electricsheepafrica/africa-cash-activities-in-far-north-nigeria

---
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:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- cash-voucher-assistance-cva
- hxl
- who-is-doing-what-and-where-3w-4w-5w
- nga
pretty_name: "Cash activities in Far North - Nigeria"
dataset_info:
splits:
- name: train
num_examples: 1910
- name: test
num_examples: 477
---

# Cash activities in Far North - Nigeria

**Publisher:** OCHA Nigeria · **Source:** [HDX](https://data.humdata.org/dataset/cash-activities-in-far-north-nigeria) · **License:** `cc-by` · **Updated:** 2024-09-13

---

## Abstract

Cash activities in Far North - Nigeria

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `date` column(s). Geographic scope: **NGA**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 2,388 |
| **Columns** | 13 (2 numeric, 10 categorical, 1 datetime) |
| **Train split** | 1,910 rows |
| **Test split** | 477 rows |
| **Geographic scope** | NGA |
| **Publisher** | OCHA Nigeria |
| **HDX last updated** | 2024-09-13 |

---

## Variables

**Geographic** — `beneficiary_hh` (range 0.0–10543.0), `admin2name` (Jere, Maiduguri, Konduga), `admin2pcod` (NG008013, NG008021, NG008016), `admin1name` (Borno, Yobe, Adamawa), `admin1pcod` (NG008, NG036, NG002) and 2 others.

**Temporal** — `date`.

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-18).

**Other** — `project_sector` (Food Security , Shelter and NFI, Early Recovery & Livelihoods), `organisation` (World Food Programme, Action Against Hunger, Catholic Relief Services), `cash_disbursement_dollar` (range 0.0–600000.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-cash-activities-in-far-north-nigeria")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date` | datetime64[ns] | 0.0% | |
| `project_sector` | object | 0.0% | Food Security , Shelter and NFI, Early Recovery & Livelihoods |
| `organisation` | object | 0.0% | World Food Programme, Action Against Hunger, Catholic Relief Services |
| `beneficiary_hh` | float64 | 0.0% | 0.0 – 10543.0 (mean 516.4001) |
| `cash_disbursement_dollar` | float64 | 0.0% | 0.0 – 600000.0 (mean 35280.8747) |
| `admin2name` | object | 0.0% | Jere, Maiduguri, Konduga |
| `admin2pcod` | object | 0.0% | NG008013, NG008021, NG008016 |
| `admin1name` | object | 0.0% | Borno, Yobe, Adamawa |
| `admin1pcod` | object | 0.0% | NG008, NG036, NG002 |
| `admin0name` | object | 0.0% | Nigeria, #country |
| `admin0pcod` | object | 0.0% | NG, #country+code |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-18 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `beneficiary_hh` | 0.0 | 10543.0 | 516.4001 | 267.0 |
| `cash_disbursement_dollar` | 0.0 | 600000.0 | 35280.8747 | 17455.0 |

---

## 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`. 63 exact duplicate rows were removed. 3 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 OCHA Nigeria 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/cash-activities-in-far-north-nigeria) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_cash_activities_in_far_north_nigeria,
title = {Cash activities in Far North - Nigeria},
author = {OCHA Nigeria},
year = {2024},
url = {https://data.humdata.org/dataset/cash-activities-in-far-north-nigeria},
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.*