electricsheepafrica/africa-foreign-direct-investment-fdi-value-us-2015-prices

Foreign Direct Investment (FDI) — Value US$, 2015 prices | Africa (FAOSTAT)

🌍 3,315 observations · 54 Africa countries · 1990–2023 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 3,315 observations of Investment data across 54 Africa countries, spanning 1990–2023, covering 6 distinct indicators.

About the source

• Source: FAOSTAT (FAO)
• Publisher: Food and Agriculture Organization of the United Nations (FAO)
• License: cc-by-4.0
• Topic: Investment

Geographic coverage

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

Country	Rows	First year	Last year
Morocco	131	1990	2023
Tunisia	112	1990	2023
Mauritius	104	1990	2023
Egypt	90	1990	2023
Nigeria	82	1990	2023
Kenya	81	1990	2023
Mozambique	80	1990	2023
Algeria	76	1990	2023
Madagascar	73	1990	2023
Ghana	70	1990	2023
Uganda	70	1990	2023
Malawi	69	1990	2023
South Africa	68	1990	2023
Namibia	68	1990	2023
Liberia	68	1990	2023
...	39 more countries

Indicators (sample)

• Total FDI inflows
• Total FDI outflows
• FDI inflows to Agriculture, Forestry and Fishing
• FDI inflows to Food, Beverages and Tobacco
• FDI outflows to Agriculture, Forestry and Fishing
• FDI outflows to Food, Beverages and Tobacco

Schema

Column	Type	Description	Example
country_name	string	—	Algeria
country_m49	int64	—	12
item	string	—	FDI inflows to Agriculture, Forestry …
year	int64	—	2003
value	float64	—	11.889668
unit	string	—	million USD
flag	string	—	X

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-foreign-direct-investment-fdi-value-us-2015-prices")
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["item"] == "FDI inflows to Agriculture, Forestry and Fishing"]
          .sort_values("year"))
sample.plot(x="year", y="value", title="FDI inflows to Agriculture, Forestry and Fishing")

Pivot to country × year matrix

matrix = (df[df["item"] == "FDI inflows to Agriculture, Forestry and Fishing"]
          .pivot_table(index="year", columns="country_name", values="value"))
print(matrix.tail())

Citation

@misc{africa_foreign_direct_investment_fdi_value_us_2015_prices_2023,
  title        = {Foreign Direct Investment (FDI) — Value US$, 2015 prices | Africa (FAOSTAT)},
  author       = {Food and Agriculture Organization of the United Nations (FAO)},
  year         = {2023},
  url          = {https://www.fao.org/faostat/en/#data/FDI},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-foreign-direct-investment-fdi-value-us-2015-prices}}
}

License

Released under cc-by-4.0.

Original data © Food and Agriculture Organization of the United Nations (FAO). 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

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Provenance: ingested 2026-06-17 via the Electric Sheep pipeline. Source URL: https://www.fao.org/faostat/en/#data/FDI