electricsheepafrica/africa-population-3-years-and-above-by-sex-and-highest-level-of-education-reached-up-to-district

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- baseline-population
- education
- indicators
- ken
pretty_name: "Kenya - Population (3 years and above) by Sex and Highest Level of Education Reached"
dataset_info:
splits:
- name: train
num_examples: 505
- name: test
num_examples: 126
---

# Kenya - Population (3 years and above) by Sex and Highest Level of Education Reached

**Publisher:** Kenya Open Data Initiative (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/population-3-years-and-above-by-sex-and-highest-level-of-education-reached-up-to-district-level) · **License:** `other-pd-nr` · **Updated:** 2023-03-02

---

## Abstract

Population, 3 years and above by Sex and Highest Level of Education Reached up to District Level - 2009. This dataset is based on 2009 Census Volume II Table 2: Population, 3 years and above by Sex and Highest Level of Education Reached up to District Level

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-03-02. Geographic scope: **KEN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 632 |
| **Columns** | 19 (2 numeric, 17 categorical, 0 datetime) |
| **Train split** | 505 rows |
| **Test split** | 126 rows |
| **Geographic scope** | KEN |
| **Publisher** | Kenya Open Data Initiative (inactive) |
| **HDX last updated** | 2023-03-02 |

---

## Variables

**Geographic** — `district` (NYANDARUA NORTH, ELDORET EAST, SAMBURU EAST), `pre_primary` (7%, 6%, 8%), `primary` (61%, 62%, 58%), `secondary` (18%, 17%, 16%), `tertiary` (2%, 1%, 3%) and 6 others.

**Demographic** — `male_female` (Male, Female).

**Identifier / Metadata** — `objectid` (range 0.0–631.0), `esa_source`, `esa_processed`.

**Other** — `rural_urban` (Urban, Rural), `never_attended` (7%, 9%, 8%), `madrassa`, `mtef_sector`.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-population-3-years-and-above-by-sex-and-highest-level-of-education-reached-up-to-district")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `district` | object | 0.0% | NYANDARUA NORTH, ELDORET EAST, SAMBURU EAST |
| `rural_urban` | object | 0.0% | Urban, Rural |
| `male_female` | object | 0.0% | Male, Female |
| `never_attended` | object | 8.2% | 7%, 9%, 8% |
| `pre_primary` | object | 8.2% | 7%, 6%, 8% |
| `primary` | object | 8.2% | 61%, 62%, 58% |
| `secondary` | object | 8.2% | 18%, 17%, 16% |
| `tertiary` | object | 8.2% | 2%, 1%, 3% |
| `university` | object | 8.2% | 1%, 0%, 2% |
| `youth_polytechnic` | object | 8.2% | 0%, 1%, 2% |
| `basic_literacy` | object | 8.2% | |
| `madrassa` | object | 8.2% | |
| `population` | int64 | 0.0% | 0.0 – 533776.0 (mean 55212.7278) |
| `province` | object | 0.0% | |
| `county` | object | 0.0% | |
| `mtef_sector` | object | 0.0% | |
| `objectid` | int64 | 0.0% | 0.0 – 631.0 (mean 315.5) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `population` | 0.0 | 533776.0 | 55212.7278 | 41032.5 |
| `objectid` | 0.0 | 631.0 | 315.5 | 315.5 |

---

## 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) with >80% missing values were removed: `location_1`. 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/population-3-years-and-above-by-sex-and-highest-level-of-education-reached-up-to-district-level) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_population_3_years_and_above_by_sex_and_highest_level_of_education_reached_up_to_district,
title = {Kenya - Population (3 years and above) by Sex and Highest Level of Education Reached},
author = {Kenya Open Data Initiative (inactive)},
year = {2023},
url = {https://data.humdata.org/dataset/population-3-years-and-above-by-sex-and-highest-level-of-education-reached-up-to-district-level},
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.*