electricsheepafrica/africa-learning-levels-in-uganda-2014

---
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:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- uga
pretty_name: "Learning levels in Uganda (2014)"
dataset_info:
splits:
- name: train
num_examples: 16992
- name: test
num_examples: 4248
---

# Learning levels in Uganda (2014)

**Publisher:** Uwezo at Twaweza East Africa (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/learning-levels-in-uganda-2014) · **License:** `cc-by` · **Updated:** 2025-02-19

---

## Abstract

These datasets contains data relevant to learner achievement in Uganda. This ranges from school enrollment, socioeconomic indicators, school infrastructure among others.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-02-19. Geographic scope: **UGA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Water, sanitation and hygiene (wash) |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 21,241 |
| **Columns** | 108 (87 numeric, 21 categorical, 0 datetime) |
| **Train split** | 16,992 rows |
| **Test split** | 4,248 rows |
| **Geographic scope** | UGA |
| **Publisher** | Uwezo at Twaweza East Africa (inactive) |
| **HDX last updated** | 2025-02-19 |

---

## Variables

**Geographic** — `id_district` (range 102.0–419.0), `id_districtcodeold` (range 102.0–419.0), `id_districtname` (Kumi, Arua, Pallisa), `id_districtnameold` (KUMI, ARUA, PALLISA), `id_region` (range 1.0–4.0) and 13 others.

**Demographic** — `id_hh` (range 1001.0–879020.0), `id_village` (range 1.0–879.0), `hhno` (range 1.0–32.0), `answering_person` (HH head, Spouse, Other adult), `hh_gender` (Female, Male) and 12 others.

**Identifier / Metadata** — `id_database` (UG14), `water_source` (Borehole, Well, Pipe), `esa_source`, `esa_processed`.

**Other** — `house_wall` (Stone/Bricks, Mud, Iron sheet), `house_lighting` (Paraffin, Other, Electricity), `asset_toilet` (range 0.0–1.0), `eat_veg` (range 0.0–1.0), `eat_fruit` (range 0.0–1.0) and 64 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-learning-levels-in-uganda-2014")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id_database` | object | 0.0% | UG14 |
| `id_district` | int64 | 0.0% | 102.0 – 419.0 (mean 257.7983) |
| `id_districtcodeold` | int64 | 0.0% | 102.0 – 419.0 (mean 257.7983) |
| `id_districtname` | object | 0.0% | Kumi, Arua, Pallisa |
| `id_districtnameold` | object | 0.0% | KUMI, ARUA, PALLISA |
| `id_hh` | int64 | 0.0% | 1001.0 – 879020.0 (mean 415638.0648) |
| `id_region` | int64 | 0.0% | 1.0 – 4.0 (mean 2.4806) |
| `id_regionname` | object | 0.0% | Nothern, Eastern, Central |
| `id_village` | int64 | 0.0% | 1.0 – 879.0 (mean 415.6278) |
| `hhno` | int64 | 0.0% | 1.0 – 32.0 (mean 10.2224) |
| `answering_person` | object | 6.4% | HH head, Spouse, Other adult |
| `hh_gender` | object | 7.3% | Female, Male |
| `hh_edu_raw` | float64 | 6.2% | 1.0 – 5.0 (mean 2.1858) |
| `household_visited` | int64 | 0.0% | 0.0 – 1.0 (mean 0.069) |
| `home_language` | object | 1.0% | Local, English, Swahili |
| `hh_size` | int64 | 0.0% | 1.0 – 41.0 (mean 7.3661) |
| `hh_males` | float64 | 0.8% | 0.0 – 41.0 (mean 3.3867) |
| `hh_females` | float64 | 1.9% | 1.0 – 31.0 (mean 3.6975) |
| `house_wall` | object | 0.4% | Stone/Bricks, Mud, Iron sheet |
| `house_lighting` | object | 0.0% | Paraffin, Other, Electricity |
| `asset_toilet` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8488) |
| `mealsperday` | float64 | 1.6% | 1.0 – 3.0 (mean 2.2137) |
| `eat_veg` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8321) |
| `eat_fruit` | int64 | 0.0% | 0.0 – 1.0 (mean 0.5707) |
| `drink_milk` | float64 | 2.9% | 1.0 – 5.0 (mean 3.6008) |
| `asset_tv` | int64 | 0.0% | 0.0 – 1.0 (mean 0.1049) |
| `asset_radio` | int64 | 0.0% | 0.0 – 1.0 (mean 0.6247) |
| `asset_computer` | int64 | 0.0% | 0.0 – 1.0 (mean 0.0168) |
| `asset_phone` | int64 | 0.0% | 0.0 – 1.0 (mean 0.6202) |
| `asset_car` | int64 | 0.0% | |
| `asset_motorbike` | int64 | 0.0% | |
| `asset_bicycle` | int64 | 0.0% | |
| `asset_cattle` | int64 | 0.0% | |
| `asset_sheep_goat` | int64 | 0.0% | |
| `asset_donkey` | int64 | 0.0% | |
| `asset_camel` | int64 | 0.0% | |
| `asset_pig` | int64 | 0.0% | |
| `water_source` | object | 1.1% | Borehole, Well, Pipe |
| `treat_water` | float64 | 2.1% | |
| `h201_2` | int64 | 0.0% | |
| `h201_3` | int64 | 0.0% | |
| `h201_4` | int64 | 0.0% | |
| `h201_5` | int64 | 0.0% | |
| `h201_6` | int64 | 0.0% | |
| `h201_7` | int64 | 0.0% | |
| `h301` | float64 | 1.7% | |
| `h301other` | object | 26.9% | |
| `h302` | float64 | 2.0% | |
| `h302other` | object | 56.7% | |
| `h303` | float64 | 2.0% | |
| `h303other` | object | 56.7% | |
| `h304` | float64 | 3.8% | |
| `h305other` | float64 | 74.0% | |
| `h306_1` | int64 | 0.0% | |
| `h306_2` | int64 | 0.0% | |
| `h305_3` | int64 | 0.0% | |
| `h1600` | int64 | 0.0% | |
| `h1700` | int64 | 0.0% | |
| `childno` | int64 | 0.0% | |
| `age` | int64 | 0.0% | |
| `gender` | int64 | 0.0% | |
| `biological_parents` | float64 | 6.0% | |
| `h600` | float64 | 26.4% | |
| `disability` | float64 | 1.3% | |
| `mothers_toschool` | float64 | 9.0% | |
| `mothers_edu_raw` | float64 | 38.2% | |
| `preschool` | int64 | 0.0% | |
| `grade` | float64 | 16.6% | |
| `schooltype` | object | 14.7% | |
| `tuition` | int64 | 0.0% | |
| `schoolmatch` | int64 | 0.0% | |
| `completed` | int64 | 0.0% | |
| `completed_second` | int64 | 0.0% | |
| `neverenrolled` | int64 | 0.0% | |
| `dropout` | int64 | 0.0% | |
| `attendyesterday` | float64 | 14.9% | |
| `mathteachyesterday` | float64 | 26.8% | |
| `testsample` | float64 | 11.8% | |
| `english` | float64 | 4.2% | |
| `english1` | float64 | 73.1% | |
| `english2` | float64 | 73.8% | |
| `locallanguage` | float64 | 78.0% | |
| `math` | object | 5.0% | |
| `bonus1` | float64 | 6.8% | |
| `bonus2` | float64 | 7.4% | |
| `h1500right` | float64 | 4.3% | |
| `h1501left` | float64 | 4.4% | |
| `sub_location` | object | 0.0% | |
| `asset_elec` | int64 | 0.0% | |
| `asset_water` | int64 | 0.0% | |
| `mothers_edu` | object | 35.3% | |
| `enr_ans` | int64 | 0.0% | |
| `hh_edu` | object | 27.4% | |
| `hh_children` | int64 | 0.0% | |
| `weight` | float64 | 0.0% | |
| `region_code` | int64 | 0.0% | |
| `district_nohhlds` | int64 | 0.0% | |
| `nohhldsinea` | int64 | 0.0% | |
| `noeasindistrict` | int64 | 0.0% | |
| `nodistricts` | int64 | 0.0% | |
| `english_imputed` | int64 | 0.0% | |
| `english1_imputed` | int64 | 0.0% | |
| `english2_imputed` | int64 | 0.0% | |
| `math_imputed` | object | 0.0% | |
| `bonus1_imputed` | int64 | 0.0% | |
| `bonus2_imputed` | int64 | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id_district` | 102.0 | 419.0 | 257.7983 | 303.0 |
| `id_districtcodeold` | 102.0 | 419.0 | 257.7983 | 303.0 |
| `id_hh` | 1001.0 | 879020.0 | 415638.0648 | 400020.0 |
| `id_region` | 1.0 | 4.0 | 2.4806 | 3.0 |
| `id_village` | 1.0 | 879.0 | 415.6278 | 400.0 |
| `hhno` | 1.0 | 32.0 | 10.2224 | 10.0 |
| `hh_edu_raw` | 1.0 | 5.0 | 2.1858 | 1.0 |
| `household_visited` | 0.0 | 1.0 | 0.069 | 0.0 |
| `hh_size` | 1.0 | 41.0 | 7.3661 | 7.0 |
| `hh_males` | 0.0 | 41.0 | 3.3867 | 3.0 |
| `hh_females` | 1.0 | 31.0 | 3.6975 | 3.0 |
| `asset_toilet` | 0.0 | 1.0 | 0.8488 | 1.0 |
| `mealsperday` | 1.0 | 3.0 | 2.2137 | 2.0 |
| `eat_veg` | 0.0 | 1.0 | 0.8321 | 1.0 |
| `eat_fruit` | 0.0 | 1.0 | 0.5707 | 1.0 |

---

## 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`. 7 column(s) with >80% missing values were removed: `h201_1`, `h201_8other`, `h305`, `dropout_year`, `dropout_class`, `locallanguage1`.... 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 Uwezo at Twaweza East Africa (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.
- The following columns have >20% missing values and should be treated with caution in modelling: `h301other`, `h302other`, `h303other`, `h305other`, `h600`, `mothers_edu_raw`, `mathteachyesterday`, `english1`....
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/learning-levels-in-uganda-2014) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_learning_levels_in_uganda_2014,
title = {Learning levels in Uganda (2014)},
author = {Uwezo at Twaweza East Africa (inactive)},
year = {2025},
url = {https://data.humdata.org/dataset/learning-levels-in-uganda-2014},
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.*