electricsheepafrica/africa-economic-indicators-niger

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- ner
pretty_name: "Niger - Economy and Growth"
dataset_info:
splits:
- name: train
num_examples: 9045
- name: test
num_examples: 2261
---

# Niger - Economy and Growth

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-niger) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-niger) on HDX.

Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **NER**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 11,307 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 9,045 rows |
| **Test split** | 2,261 rows |
| **Geographic scope** | NER |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Niger), `country_iso3` (NER), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -1408393715700.0–12051797049800.0).

**Identifier / Metadata** — `indicator_name` (Gross domestic savings (current LCU), Final consumption expenditure (current US$), Discrepancy in expenditure estimate of GDP (current LCU)), `indicator_code` (NY.GDS.TOTL.CN, NE.CON.TOTL.CD, NY.GDP.DISC.CN), `esa_source` (HDX), `esa_processed` (2026-04-27).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-economic-indicators-niger")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Niger |
| `country_iso3` | object | 0.0% | NER |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 1998.2665) |
| `indicator_name` | object | 0.0% | Gross domestic savings (current LCU), Final consumption expenditure (current US$), Discrepancy in expenditure estimate of GDP (current LCU) |
| `indicator_code` | object | 0.0% | NY.GDS.TOTL.CN, NE.CON.TOTL.CD, NY.GDP.DISC.CN |
| `value` | float64 | 0.0% | -1408393715700.0 – 12051797049800.0 (mean 268004176280.3645) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-27 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1998.2665 | 2000.0 |
| `value` | -1408393715700.0 | 12051797049800.0 | 268004176280.3645 | 3798692.0 |

---

## 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`. 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 World Bank Group 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/world-bank-economy-and-growth-indicators-for-niger) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_economic_indicators_niger,
title = {Niger - Economy and Growth},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-niger},
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.*