electricsheepafrica/africa-kenya-boy-child-vs-girls-child-enrollment-comparison-at-primary-school-level-by-county

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- ken
pretty_name: "Kenya - Boy Child VS Girls Child Enrollment comparison at Primary school level by County"
dataset_info:
splits:
- name: train
num_examples: 37
- name: test
num_examples: 9
---

# Kenya - Boy Child VS Girls Child Enrollment comparison at Primary school level by County

**Publisher:** Kenya Open Data Initiative (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/kenya-boy-child-vs-girls-child-enrollment-comparison-at-primary-school-level-by-county) · **License:** `cc-by` · **Updated:** 2023-03-03

---

## Abstract

Boy Child VS Girls Child Enrollment comparison at Primary school level by County

Each row in this dataset represents time-series observations. Data was last updated on HDX on 2023-03-03. Geographic scope: **KEN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 47 |
| **Columns** | 11 (7 numeric, 3 categorical, 0 datetime) |
| **Train split** | 37 rows |
| **Test split** | 9 rows |
| **Geographic scope** | KEN |
| **Publisher** | Kenya Open Data Initiative (inactive) |
| **HDX last updated** | 2023-03-03 |

---

## Variables

**Geographic** — `county` (BARINGO, NYERI, MIGORI), `urban_semiurban_boys_number` (range 267.0–193562.0), `rural_boys_number` (range 1261.0–250541.0), `year`.

**Outcome / Measurement** — `urban_semiurban_girls_number` (range 301.0–201169.0), `urban_semiurban_total_number` (range 568.0–394731.0), `rural_girls_number` (range 1322.0–257147.0), `rural_total_number` (range 2583.0–507688.0).

**Identifier / Metadata** — `objectid` (range 1.0–47.0), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-kenya-boy-child-vs-girls-child-enrollment-comparison-at-primary-school-level-by-county")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `objectid` | int64 | 0.0% | 1.0 – 47.0 (mean 24.0) |
| `county` | object | 0.0% | BARINGO, NYERI, MIGORI |
| `urban_semiurban_boys_number` | int64 | 0.0% | 267.0 – 193562.0 (mean 16082.3404) |
| `urban_semiurban_girls_number` | int64 | 0.0% | 301.0 – 201169.0 (mean 15919.5106) |
| `urban_semiurban_total_number` | int64 | 0.0% | 568.0 – 394731.0 (mean 32001.8511) |
| `rural_boys_number` | int64 | 0.0% | 1261.0 – 250541.0 (mean 87130.3191) |
| `rural_girls_number` | int64 | 0.0% | 1322.0 – 257147.0 (mean 84141.7021) |
| `rural_total_number` | int64 | 0.0% | 2583.0 – 507688.0 (mean 171272.0213) |
| `year` | datetime64[ns, UTC] | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `objectid` | 1.0 | 47.0 | 24.0 | 24.0 |
| `urban_semiurban_boys_number` | 267.0 | 193562.0 | 16082.3404 | 7341.0 |
| `urban_semiurban_girls_number` | 301.0 | 201169.0 | 15919.5106 | 7801.0 |
| `urban_semiurban_total_number` | 568.0 | 394731.0 | 32001.8511 | 14644.0 |
| `rural_boys_number` | 1261.0 | 250541.0 | 87130.3191 | 84323.0 |
| `rural_girls_number` | 1322.0 | 257147.0 | 84141.7021 | 80773.0 |
| `rural_total_number` | 2583.0 | 507688.0 | 171272.0213 | 165696.0 |

---

## 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`. 1 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 Kenya Open Data Initiative (inactive) 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/kenya-boy-child-vs-girls-child-enrollment-comparison-at-primary-school-level-by-county) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_kenya_boy_child_vs_girls_child_enrollment_comparison_at_primary_school_level_by_county,
title = {Kenya - Boy Child VS Girls Child Enrollment comparison at Primary school level by County},
author = {Kenya Open Data Initiative (inactive)},
year = {2023},
url = {https://data.humdata.org/dataset/kenya-boy-child-vs-girls-child-enrollment-comparison-at-primary-school-level-by-county},
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.*