---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- gas
- imports
- uganda
pretty_name: "Gas Importation in Uganda | 2000 - 2019"
dataset_info:
  splits:
    - name: train
      num_examples: 20
    - name: test
      num_examples: 5
---

# Gas Importation in Uganda | 2000 - 2019

**Publisher:** Food and Agriculture Organization (FAO) · **Source:** [OpenAfrica](https://open.africa/dataset/gas-importation-in-uganda-2000-2019) · **License:** `cc-by` · **Updated:** 2021-04-26

---

## Abstract

The dataset shows gas importation values in Uganda from the year 2000 to 2019.

Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2021-04-26. Geographic scope: **Africa (multiple countries)**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 26 |
| **Columns** | 4 (2 numeric, 2 categorical, 0 datetime) |
| **Train split** | 20 rows |
| **Test split** | 5 rows |
| **Geographic scope** | Africa (multiple countries) |
| **Publisher** | Food and Agriculture Organization (FAO) |
| **OpenAfrica last updated** | 2021-04-26 |

---

## Variables

**Geographic** — `http_data_un_org_data_aspx_q_uganda_datamart_5bedata_5d_d_edata_f_cmid_3alp_3bcrid_3a800` (range 0.0–7000.0).

**Identifier / Metadata** — `source` (range 1996.0–2018.0), `esa_source` (HDX), `esa_processed` (2026-04-29).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `source` | float64 | 11.5% | 1996.0 – 2018.0 (mean 2007.0) |
| `http_data_un_org_data_aspx_q_uganda_datamart_5bedata_5d_d_edata_f_cmid_3alp_3bcrid_3a800` | float64 | 11.5% | 0.0 – 7000.0 (mean 3565.2174) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-29 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `source` | 1996.0 | 2018.0 | 2007.0 | 2007.0 |
| `http_data_un_org_data_aspx_q_uganda_datamart_5bedata_5d_d_edata_f_cmid_3alp_3bcrid_3a800` | 0.0 | 7000.0 | 3565.2174 | 4000.0 |

---

## Curation

Raw data was downloaded from OpenAfrica 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: `unnamed_0`. 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 Food and Agriculture Organization (FAO) 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://open.africa/dataset/gas-importation-in-uganda-2000-2019) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{openafrica_africa_ports_all,
  title     = {Gas Importation in Uganda | 2000 - 2019},
  author    = {Food and Agriculture Organization (FAO)},
  year      = {2021},
  url       = {https://open.africa/dataset/gas-importation-in-uganda-2000-2019},
  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.*