Datasets:

electricsheepafrica
/

africa-comtrade-angola-exports-by-partner

license: other
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 1K<n<10K
tags:
  - tabular
  - africa
  - un-comtrade
  - international-merchandise-trade
  - trade
  - exports-imports
  - hs
  - bilateral
  - time-series
pretty_name: Angola — Exports by Trading Partner | Africa (UN Comtrade)

Angola — Exports by Trading Partner | Africa (UN Comtrade)

🌍 5,056 observations · 1 Africa countries · 2004–2023 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 5,056 observations of International merchandise trade data across 1 Africa countries, spanning 2004–2023, covering 208 distinct indicators.

About the source

Source: UN Comtrade
Publisher: United Nations Statistics Division (UN Comtrade)
License: other
Topic: International merchandise trade

Geographic coverage

1 Africa countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
`Angola`	5,056	2004	2023

Indicators (sample)

Afghanistan — Afghanistan
Albania — Albania
Algeria — Algeria
American Samoa — American Samoa
Andorra — Andorra
Antarctica — Antarctica
Antigua and Barbuda — Antigua and Barbuda
Areas, nes — Areas, nes
Argentina — Argentina
Armenia — Armenia
... and 198 more indicators

Schema

Column	Type	Description	Example
`country_name`	`string`	—	`Angola`
`country_iso3`	`string`	—	`AGO`
`partner_name`	`string`	—	`Afghanistan`
`partner_iso3`	`string`	—	`AFG`
`year`	`int64`	—	`2004`
`flow`	`string`	—	`exports`
`trade_value_usd`	`float64`	—	`14392.521`

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-comtrade-angola-exports-by-partner")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["country_name"] == "KEN"]

Time-series for a single indicator

sample = (df[df["partner_name"] == "Afghanistan"]
          .sort_values("year"))
sample.plot(x="year", y="trade_value_usd", title="Afghanistan")

Pivot to country × year matrix

matrix = (df[df["partner_name"] == "Afghanistan"]
          .pivot_table(index="year", columns="country_name", values="trade_value_usd"))
print(matrix.tail())

Citation

@misc{africa_comtrade_angola_exports_by_partner_2023,
  title        = {Angola — Exports by Trading Partner | Africa (UN Comtrade)},
  author       = {United Nations Statistics Division (UN Comtrade)},
  year         = {2023},
  url          = {https://comtradeplus.un.org/},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-comtrade-angola-exports-by-partner}}
}

License

Released under other.

Original data © United Nations Statistics Division (UN Comtrade). When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

Provenance: ingested 2026-06-17 via the Electric Sheep pipeline. Source URL: https://comtradeplus.un.org/