electricsheepafrica/africa-ports-comoros

Comoros: 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 Comoros.

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

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)	5,328
Columns	31 (24 numeric, 6 categorical, 0 datetime)
Train split	4,262 rows
Test split	1,065 rows
Geographic scope	COM
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 (Comoros), iso3 (COM), portcalls_dry_bulk (range 0.0–2.0) and 8 others.

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

Identifier / Metadata — portid (port769, port773), portname (Moroni, Mutsamudu), esa_source (HDX), esa_processed (2026-04-23).

Other — portcalls_container (range 0.0–2.0), portcalls_general_cargo (range 0.0–3.0), portcalls_roro (range 0.0–1.0), portcalls_tanker (range 0.0–2.0), portcalls_cargo (range 0.0–3.0) and 7 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ports-comoros")
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%	port769, port773
portname	object	0.0%	Moroni, Mutsamudu
country	object	0.0%	Comoros
iso3	object	0.0%	COM
portcalls_container	int64	0.0%	0.0 – 2.0 (mean 0.0845)
portcalls_dry_bulk	int64	0.0%	0.0 – 2.0 (mean 0.0312)
portcalls_general_cargo	int64	0.0%	0.0 – 3.0 (mean 0.0901)
portcalls_roro	int64	0.0%	0.0 – 1.0 (mean 0.0013)
portcalls_tanker	int64	0.0%	0.0 – 2.0 (mean 0.0295)
portcalls_cargo	int64	0.0%	0.0 – 3.0 (mean 0.207)
portcalls	int64	0.0%	0.0 – 4.0 (mean 0.2365)
import_container	int64	0.0%	0.0 – 12957.0 (mean 199.722)
import_dry_bulk	int64	0.0%	0.0 – 44348.0 (mean 419.7279)
import_general_cargo	int64	0.0%	0.0 – 18644.0 (mean 127.6659)
import_roro	int64	0.0%	0.0 – 0.0 (mean 0.0)
import_tanker	int64	0.0%	0.0 – 17196.0 (mean 148.4495)
import_cargo	int64	0.0%	0.0 – 48016.0 (mean 747.1194)
import	int64	0.0%	0.0 – 48016.0 (mean 895.5711)
export_container	int64	0.0%	0.0 – 9231.0 (mean 40.9489)
export_dry_bulk	int64	0.0%	0.0 – 1670.0 (mean 1.0499)
export_general_cargo	int64	0.0%	0.0 – 4473.0 (mean 35.2774)
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%	2026-04-23

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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	2.0	0.0845	0.0
portcalls_dry_bulk	0.0	2.0	0.0312	0.0
portcalls_general_cargo	0.0	3.0	0.0901	0.0
portcalls_roro	0.0	1.0	0.0013	0.0
portcalls_tanker	0.0	2.0	0.0295	0.0
portcalls_cargo	0.0	3.0	0.207	0.0
portcalls	0.0	4.0	0.2365	0.0
import_container	0.0	12957.0	199.722	0.0
import_dry_bulk	0.0	44348.0	419.7279	0.0
import_general_cargo	0.0	18644.0	127.6659	0.0
import_roro	0.0	0.0	0.0	0.0
import_tanker	0.0	17196.0	148.4495	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_comoros,
  title     = {Comoros: Daily Port Activity Data and Shipment Estimates},
  author    = {PortWatch},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/comoros-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.