electricsheepasia/asia-ports-turkmenistan-daily-port-activity-data-an

Turkmenistan: Daily Port Activity Data and Shipment Estimates

Publisher: PortWatch · Source: HDX · License: hdx-other · Updated: 2026-05-06

Abstract

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

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-05-06. Geographic scope: TKM.

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

Dataset Characteristics

Variables

Geographic — country (Turkmenistan), iso3 (TKM), portcalls_dry_bulk (range 0.0–2.0), import_dry_bulk (range 0.0–1429.0), export_container (range 0.0–1006.0) and 8 others.

Temporal — date, month.

Identifier / Metadata — portid (port847, port1330, port2487), portname (Okaram, Turkmenbashi Port, Kiyanly LNG Terminal), esa_source (HDX), esa_processed (2026-05-07).

Other — portcalls_container (range 0.0–1.0), portcalls_general_cargo (range 0.0–7.0), portcalls_roro (range 0.0–4.0), portcalls_tanker (range 0.0–5.0), portcalls_cargo (range 0.0–8.0) and 7 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-ports-turkmenistan-daily-port-activity-data-an")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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 for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_ports_turkmenistan_daily_port_activity_data_an,
  title     = {Turkmenistan: Daily Port Activity Data and Shipment Estimates},
  author    = {PortWatch},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/turkmenistan-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.