electricsheepafrica/africa-kenya-distribution-of-population-aged-above-3-years-by-highest-level-of-education-reached

---
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:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- census
- education
- ken
pretty_name: "Kenya: Distribution of Population aged above 3 Years by highest Level of Education Reached"
dataset_info:
splits:
- name: train
num_examples: 932
- name: test
num_examples: 233
---

# Kenya: Distribution of Population aged above 3 Years by highest Level of Education Reached

**Publisher:** Kenya National Bureau of Statistics (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/kenya-distribution-of-population-aged-above-3-years-by-highest-level-of-education-reached) · **License:** `cc-by` · **Updated:** 2025-02-06

---

## Abstract

Distribution of Population age 3 Years and Above by highest Level of Education Reached, Area of Residence and Sex per Sub-County

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-02-06. Geographic scope: **KEN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Geolocated point observations |
| **Rows (total)** | 1,165 |
| **Columns** | 13 (10 numeric, 3 categorical, 0 datetime) |
| **Train split** | 932 rows |
| **Test split** | 233 rows |
| **Geographic scope** | KEN |
| **Publisher** | Kenya National Bureau of Statistics (inactive) |
| **HDX last updated** | 2025-02-06 |

---

## Variables

**Geographic** — `distribution_of_population_age_3_years_and_above_by_highest_level_of_education_reached_area_of_residence_sex_county_and_sub_county` (Male, Female, MT. KENYA FOREST).

**Identifier / Metadata** — `unnamed_1` (range 3.0–36212477.0), `unnamed_2` (range 1.0–3616843.0), `unnamed_3` (range 1.0–18341098.0), `unnamed_4` (range 2.0–9787320.0), `unnamed_5` (range 1.0–2755273.0) and 7 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-kenya-distribution-of-population-aged-above-3-years-by-highest-level-of-education-reached")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `distribution_of_population_age_3_years_and_above_by_highest_level_of_education_reached_area_of_residence_sex_county_and_sub_county` | object | 0.0% | Male, Female, MT. KENYA FOREST |
| `unnamed_1` | float64 | 0.1% | 3.0 – 36212477.0 (mean 215188.7405) |
| `unnamed_2` | float64 | 1.3% | 1.0 – 3616843.0 (mean 21830.1278) |
| `unnamed_3` | float64 | 0.3% | 1.0 – 18341098.0 (mean 109726.4875) |
| `unnamed_4` | float64 | 0.1% | 2.0 – 9787320.0 (mean 57970.6572) |
| `unnamed_5` | float64 | 0.3% | 1.0 – 2755273.0 (mean 16264.8262) |
| `unnamed_6` | float64 | 1.0% | 1.0 – 1511943.0 (mean 8865.9748) |
| `unnamed_7` | float64 | 2.9% | 1.0 – 37546.0 (mean 231.1477) |
| `unnamed_8` | float64 | 16.7% | 1.0 – 51996.0 (mean 372.6763) |
| `unnamed_9` | float64 | 2.8% | 1.0 – 101326.0 (mean 615.8578) |
| `unnamed_10` | float64 | 11.9% | 1.0 – 9132.0 (mean 60.8947) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-19 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `unnamed_1` | 3.0 | 36212477.0 | 215188.7405 | 64487.0 |
| `unnamed_2` | 1.0 | 3616843.0 | 21830.1278 | 6594.5 |
| `unnamed_3` | 1.0 | 18341098.0 | 109726.4875 | 33974.0 |
| `unnamed_4` | 2.0 | 9787320.0 | 57970.6572 | 16018.5 |
| `unnamed_5` | 1.0 | 2755273.0 | 16264.8262 | 3567.5 |
| `unnamed_6` | 1.0 | 1511943.0 | 8865.9748 | 1658.0 |
| `unnamed_7` | 1.0 | 37546.0 | 231.1477 | 71.0 |
| `unnamed_8` | 1.0 | 51996.0 | 372.6763 | 6.0 |
| `unnamed_9` | 1.0 | 101326.0 | 615.8578 | 163.0 |
| `unnamed_10` | 1.0 | 9132.0 | 60.8947 | 13.0 |

---

## 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`. 10 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 National Bureau of Statistics (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-distribution-of-population-aged-above-3-years-by-highest-level-of-education-reached) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_kenya_distribution_of_population_aged_above_3_years_by_highest_level_of_education_reached,
title = {Kenya: Distribution of Population aged above 3 Years by highest Level of Education Reached},
author = {Kenya National Bureau of Statistics (inactive)},
year = {2025},
url = {https://data.humdata.org/dataset/kenya-distribution-of-population-aged-above-3-years-by-highest-level-of-education-reached},
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.*