Datasets:

electricsheepafrica
/

africa-world-bank-trade-indicators-for-zimbabwe

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - indicators
  - trade
  - zwe
pretty_name: Zimbabwe - Trade
dataset_info:
  splits:
    - name: train
      num_examples: 3358
    - name: test
      num_examples: 839

Zimbabwe - Trade

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Trade is a key means to fight poverty and achieve the Millennium Development Goals, specifically by improving developing country access to markets, and supporting a rules based, predictable trading system. In cooperation with other international development partners, the World Bank launched the Transparency in Trade Initiative to provide free and easy access to data on country-specific trade policies.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: ZWE.

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

Dataset Characteristics


Domain	Poverty and economic vulnerability
Unit of observation	Country-level aggregates
Rows (total)	4,198
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	3,358 rows
Test split	839 rows
Geographic scope	ZWE
Publisher	World Bank Group
HDX last updated	2026-03-27

Variables

Geographic — country_name (Zimbabwe), country_iso3 (ZWE), year (range 1960.0–2024.0).

Outcome / Measurement — value (range -4402366967.1392–22074260817.5123).

Identifier / Metadata — indicator_name (Merchandise trade (% of GDP), Merchandise imports (current US$), Merchandise exports (current US$)), indicator_code (TG.VAL.TOTL.GD.ZS, TM.VAL.MRCH.CD.WT, TX.VAL.MRCH.CD.WT), esa_source (HDX), esa_processed (2026-04-11).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-trade-indicators-for-zimbabwe")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`country_name`	object	0.0%	Zimbabwe
`country_iso3`	object	0.0%	ZWE
`year`	int64	0.0%	1960.0 – 2024.0 (mean 2001.4474)
`indicator_name`	object	0.0%	Merchandise trade (% of GDP), Merchandise imports (current US$), Merchandise exports (current US$)
`indicator_code`	object	0.0%	TG.VAL.TOTL.GD.ZS, TM.VAL.MRCH.CD.WT, TX.VAL.MRCH.CD.WT
`value`	float64	0.0%	-4402366967.1392 – 22074260817.5123 (mean 539633016.6936)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-11

Numeric Summary

Column	Min	Max	Mean	Median
`year`	1960.0	2024.0	2001.4474	2003.0
`value`	-4402366967.1392	22074260817.5123	539633016.6936	30.0528

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. 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 World Bank Group 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_africa_world_bank_trade_indicators_for_zimbabwe,
  title     = {Zimbabwe - Trade},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-trade-indicators-for-zimbabwe},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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