electricsheepafrica/africa-world-bank-financial-sector-indicators-for-zimbabwe

Zimbabwe - Financial Sector

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.

An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries.

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)	2,822
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	2,257 rows
Test split	564 rows
Geographic scope	ZWE
Publisher	World Bank Group
HDX last updated	2026-03-27

---

Variables

Geographic — country_name (Zimbabwe), country_iso3 (ZWE), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -604196000.0–7569697684.9433).

Identifier / Metadata — indicator_name (Domestic credit to private sector (% of GDP), Net migration, DEC alternative conversion factor (LCU per US$)), indicator_code (SM.POP.NETM, PA.NUS.ATLS, FI.RES.TOTL.CD), esa_source (HDX), esa_processed (2026-04-10).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-financial-sector-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 – 2025.0 (mean 2001.5879)
indicator_name	object	0.0%	Domestic credit to private sector (% of GDP), Net migration, DEC alternative conversion factor (LCU per US$)
indicator_code	object	0.0%	SM.POP.NETM, PA.NUS.ATLS, FI.RES.TOTL.CD
value	float64	0.0%	-604196000.0 – 7569697684.9433 (mean 55396269.1602)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-10

---

Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	2001.5879	2002.0
value	-604196000.0	7569697684.9433	55396269.1602	12.5035

---

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_financial_sector_indicators_for_zimbabwe,
  title     = {Zimbabwe - Financial Sector},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-financial-sector-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.