electricsheepafrica/africa-uganda-languages

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- languages
- uga
pretty_name: "Uganda: Languages"
dataset_info:
splits:
- name: train
num_examples: 20
- name: test
num_examples: 5
---

# Uganda: Languages

**Publisher:** CLEAR Global (previously Translators without Borders) · **Source:** [HDX](https://data.humdata.org/dataset/uganda-languages) · **License:** `cc-by-sa` · **Updated:** 2026-04-06

---

## Abstract

Data on languages spoken in Uganda, showing the main language spoken in the household by proportion of the population. Data is drawn from AfroBarometer. For more resources on the languages of Uganda and language use in humanitarian contexts please visit: https://clearglobal.org/language-maps-and-data/

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the `datetime_published`, `date_creation` column(s). Geographic scope: **UGA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 25 |
| **Columns** | 16 (4 numeric, 10 categorical, 2 datetime) |
| **Train split** | 20 rows |
| **Test split** | 5 rows |
| **Geographic scope** | UGA |
| **Publisher** | CLEAR Global (previously Translators without Borders) |
| **HDX last updated** | 2026-04-06 |

---

## Variables

**Geographic** — `location_code` (UGA), `location_name` (Uganda), `location_level` (range 0.0–0.0), `reliability_score` (range 0.6965–0.6965), `representivity_rating` (moderate).

**Temporal** — `datetime_published`, `date_creation`.

**Demographic** — `language_code` (saam1283, stan1293, acol1236), `language_name` (Saamia, English, Acoli), `language_rank` (range 1.0–25.0).

**Outcome / Measurement** — `proportion_value` (range 0.0005–0.2516).

**Identifier / Metadata** — `dataset_name` (Uganda Round 9 data (2022)), `source` (AfroBarometer), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `url` (https://www.afrobarometer.org/wp-content/uploads/2024/03/UGA_R9.data_.final_.wtd_release.19Jun22_Updated.7Nov23.sav).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-languages")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `location_code` | object | 0.0% | UGA |
| `location_name` | object | 0.0% | Uganda |
| `location_level` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `language_code` | object | 0.0% | saam1283, stan1293, acol1236 |
| `language_name` | object | 0.0% | Saamia, English, Acoli |
| `language_rank` | int64 | 0.0% | 1.0 – 25.0 (mean 13.0) |
| `proportion_value` | float64 | 0.0% | 0.0005 – 0.2516 (mean 0.04) |
| `reliability_score` | float64 | 0.0% | 0.6965 – 0.6965 (mean 0.6965) |
| `dataset_name` | object | 0.0% | Uganda Round 9 data (2022) |
| `url` | object | 0.0% | https://www.afrobarometer.org/wp-content/uploads/2024/03/UGA_R9.data_.final_.wtd_release.19Jun22_Updated.7Nov23.sav |
| `source` | object | 0.0% | AfroBarometer |
| `datetime_published` | datetime64[ns] | 0.0% | |
| `date_creation` | datetime64[ns] | 0.0% | |
| `representivity_rating` | object | 0.0% | moderate |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `location_level` | 0.0 | 0.0 | 0.0 | 0.0 |
| `language_rank` | 1.0 | 25.0 | 13.0 | 13.0 |
| `proportion_value` | 0.0005 | 0.2516 | 0.04 | 0.0234 |
| `reliability_score` | 0.6965 | 0.6965 | 0.6965 | 0.6965 |

---

## 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`. 2 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 CLEAR Global (previously Translators without Borders) 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/uganda-languages) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_uganda_languages,
title = {Uganda: Languages},
author = {CLEAR Global (previously Translators without Borders)},
year = {2026},
url = {https://data.humdata.org/dataset/uganda-languages},
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.*