electricsheepafrica/africa-ports-gabon

Gabon: Daily Port Activity Data and Shipment Estimates

Publisher: PortWatch · Source: HDX · License: hdx-other · Updated: 2026-04-21

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Abstract

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

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

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	15,984
Columns	31 (24 numeric, 6 categorical, 0 datetime)
Train split	12,787 rows
Test split	3,196 rows
Geographic scope	GAB
Publisher	PortWatch
HDX last updated	2026-04-21

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Variables

Geographic — year (range 2019.0–2026.0), day (range 1.0–31.0), country (Gabon), iso3 (GAB), portcalls_dry_bulk (range 0.0–3.0) and 8 others.

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

Identifier / Metadata — portid (port952, port973, port2138), portname (Port Gentil, Port Owendo, Gamba), esa_source (HDX), esa_processed (2026-04-24).

Other — portcalls_container (range 0.0–3.0), portcalls_general_cargo (range 0.0–3.0), portcalls_roro (range 0.0–2.0), portcalls_tanker (range 0.0–3.0), portcalls_cargo (range 0.0–4.0) and 7 others.

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Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

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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%	port952, port973, port2138
portname	object	0.0%	Port Gentil, Port Owendo, Gamba
country	object	0.0%	Gabon
iso3	object	0.0%	GAB
portcalls_container	int64	0.0%	0.0 – 3.0 (mean 0.0664)
portcalls_dry_bulk	int64	0.0%	0.0 – 3.0 (mean 0.0547)
portcalls_general_cargo	int64	0.0%	0.0 – 3.0 (mean 0.0726)
portcalls_roro	int64	0.0%	0.0 – 2.0 (mean 0.0091)
portcalls_tanker	int64	0.0%	0.0 – 3.0 (mean 0.1639)
portcalls_cargo	int64	0.0%	0.0 – 4.0 (mean 0.2029)
portcalls	int64	0.0%	0.0 – 5.0 (mean 0.3668)
import_container	int64	0.0%	0.0 – 20068.0 (mean 112.8137)
import_dry_bulk	int64	0.0%	0.0 – 50160.0 (mean 184.7677)
import_general_cargo	int64	0.0%	0.0 – 15728.0 (mean 78.4627)
import_roro	int64	0.0%	0.0 – 2135.0 (mean 0.7958)
import_tanker	int64	0.0%	0.0 – 39072.0 (mean 208.8369)
import_cargo	int64	0.0%	0.0 – 50218.0 (mean 376.844)
import	int64	0.0%	0.0 – 57536.0 (mean 585.689)
export_container	int64	0.0%	0.0 – 17944.0 (mean 88.3282)
export_dry_bulk	int64	0.0%	0.0 – 92036.0 (mean 1338.5518)
export_general_cargo	int64	0.0%	0.0 – 24035.0 (mean 57.4808)
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%	2026-04-24

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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	3.0	0.0664	0.0
portcalls_dry_bulk	0.0	3.0	0.0547	0.0
portcalls_general_cargo	0.0	3.0	0.0726	0.0
portcalls_roro	0.0	2.0	0.0091	0.0
portcalls_tanker	0.0	3.0	0.1639	0.0
portcalls_cargo	0.0	4.0	0.2029	0.0
portcalls	0.0	5.0	0.3668	0.0
import_container	0.0	20068.0	112.8137	0.0
import_dry_bulk	0.0	50160.0	184.7677	0.0
import_general_cargo	0.0	15728.0	78.4627	0.0
import_roro	0.0	2135.0	0.7958	0.0
import_tanker	0.0	39072.0	208.8369	0.0

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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.

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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 for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_ports_gabon,
  title     = {Gabon: Daily Port Activity Data and Shipment Estimates},
  author    = {PortWatch},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/gabon-daily-port-activity-data-and-shipment-estimates},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.