 0.0% | |
| `low_value` | float64 | 0.0% | 100000.0 – 2000000.0 (mean 529333.3333) |
| `high_value` | float64 | 0.0% | 499999.0 – 2499999.0 (mean 996665.6667) |
| `value` | float64 | 0.0% | 100000.0 – 2000000.0 (mean 529333.3333) |
| `id` | int64 | 0.0% | 33126785.0 – 40657280.0 (mean 34149649.6667) |
| `datacollectionperiod` | int64 | 0.0% | 310322.0 – 373072.0 (mean 319279.3333) |
| `datacollection` | int64 | 0.0% | 325936.0 – 383437.0 (mean 333815.6) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 8015.0 – 8015.0 (mean 8015.0) |
| `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `datasourcedocument` | int64 | 0.0% | 6986.0 – 6986.0 (mean 6986.0) |
| `dataseries` | int64 | 0.0% | 6932830.0 – 6932830.0 (mean 6932830.0) |
| `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% | |
| `phase_name` | object | 0.0% | |
| `population_range` | object | 0.0% | |
| `description` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `low_value` | 100000.0 | 2000000.0 | 529333.3333 | 500000.0 |
| `high_value` | 499999.0 | 2499999.0 | 996665.6667 | 749999.0 |
| `value` | 100000.0 | 2000000.0 | 529333.3333 | 500000.0 |
| `id` | 33126785.0 | 40657280.0 | 34149649.6667 | 33128897.0 |
| `datacollectionperiod` | 310322.0 | 373072.0 | 319279.3333 | 310396.0 |
| `datacollection` | 325936.0 | 383437.0 | 333815.6 | 325973.0 |
| `geographic_unit` | 8015.0 | 8015.0 | 8015.0 | 8015.0 |
| `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 |
| `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 |
| `dataseries` | 6932830.0 | 6932830.0 | 6932830.0 | 6932830.0 |

---

## 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`. 7 column(s) with >80% missing values were removed: `admin_1`, `admin_2`, `admin_3`, `admin_4`, `pct_phase3`, `pct_phase4`.... 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/chad_current_situation_fewsnet_fipe) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_chad_current_situation_fewsnet_fipe,
title = {Chad Current Situation FEWS NET Acutely Food Insecure Population Estimates Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/chad_current_situation_fewsnet_fipe},
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.*