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electricsheepafrica/africa-uganda-commercial-banks-foreign-currency-loans-to-the-private-sec-1a806b7c

Commercial Banks Foreign Currency Loans to the Private Sec | Africa (Uganda Bureau of Statistics) 65 rows - 1 Africa country/area - 2014-2021 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 65 rows from Uganda Bureau of Statistics, covering Commercial Banks Foreign Currency Loans to the Private Sec. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-uganda-commercial-banks-foreign-currency-loans-to-the-private-sec-1a806b7c.

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Dataset Card

Commercial Banks Foreign Currency Loans to the Private Sec | Africa (Uganda Bureau of Statistics)

65 rows - 1 Africa country/area - 2014-2021 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 65 rows from Uganda Bureau of Statistics, covering Commercial Banks Foreign Currency Loans to the Private Sec. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: Commercial banks foreign currency loans to the private sector (Billion shillings), June 2014-2019 - Last Updated on 21st July 2021

How To Read This Dataset

  • —One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
  • —Primary geography column: country_iso3.
  • —Best time column: not detected.
  • —Time coverage basis: source metadata.
  • —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

DimensionValue
Rows65
Countries/areas1
First period2014
Last period2021
Indicators0
Columns29
Source formatXLSX

Geographic Coverage

Top areas shown below, sorted by row count when available:

AreaRowsFirst yearLast yearName
UGA6520142021Uganda

Indicators, Variables, Or Resource Contents

  • —This repo preserves one source tabular resource with its usable columns kept together.

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.ubos-stat-1c5b6cc76b3e1a:sheet3:0
country_iso3stringISO3 country or area code.UGA
country_namestringCountry or area name.Uganda
source_sheetstringSource column from the original resource.Sheet3
total_liabilities_billion_shillingsstringSource column from the original resource.DEPOSITS
2014doubleSource column from the original resource.12406.0
2015doubleSource column from the original resource.14491.0
2016doubleSource column from the original resource.15578.0
2017doubleSource column from the original resource.17197.0
2018doubleSource column from the original resource.19100.0
2019doubleSource column from the original resource.21031.0
source_period_start_yearint64Start year inferred from source metadata.2014
source_period_end_yearint64End year inferred from source metadata.2021
source_period_labelstringSource column from the original resource.2014-2021
source_providerstringPublishing organization.Uganda Bureau of Statistics
source_datasetstringSource dataset or package title.Commercial banks foreign currency loans to the private sector (Billio...
source_resourcestringSource resource title, table name, or file name.Commercial banks foreign currency loans to the private sector (Billio...
source_package_idstringSource package identifier.commercial-banks-foreign-currency-loans-to-the-private-sector-billion...
source_resource_idstringSource resource identifier.ubos-stat-1c5b6cc76b3e1a
source_urlstringOriginal source URL or download URL.https://www.ubos.org/wp-content/uploads/statistics/Commercial_banks_f...
license_idstringSource license identifier.other-open
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-21T21:37:51Z
agriculturestringSource column from the original resource.``
d_413doubleSource column from the original resource.``
d_546doubleSource column from the original resource.``
d_585doubleSource column from the original resource.``
d_712doubleSource column from the original resource.``
d_766doubleSource column from the original resource.``
d_779doubleSource column from the original resource.``

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-commercial-banks-foreign-currency-loans-to-the-private-sec-1a806b7c")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

python
print(df.info())
print(df.head())

Filter By Geography

python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "UGA"]

Time-Series Pattern

python
if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

python
if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

  • —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
  • —Missing values are preserved rather than silently imputed.
  • —Column names are standardized for machine use; source meanings are preserved where known.
  • —Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

Transformations Applied

  • —Converted the source table to Parquet for efficient analytics and ML workflows.
  • —Added or preserved source provenance columns where available.
  • —Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
  • —Preserved source-reported values without analytical imputation.

Suggested Analyses

  • —Profile the distribution of values
  • —Compare categories or geographies
  • —Join with complementary public datasets
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_uganda_commercial_banks_foreign_currency_loans_to_the_private_sec_1a806b7_2021,
  title        = {Commercial Banks Foreign Currency Loans to the Private Sec | Africa (Uganda Bureau of Statistics)},
  author       = {Uganda Bureau of Statistics},
  year         = {2021},
  url          = {https://www.ubos.org/explore-statistics/0/},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-uganda-commercial-banks-foreign-currency-loans-to-the-private-sec-1a806b7c}}
}

License

Released under other-open.

Original data is published by Uganda Bureau of Statistics. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://www.ubos.org/explore-statistics/0/