electricsheepafrica/africa-ports-angola

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- ports
- trade
- ago
pretty_name: "Angola: Daily Port Activity Data and Shipment Estimates"
dataset_info:
splits:
- name: train
num_examples: 10656
- name: test
num_examples: 2664
---

# Angola: Daily Port Activity Data and Shipment Estimates

**Publisher:** PortWatch · **Source:** [HDX](https://data.humdata.org/dataset/angola-daily-port-activity-data-and-shipment-estimates) · **License:** `hdx-other` · **Updated:** 2026-04-21

---

## Abstract

Daily count of port calls, estimates of incoming shipment volumes and outgoing shipment volumes (in metric tons) for ports in Angola.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-21. Geographic scope: **AGO**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 13,320 |
| **Columns** | 31 (24 numeric, 6 categorical, 0 datetime) |
| **Train split** | 10,656 rows |
| **Test split** | 2,664 rows |
| **Geographic scope** | AGO |
| **Publisher** | PortWatch |
| **HDX last updated** | 2026-04-21 |

---

## Variables

**Geographic** — `year` (range 2019.0–2026.0), `day` (range 1.0–31.0), `country` (Angola), `iso3` (AGO), `portcalls_dry_bulk` (range 0.0–4.0) and 8 others.

**Temporal** — `date`, `month` (range 1.0–12.0).

**Identifier / Metadata** — `portid` (port656, port665, port691), `portname` (Lobito, Luanda, Malongo), `esa_source` (HDX), `esa_processed` (2026-04-28).

**Other** — `portcalls_container` (range 0.0–5.0), `portcalls_general_cargo` (range 0.0–3.0), `portcalls_roro` (range 0.0–2.0), `portcalls_tanker` (range 0.0–6.0), `portcalls_cargo` (range 0.0–7.0) and 7 others.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date` | datetime64[ns, UTC] | 0.0% | |
| `year` | int64 | 0.0% | 2019.0 – 2026.0 (mean 2022.1607) |
| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.3536) |
| `day` | int64 | 0.0% | 1.0 – 31.0 (mean 15.6813) |
| `portid` | object | 0.0% | port656, port665, port691 |
| `portname` | object | 0.0% | Lobito, Luanda, Malongo |
| `country` | object | 0.0% | Angola |
| `iso3` | object | 0.0% | AGO |
| `portcalls_container` | int64 | 0.0% | 0.0 – 5.0 (mean 0.244) |
| `portcalls_dry_bulk` | int64 | 0.0% | 0.0 – 4.0 (mean 0.066) |
| `portcalls_general_cargo` | int64 | 0.0% | 0.0 – 3.0 (mean 0.1432) |
| `portcalls_roro` | int64 | 0.0% | 0.0 – 2.0 (mean 0.0202) |
| `portcalls_tanker` | int64 | 0.0% | 0.0 – 6.0 (mean 0.5488) |
| `portcalls_cargo` | int64 | 0.0% | 0.0 – 7.0 (mean 0.4733) |
| `portcalls` | int64 | 0.0% | 0.0 – 13.0 (mean 1.0221) |
| `import_container` | int64 | 0.0% | 0.0 – 53690.0 (mean 1422.2544) |
| `import_dry_bulk` | int64 | 0.0% | 0.0 – 102712.0 (mean 1102.7288) |
| `import_general_cargo` | int64 | 0.0% | 0.0 – 22535.0 (mean 186.3817) |
| `import_roro` | int64 | 0.0% | 0.0 – 7034.0 (mean 21.2547) |
| `import_tanker` | int64 | 0.0% | 0.0 – 191383.0 (mean 3206.3669) |
| `import_cargo` | int64 | 0.0% | 0.0 – 130740.0 (mean 2732.6471) |
| `import` | int64 | 0.0% | 0.0 – 200284.0 (mean 5939.0702) |
| `export_container` | int64 | 0.0% | 0.0 – 35283.0 (mean 178.7572) |
| `export_dry_bulk` | int64 | 0.0% | 0.0 – 60570.0 (mean 56.5802) |
| `export_general_cargo` | int64 | 0.0% | 0.0 – 16669.0 (mean 119.3551) |
| `export_roro` | int64 | 0.0% | |
| `export_tanker` | int64 | 0.0% | |
| `export_cargo` | int64 | 0.0% | |
| `export` | int64 | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-28 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2019.0 | 2026.0 | 2022.1607 | 2022.0 |
| `month` | 1.0 | 12.0 | 6.3536 | 6.0 |
| `day` | 1.0 | 31.0 | 15.6813 | 16.0 |
| `portcalls_container` | 0.0 | 5.0 | 0.244 | 0.0 |
| `portcalls_dry_bulk` | 0.0 | 4.0 | 0.066 | 0.0 |
| `portcalls_general_cargo` | 0.0 | 3.0 | 0.1432 | 0.0 |
| `portcalls_roro` | 0.0 | 2.0 | 0.0202 | 0.0 |
| `portcalls_tanker` | 0.0 | 6.0 | 0.5488 | 0.0 |
| `portcalls_cargo` | 0.0 | 7.0 | 0.4733 | 0.0 |
| `portcalls` | 0.0 | 13.0 | 1.0221 | 0.0 |
| `import_container` | 0.0 | 53690.0 | 1422.2544 | 0.0 |
| `import_dry_bulk` | 0.0 | 102712.0 | 1102.7288 | 0.0 |
| `import_general_cargo` | 0.0 | 22535.0 | 186.3817 | 0.0 |
| `import_roro` | 0.0 | 7034.0 | 21.2547 | 0.0 |
| `import_tanker` | 0.0 | 191383.0 | 3206.3669 | 0.0 |

---

## 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`. 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 PortWatch 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/angola-daily-port-activity-data-and-shipment-estimates) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ports_angola,
title = {Angola: Daily Port Activity Data and Shipment Estimates},
author = {PortWatch},
year = {2026},
url = {https://data.humdata.org/dataset/angola-daily-port-activity-data-and-shipment-estimates},
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.*