Datasets:
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
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
- gender-and-age-disaggregated-data-gadd
- ken
pretty_name: Kenya - Enrolment in Secondary Schools by Level and Sex, 2009 – 2013
dataset_info:
splits:
- name: train
num_examples: 8
- name: test
num_examples: 2
Kenya - Enrolment in Secondary Schools by Level and Sex, 2009 – 2013
Publisher: Kenya National Bureau of Statistics (inactive) · Source: HDX · License: other-pd-nr · Updated: 2025-07-22
Abstract
The Dataset shows the Enrolment in Secondary Schools by Level and Sex for the period of 2009 – 2013 as reported by the Kenya National Bureau of statistics in the KNBS Economic Survey 2014
Each row in this dataset represents time-series observations. Data was last updated on HDX on 2025-07-22. Geographic scope: KEN.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Education |
| Unit of observation | Time-series observations |
| Rows (total) | 10 |
| Columns | 8 (5 numeric, 3 categorical, 0 datetime) |
| Train split | 8 rows |
| Test split | 2 rows |
| Geographic scope | KEN |
| Publisher | Kenya National Bureau of Statistics (inactive) |
| HDX last updated | 2025-07-22 |
Variables
Geographic — year (range 2009.0–2013.0).
Demographic — gender (Boys, Girls).
Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-07).
Other — form_1 (range 212467.0–327775.0), form_2 (range 175098.0–288238.0), form_3 (range 142579.0–267221.0), form_4 (range 141999.0–244463.0).
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-enrolment-in-secondary-schools-by-level-and-sex-2009-2013")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
year |
int64 | 0.0% | 2009.0 – 2013.0 (mean 2011.0) |
gender |
object | 0.0% | Boys, Girls |
form_1 |
int64 | 0.0% | 212467.0 – 327775.0 (mean 261551.1) |
form_2 |
int64 | 0.0% | 175098.0 – 288238.0 (mean 233702.3) |
form_3 |
int64 | 0.0% | 142579.0 – 267221.0 (mean 207803.1) |
form_4 |
int64 | 0.0% | 141999.0 – 244463.0 (mean 188225.8) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-07 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
year |
2009.0 | 2013.0 | 2011.0 | 2011.0 |
form_1 |
212467.0 | 327775.0 | 261551.1 | 258140.0 |
form_2 |
175098.0 | 288238.0 | 233702.3 | 235944.0 |
form_3 |
142579.0 | 267221.0 | 207803.1 | 217532.0 |
form_4 |
141999.0 | 244463.0 | 188225.8 | 185481.0 |
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. 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 for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_africa_enrolment_in_secondary_schools_by_level_and_sex_2009_2013,
title = {Kenya - Enrolment in Secondary Schools by Level and Sex, 2009 – 2013},
author = {Kenya National Bureau of Statistics (inactive)},
year = {2025},
url = {https://data.humdata.org/dataset/enrolment-in-secondary-schools-by-level-and-sex-2009-2013},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.