electricsheepafrica/africa-elections-kenya

---
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-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- elctions-2017
- elections
- iebc
- kenya
- register
- voter
- gender
pretty_name: "IEBC Voter Register 2017 - Kenya"
dataset_info:
splits:
- name: train
num_examples: 32707
- name: test
num_examples: 8176
---

# IEBC Voter Register 2017 - Kenya

**Publisher:** Code for Africa · **Source:** [OpenAfrica](https://open.africa/dataset/iebc-voter-register-2017) · **License:** `cc-by` · **Updated:** 2023-11-30

---

## Abstract

Statistics on voter registration in 2017 published by the IEBC.

Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2023-11-30. Geographic scope: **GENDER, KENYA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 40,884 |
| **Columns** | 14 (7 numeric, 7 categorical, 0 datetime) |
| **Train split** | 32,707 rows |
| **Test split** | 8,176 rows |
| **Geographic scope** | GENDER, KENYA |
| **Publisher** | Code for Africa |
| **OpenAfrica last updated** | 2023-11-30 |

---

## Variables

**Geographic** — `county_code` (range 1.0–49.0), `county_name` (NAIROBI CITY, KIAMBU, NAKURU), `constituency_code` (range 1.0–292.0), `constituency_name` (NAIVASHA, STAREHE, RUIRU).

**Identifier / Metadata** — `caw_code` (range 1.0–5004.0), `caw_name` (TOWNSHIP, BIASHARA, PRISONS), `registration_centre_code` (range 1.0–226.0), `registration_centre_name` (MAKUTANO PRIMARY SCHOOL , MILIMANI PRIMARY SCHOOL , BAHATI PRIMARY SCHOOL ), `polling_station_code` (range 1001000100101.0–49292145111801.0) and 3 others.

**Other** — `voters_per_registration_centre` (range 1.0–19611423.0), `voters_per_polling_station` (range 1.0–19611423.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `county_code` | float64 | 0.0% | 1.0 – 49.0 (mean 27.1634) |
| `county_name` | object | 0.0% | NAIROBI CITY, KIAMBU, NAKURU |
| `constituency_code` | float64 | 0.0% | 1.0 – 292.0 (mean 149.291) |
| `constituency_name` | object | 0.0% | NAIVASHA, STAREHE, RUIRU |
| `caw_code` | float64 | 0.0% | 1.0 – 5004.0 (mean 745.3125) |
| `caw_name` | object | 0.0% | TOWNSHIP, BIASHARA, PRISONS |
| `registration_centre_code` | float64 | 0.0% | 1.0 – 226.0 (mean 45.0475) |
| `registration_centre_name` | object | 39.6% | MAKUTANO PRIMARY SCHOOL , MILIMANI PRIMARY SCHOOL , BAHATI PRIMARY SCHOOL |
| `voters_per_registration_centre` | float64 | 39.6% | 1.0 – 19611423.0 (mean 1587.7766) |
| `polling_station_code` | float64 | 0.0% | 1001000100101.0 – 49292145111801.0 (mean 27312734125312.684) |
| `polling_station_name` | object | 0.0% | UMOJA 1 PRIMARY SCHOOL, ST MONICA NUR.SCH, MULOLONGO PRIMARY SCHOOL |
| `voters_per_polling_station` | int64 | 0.0% | 1.0 – 19611423.0 (mean 959.3691) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-27 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `county_code` | 1.0 | 49.0 | 27.1634 | 28.0 |
| `constituency_code` | 1.0 | 292.0 | 149.291 | 148.0 |
| `caw_code` | 1.0 | 5004.0 | 745.3125 | 735.0 |
| `registration_centre_code` | 1.0 | 226.0 | 45.0475 | 37.0 |
| `voters_per_registration_centre` | 1.0 | 19611423.0 | 1587.7766 | 521.0 |
| `polling_station_code` | 1001000100101.0 | 49292145111801.0 | 27312734125312.684 | 28148073501801.0 |
| `voters_per_polling_station` | 1.0 | 19611423.0 | 959.3691 | 502.0 |

---

## Curation

Raw data was downloaded from OpenAfrica 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 Code for Africa and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `registration_centre_name`, `voters_per_registration_centre`.
- This dataset spans 2 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://open.africa/dataset/iebc-voter-register-2017) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{openafrica_africa_elections_kenya,
title = {IEBC Voter Register 2017 - Kenya},
author = {Code for Africa},
year = {2023},
url = {https://open.africa/dataset/iebc-voter-register-2017},
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.*