Datasetpublic

electricsheepafrica/africa-uganda-cpi-composite-july-2022-aae819ab

Cpi Composite July 2022 | Africa (Uganda Bureau of Statistics) 498 rows - 1 Africa country/area - 2022 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 498 rows from Uganda Bureau of Statistics, covering Cpi Composite July 2022. 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-uganda-cpi-composite-july-2022-aae819ab.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

0likes14downloads

Dataset Card

Cpi Composite July 2022 | Africa (Uganda Bureau of Statistics)

498 rows - 1 Africa country/area - 2022 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 498 rows from Uganda Bureau of Statistics, covering Cpi Composite July 2022. 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: CPI Composite July 2022 - Last Updated on 29th July 2022

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: year.
—Time coverage basis: year.
—Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	498
Countries/areas	1
First period	2022
Last period	2022
Indicators	0
Columns	143
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
`UGA`	498	2022	2022	`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-fb5ac086a9625f:division: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.	`Division`
`year`	`int64`	Observation year.	`2022`
`d_1`	`double`	Source column from the original resource.	`2.0`
`food_and_non_alcoholic_beverages`	`string`	Source column from the original resource.	`Alcoholic Beverages, Tobacco and Narcotics`
`d_270_5390004753476`	`double`	Source column from the original resource.	`38.79579134256464`
`d_104_323446711604`	`string`	Source column from the original resource.	`99.71642479425054`
`d_104_96487548601849`	`double`	Source column from the original resource.	`100.0105572356257`
`d_106_08768062751246`	`double`	Source column from the original resource.	`101.23663455689574`
`d_106_3809938207119`	`double`	Source column from the original resource.	`101.96519982206812`
`d_105_56630673493372`	`double`	Source column from the original resource.	`102.02013772272944`
`d_104_43688446512857`	`double`	Source column from the original resource.	`102.24868564497064`
`d_103_87855461970204`	`double`	Source column from the original resource.	`102.46148669894606`
`d_104_33655828638817`	`double`	Source column from the original resource.	`102.79739181374386`
`d_104_80635073936715`	`double`	Source column from the original resource.	`102.4116810612097`
`d_105_54774785793376`	`double`	Source column from the original resource.	`103.2132221631214`
`d_106_71149099973304`	`double`	Source column from the original resource.	`103.85263401850229`
`d_104_1417213900405`	`double`	Source column from the original resource.	`103.4317186415158`
`d_101_37881348276242`	`double`	Source column from the original resource.	`103.78450004905017`
`d_101_79020982426509`	`double`	Source column from the original resource.	`103.50314325144276`
`d_104_0230914698688`	`double`	Source column from the original resource.	`103.59942000972282`
`d_103_47595130523892`	`double`	Source column from the original resource.	`103.52452976993868`
`d_102_79748766775307`	`double`	Source column from the original resource.	`103.80641566107369`
`d_101_42298987229596`	`double`	Source column from the original resource.	`103.73463385364916`
`d_101_39324873659415`	`double`	Source column from the original resource.	`104.04864369303208`
`d_100_5764504066184`	`double`	Source column from the original resource.	`104.30537046149044`
`d_101_81507672265715`	`double`	Source column from the original resource.	`104.84742008704691`
`d_104_01344105342879`	`double`	Source column from the original resource.	`104.79488634916493`
`d_106_22248081016167`	`double`	Source column from the original resource.	`104.70240498263134`
`d_104_76252303516038`	`double`	Source column from the original resource.	`105.39718468221216`
`d_103_9253652149866`	`double`	Source column from the original resource.	`104.93357573197116`
`d_104_08080980947149`	`double`	Source column from the original resource.	`104.74969585027756`
`d_105_74196816707108`	`double`	Source column from the original resource.	`105.0271370028006`
`d_106_11030217905594`	`double`	Source column from the original resource.	`104.97229770921123`
`d_106_43211006932609`	`double`	Source column from the original resource.	`105.4096956349482`
`d_106_2328175012169`	`double`	Source column from the original resource.	`104.74495579760811`
`d_107_35074872383414`	`double`	Source column from the original resource.	`104.9946120090639`
`d_107_70850262492613`	`double`	Source column from the original resource.	`104.9434215067703`
`d_106_77313414796033`	`double`	Source column from the original resource.	`105.01449257947424`
`d_110_0366995160737`	`double`	Source column from the original resource.	`105.23012635704556`
`d_111_33402246226395`	`double`	Source column from the original resource.	`105.58438118399222`
`d_108_26264019882746`	`double`	Source column from the original resource.	`105.49715361757671`
`d_105_70596781210712`	`double`	Source column from the original resource.	`105.7900425931943`
`d_106_14760095570756`	`double`	Source column from the original resource.	`105.65655571667943`
`d_105_76739481626448`	`double`	Source column from the original resource.	`105.98644797410216`
`d_105_99595332528777`	`double`	Source column from the original resource.	`107.18920454838111`
`d_104_13897425721068`	`double`	Source column from the original resource.	`107.57984211866535`
`d_103_84033743304953`	`double`	Source column from the original resource.	`107.38550598260342`
`d_104_29416137378942`	`double`	Source column from the original resource.	`107.27060614242328`
`d_105_48472337522627`	`double`	Source column from the original resource.	`107.7434471417248`
`d_107_18699709295379`	`double`	Source column from the original resource.	`107.39712091538344`
`d_107_88571212160859`	`double`	Source column from the original resource.	`107.3559339775018`
`d_107_43329574397661`	`double`	Source column from the original resource.	`107.52228097417289`
`d_106_57980217072055`	`double`	Source column from the original resource.	`107.19352301979036`
`d_106_42933368582345`	`double`	Source column from the original resource.	`107.19731435356294`
`d_107_57013224969026`	`double`	Source column from the original resource.	`106.7067957330012`
`d_108_83448011318875`	`double`	Source column from the original resource.	`106.9789009956514`
`d_109_23219946189951`	`double`	Source column from the original resource.	`106.65155429492002`
`d_108_958744578791`	`double`	Source column from the original resource.	`107.20800957290018`
`d_109_34741255113057`	`double`	Source column from the original resource.	`108.7167154802438`
`d_109_8276489361123`	`double`	Source column from the original resource.	`108.53206302551106`
`d_110_27783480759287`	`double`	Source column from the original resource.	`108.83624687491708`
`d_111_85327587200268`	`double`	Source column from the original resource.	`108.61286300673169`
`d_115_28082480641726`	`double`	Source column from the original resource.	`108.91429509966764`
`d_119_27862469390165`	`double`	Source column from the original resource.	`109.95618674014857`
`d_120_47727269882105`	`double`	Source column from the original resource.	`114.22894474907034`
`d_122_31474186286131`	`double`	Source column from the original resource.	`114.28811339744836`
`source_period_start_year`	`int64`	Start year inferred from source metadata.	`2022`
`source_period_end_year`	`int64`	End year inferred from source metadata.	`2022`
`source_period_label`	`string`	Source column from the original resource.	`2022`
`source_provider`	`string`	Publishing organization.	`Uganda Bureau of Statistics`
`source_dataset`	`string`	Source dataset or package title.	`CPI Composite July 2022`
`source_resource`	`string`	Source resource title, table name, or file name.	`CPI Composite July 2022`
`source_package_id`	`string`	Source package identifier.	`cpi-composite-july-2022-ubos-stat-fb5ac086a9625f`
`source_resource_id`	`string`	Source resource identifier.	`ubos-stat-fb5ac086a9625f`
`source_url`	`string`	Original source URL or download URL.	`https://www.ubos.org/wp-content/uploads/statistics/CPI_Composite_July...`
`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`
`row_labels`	`string`	Source column from the original resource.	``
`weights`	`double`	Source column from the original resource.	``
`2017`	`double`	Source column from the original resource.	``
`2017_2`	`double`	Source column from the original resource.	``
`2017_3`	`double`	Source column from the original resource.	``
`2017_4`	`double`	Source column from the original resource.	``
`2017_5`	`double`	Source column from the original resource.	``
`2017_6`	`double`	Source column from the original resource.	``
`2018`	`double`	Source column from the original resource.	``
`2018_2`	`double`	Source column from the original resource.	``
`2018_3`	`double`	Source column from the original resource.	``
`2018_4`	`double`	Source column from the original resource.	``
`2018_5`	`double`	Source column from the original resource.	``
`2018_6`	`double`	Source column from the original resource.	``
`2018_7`	`double`	Source column from the original resource.	``
`2018_8`	`double`	Source column from the original resource.	``
`2018_9`	`double`	Source column from the original resource.	``
`2018_10`	`double`	Source column from the original resource.	``
`2018_11`	`double`	Source column from the original resource.	``
`2018_12`	`double`	Source column from the original resource.	``
`2019`	`double`	Source column from the original resource.	``
`2019_2`	`double`	Source column from the original resource.	``
`2019_3`	`double`	Source column from the original resource.	``
`2019_4`	`double`	Source column from the original resource.	``
`2019_5`	`double`	Source column from the original resource.	``
`2019_6`	`double`	Source column from the original resource.	``
`2019_7`	`double`	Source column from the original resource.	``
`2019_8`	`double`	Source column from the original resource.	``
`2019_9`	`double`	Source column from the original resource.	``
`2019_10`	`double`	Source column from the original resource.	``
`2019_11`	`double`	Source column from the original resource.	``
`2019_12`	`double`	Source column from the original resource.	``
`2020`	`double`	Source column from the original resource.	``
`2020_2`	`double`	Source column from the original resource.	``
`2020_3`	`double`	Source column from the original resource.	``
`2020_4`	`double`	Source column from the original resource.	``
`2020_5`	`double`	Source column from the original resource.	``
`2020_6`	`double`	Source column from the original resource.	``
`2020_7`	`double`	Source column from the original resource.	``
`2020_8`	`double`	Source column from the original resource.	``
`2020_9`	`double`	Source column from the original resource.	``
`2020_10`	`double`	Source column from the original resource.	``
`2020_11`	`double`	Source column from the original resource.	``
`2020_12`	`double`	Source column from the original resource.	``
`2021`	`double`	Source column from the original resource.	``
`2021_2`	`double`	Source column from the original resource.	``
`2021_3`	`double`	Source column from the original resource.	``
`2021_4`	`double`	Source column from the original resource.	``
`2021_5`	`double`	Source column from the original resource.	``
`2021_6`	`double`	Source column from the original resource.	``
`2021_7`	`double`	Source column from the original resource.	``
`2021_8`	`double`	Source column from the original resource.	``
`2021_9`	`double`	Source column from the original resource.	``
`2021_10`	`double`	Source column from the original resource.	``
`2021_11`	`double`	Source column from the original resource.	``
`2021_12`	`double`	Source column from the original resource.	``
`2022`	`double`	Source column from the original resource.	``
`2022_2`	`double`	Source column from the original resource.	``
`2022_3`	`double`	Source column from the original resource.	``
`2022_4`	`double`	Source column from the original resource.	``
`2022_5`	`double`	Source column from the original resource.	``
`2022_6`	`double`	Source column from the original resource.	``
`2022_7`	`double`	Source column from the original resource.	``

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-cpi-composite-july-2022-aae819ab")
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

—Canonical time field: year.
—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: CPI Composite July 2022
—License: other-open
—Retrieved/generated: 2026-07-21T21:45:27Z
—Hugging Face repo: electricsheepafrica/africa-uganda-cpi-composite-july-2022-aae819ab

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
—Build time-series views and period-over-period comparisons
—Check missingness before modeling
—Use country_iso3 as the safest geography join key when present

Citation

bibtex

@misc{electric_sheep_africa_africa_uganda_cpi_composite_july_2022_aae819ab_2022,
  title        = {Cpi Composite July 2022 | Africa (Uganda Bureau of Statistics)},
  author       = {Uganda Bureau of Statistics},
  year         = {2022},
  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-cpi-composite-july-2022-aae819ab}}
}

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/