electricsheepafrica/africa-climate-nigeria

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- baseline-population
- climate-weather
- conflict-violence
- displacement
- economics
- education
- food-security
- funding
- nga
pretty_name: "HDX HAPI Data for Nigeria"
dataset_info:
splits:
- name: train
num_examples: 10121
- name: test
num_examples: 2530
---

# HDX HAPI Data for Nigeria

**Publisher:** HDX Humanitarian API Data · **Source:** [HDX](https://data.humdata.org/dataset/hdx-hapi-nga) · **License:** `hdx-other` · **Updated:** 2026-02-18

---

## Abstract

This dataset contains data obtained from the
[HDX Humanitarian API](https://hapi.humdata.org/) (HDX HAPI),
which provides standardized humanitarian indicators designed
for seamless interoperability from multiple sources.
The data facilitates automated workflows and visualizations
to support humanitarian decision making.
For more information, please see the HDX HAPI
[landing page](https://data.humdata.org/hapi)
and
[documentation](https://hdx-hapi.readthedocs.io/en/latest/).

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `reference_period_start`, `reference_period_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** | Subnational administrative unit observations |
| **Rows (total)** | 12,652 |
| **Columns** | 18 (3 numeric, 11 categorical, 2 datetime) |
| **Train split** | 10,121 rows |
| **Test split** | 2,530 rows |
| **Geographic scope** | NGA |
| **Publisher** | HDX Humanitarian API Data |
| **HDX last updated** | 2026-02-18 |

---

## Variables

**Geographic** — `location_code` (NGA), `provider_admin1_name` (Borno, Adamawa, Bauchi), `provider_admin2_name` (Yola South, Gombi, Yola North), `admin1_code` (NG008, NG002, NG005), `admin1_name` (Borno, Adamawa, Bauchi) and 5 others.

**Temporal** — `reference_period_start`, `reference_period_end`.

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed`.

**Other** — `has_hrp`, `in_gho`, `operation` (Lake Chad Basin Crisis, North Central and North West), `reporting_round` (range 1.0–50.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-climate-nigeria")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `location_code` | object | 0.0% | NGA |
| `has_hrp` | bool | 0.0% | |
| `in_gho` | bool | 0.0% | |
| `provider_admin1_name` | object | 0.8% | Borno, Adamawa, Bauchi |
| `provider_admin2_name` | object | 6.5% | Yola South, Gombi, Yola North |
| `admin1_code` | object | 0.8% | NG008, NG002, NG005 |
| `admin1_name` | object | 0.8% | Borno, Adamawa, Bauchi |
| `admin2_code` | object | 6.5% | NG002021, NG002004, NG002020 |
| `admin2_name` | object | 6.5% | Yola South, Gombi, Yola North |
| `admin_level` | int64 | 0.0% | 0.0 – 2.0 (mean 1.9264) |
| `operation` | object | 0.0% | Lake Chad Basin Crisis, North Central and North West |
| `assessment_type` | object | 0.0% | SA, BA |
| `population` | int64 | 0.0% | 7.0 – 2455190.0 (mean 39737.2898) |
| `reporting_round` | int64 | 0.0% | 1.0 – 50.0 (mean 20.1153) |
| `reference_period_start` | datetime64[ns] | 0.0% | |
| `reference_period_end` | datetime64[ns] | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `admin_level` | 0.0 | 2.0 | 1.9264 | 2.0 |
| `population` | 7.0 | 2455190.0 | 39737.2898 | 3429.0 |
| `reporting_round` | 1.0 | 50.0 | 20.1153 | 17.0 |

---

## 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`. 2 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 HDX Humanitarian API Data 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/hdx-hapi-nga) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_climate_nigeria,
title = {HDX HAPI Data for Nigeria},
author = {HDX Humanitarian API Data},
year = {2026},
url = {https://data.humdata.org/dataset/hdx-hapi-nga},
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.*