electricsheepafrica/africa-uganda-2016-annual-crime-data-b3aa9de2

2016 Annual Crime Data | Africa (Uganda Bureau of Statistics)

104 rows - 1 Africa country/area - 2016-2020 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 104 rows from Uganda Bureau of Statistics, covering 2016 Annual Crime Data. 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: 2016 Annual Crime Data - Last Updated on 4th February 2020

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

Dimension	Value
Rows	104
Countries/areas	1
First period	2016
Last period	2020
Indicators	0
Columns	53
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
UGA	104	2016	2020	Uganda

Indicators, Variables, Or Resource Contents

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

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	ubos-stat-d46056adb88104:cons-annual-2016-cleaned:0
country_iso3	string	ISO3 country or area code.	UGA
country_name	string	Country or area name.	Uganda
source_sheet	string	Source column from the original resource.	CONS ANNUAL 2016-cleaned
d_16	double	Source column from the original resource.	17.0
obtaining_by_false_pretences	string	Source column from the original resource.	Cyber (Computer) Crimes
d_14065	double	Source column from the original resource.	106.0
d_6231	double	Source column from the original resource.	80.0
d_5253	double	Source column from the original resource.	14.0
d_2581	double	Source column from the original resource.	12.0
d_4517	double	Source column from the original resource.	18.0
d_821	double	Source column from the original resource.	6.0
d_59	double	Source column from the original resource.	0.0
d_396	double	Source column from the original resource.	2.0
d_1305	double	Source column from the original resource.	4.0
d_2473	double	Source column from the original resource.	14.0
d_4	double	Source column from the original resource.	0.0
d_249	double	Source column from the original resource.	1.0
d_0	double	Source column from the original resource.	0.0
d_801	double	Source column from the original resource.	6.0
d_1	double	Source column from the original resource.	0.0
d_78	double	Source column from the original resource.	1.0
d_0_2	double	Source column from the original resource.	0.0
d_70	double	Source column from the original resource.	0.0
d_411	double	Source column from the original resource.	4.0
d_1365	double	Source column from the original resource.	4.0
d_572	double	Source column from the original resource.	3.0
d_0_3	double	Source column from the original resource.	0.0
d_50	double	Source column from the original resource.	0.0
d_0_4	double	Source column from the original resource.	0.0
d_0_5	double	Source column from the original resource.	0.0
d_112	double	Source column from the original resource.	1.0
d_3	double	Source column from the original resource.	0.0
d_70_2	double	Source column from the original resource.	1.0
d_73	double	Source column from the original resource.	2.0
d_60389002	double	Source column from the original resource.	600000.0
d_10637	double	Source column from the original resource.	115.0
d_23	double	Source column from the original resource.	0.0
d_3399	double	Source column from the original resource.	44.0
d_6	double	Source column from the original resource.	0.0
d_47769107595	double	Source column from the original resource.	11915384817.0
d_6036046151	double	Source column from the original resource.	41527000.0
source_period_start_year	int64	Start year inferred from source metadata.	2016
source_period_end_year	int64	End year inferred from source metadata.	2020
source_period_label	string	Source column from the original resource.	2016-2020
source_provider	string	Publishing organization.	Uganda Bureau of Statistics
source_dataset	string	Source dataset or package title.	2016 Annual Crime Data
source_resource	string	Source resource title, table name, or file name.	2016 Annual Crime Data
source_package_id	string	Source package identifier.	2016-annual-crime-data-ubos-stat-d46056adb88104
source_resource_id	string	Source resource identifier.	ubos-stat-d46056adb88104
source_url	string	Original source URL or download URL.	https://www.ubos.org/wp-content/uploads/statistics/2016_Annual_Crime_...
license_id	string	Source license identifier.	other-open
retrieved_at	string	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-07-21T21:37:51Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-2016-annual-crime-data-b3aa9de2")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

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

Time-Series Pattern

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

Pivot For Analysis

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

• Source: Uganda Bureau of Statistics
• Publisher: Uganda Bureau of Statistics
• Portal: https://www.ubos.org
• Resource: 2016 Annual Crime Data
• License: other-open
• Retrieved/generated: 2026-07-21T21:38:13Z
• Hugging Face repo: electricsheepafrica/africa-uganda-2016-annual-crime-data-b3aa9de2

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

@misc{electric_sheep_africa_africa_uganda_2016_annual_crime_data_b3aa9de2_2020,
  title        = {2016 Annual Crime Data | Africa (Uganda Bureau of Statistics)},
  author       = {Uganda Bureau of Statistics},
  year         = {2020},
  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-2016-annual-crime-data-b3aa9de2}}
}

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.

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