---
license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: monolingual
size_categories:
- n<1K
tags:
- "africa"
- "electric-sheep-africa"
- "open-data"
- "metadata-backed"
- "economics-finance"
- "csv"
- "tabular"
- "text"
- "finance"
- "banking"
pretty_name: "Africa Automated Teller Machines ATMs per 100000 adults | Africa (World Bank)"
---

# Africa Automated Teller Machines ATMs per 100000 adults | Africa (World Bank)

**Size category:** `n<1K` - **Formats:** `csv` - **Sector:** economics_finance - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![size](https://img.shields.io/badge/size-n%3C1K-blue)
![sector](https://img.shields.io/badge/sector-economics_finance-green)
![downloads](https://img.shields.io/badge/HF_downloads-13-orange)
![license](https://img.shields.io/badge/license-other-lightgrey)

## TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

## What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Africa Automated Teller Machines (ATMs) (per 100,000 adults) Dataset Overview This dataset contains automated teller machines (atms) (per 100,000 adults) data for African countries from the World Bank. Data Details Indicator Code: FB.ATM.TOTL.P5 Description: Automated Teller Machines (ATMs) (per 100,000 adults) Geographic Coverage: 52 African countries Time Period: 2004-2023 Data Points: 858 observations Coverage: 24.44% of possible country-year combinations… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults.

## Dataset Profile

| Field | Value |
|---|---|
| Hugging Face repo | [`electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults`](https://huggingface.co/datasets/electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults) |
| Sector | economics_finance |
| Topic tags | finance, banking |
| Modalities | `tabular`, `text` |
| Formats | `csv` |
| Size category | `n<1K` |
| Countries | Africa-wide or source-defined African coverage |
| ISO3 coverage | `not declared` |
| Last modified on HF | `2025-08-19 15:00:27+00:00` |
| Inventory snapshot | `2026-07-16T16:00:34Z` |

## How To Read This Dataset

- Start from the repository files and the dataset viewer when available.
- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
- Preserve missing values until you have a defensible imputation rule.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])
```

### Convert To Pandas When Tabular

```python
from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())
```

## Data Quality Notes

- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
- Exact schema, row counts, and source files should be inspected in the repository data files.
- Metadata gaps from the inventory: country, upstream_publisher.
- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

## Source And Provenance

- **Source context:** World Bank
- **Publisher/source attribution:** World Bank open data
- **License:** gpl
- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults](https://huggingface.co/datasets/electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults)
- **Inventory retrieved at:** `2026-07-16T16:00:34Z`

## Suggested Analyses

- Inspect schema and missingness before modeling.
- Profile variables by geography, time, and subgroup columns where present.
- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

## Citation

```bibtex
@misc{electric_sheep_africa_africa_automated_teller_machines_atms_per_100000_adults_2026,
  title        = {Africa Automated Teller Machines ATMs per 100000 adults | Africa (World Bank)},
  author       = {World Bank open data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/Africa-Automated-Teller-Machines-ATMs-per-100000-adults}}
}
```

## License

Released under gpl.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

## About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

---

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.