electricsheepafrica/africa-uganda-cpi-composite-may-2022-c08429b8

CPI Composite May 2022 | Africa (Uganda official open data)

496 rows - 1 Africa country - 2022 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official XLSX resource from Uganda as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: CPI Composite May 2022
• Publisher: Uganda Bureau of Statistics
• Resource: CPI Composite May 2022
• Format: XLSX
• License: Other open license
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
UGA	496	2022	2022	Uganda

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier for tabular resources.	ubos-stat-b070e2ef6cef5c:division:0
country_iso3	string	ISO3 country code.	UGA
country_name	string	Country name.	Uganda
source_sheet	string	Workbook sheet name, when the source is a spreadsheet.	Division
year	Int64	Observation year.	2022
d_1	float64	Source column.	2.0
food_and_non_alcoholic_beverages	string	Source column.	Alcoholic Beverages, Tobacco and Narcotics
d_270_5390004753476	float64	Source column.	38.79579134256464
d_104_323446711604	string	Source column.	99.71642479425054
d_104_96487548601849	float64	Source column.	100.0105572356257
d_106_08768062751246	float64	Source column.	101.23663455689574
d_106_3809938207119	float64	Source column.	101.96519982206812
d_105_56630673493372	float64	Source column.	102.02013772272944
d_104_43688446512857	float64	Source column.	102.24868564497064
d_103_87855461970204	float64	Source column.	102.46148669894606
d_104_33655828638817	float64	Source column.	102.79739181374389
d_104_80635073936715	float64	Source column.	102.4116810612097
d_105_54774785793376	float64	Source column.	103.2132221631214
d_106_71149099973304	float64	Source column.	103.85263401850229
d_104_1417213900405	float64	Source column.	103.4317186415158
d_101_37881348276242	float64	Source column.	103.78450004905017
d_101_79020982426509	float64	Source column.	103.50314325144276
d_104_0230914698688	float64	Source column.	103.59942000972282
d_103_47595130523892	float64	Source column.	103.52452976993868
d_102_79748766775307	float64	Source column.	103.80641566107369
d_101_42298987229596	float64	Source column.	103.73463385364916
d_101_39324873659415	float64	Source column.	104.04864369303208
d_100_5764504066184	float64	Source column.	104.30537046149044
d_101_81507672265715	float64	Source column.	104.84742008704691
d_104_01344105342879	float64	Source column.	104.79488634916493
d_106_22248081016167	float64	Source column.	104.70240498263134
d_104_76252303516038	float64	Source column.	105.39718468221216
d_103_9253652149866	float64	Source column.	104.93357573197116
d_104_08080980947149	float64	Source column.	104.74969585027756
d_105_74196816707108	float64	Source column.	105.0271370028006
d_106_11030217905594	float64	Source column.	104.97229770921123
d_106_43211006932609	float64	Source column.	105.4096956349482
d_106_2328175012169	float64	Source column.	104.74495579760811
d_107_35074872383414	float64	Source column.	104.9946120090639
d_107_70850262492613	float64	Source column.	104.9434215067703
d_106_77313414796033	float64	Source column.	105.01449257947424
d_110_0366995160737	float64	Source column.	105.23012635704556
d_111_33402246226395	float64	Source column.	105.58438118399222
d_108_26264019882746	float64	Source column.	105.49715361757671
d_105_70596781210712	float64	Source column.	105.7900425931943
d_106_14760095570756	float64	Source column.	105.65655571667943
d_105_76739481626448	float64	Source column.	105.98644797410216
d_105_99595332528777	float64	Source column.	107.18920454838111
d_104_13897425721068	float64	Source column.	107.57984211866535
d_103_84033743304953	float64	Source column.	107.3855059826034
d_104_29416137378942	float64	Source column.	107.27060614242328
d_105_48472337522627	float64	Source column.	107.7434471417248
d_107_18699709295379	float64	Source column.	107.39712091538344
d_107_88571212160859	float64	Source column.	107.3559339775018
d_107_43329574397661	float64	Source column.	107.52228097417289
d_106_57980217072055	float64	Source column.	107.19352301979036
d_106_42933368582345	float64	Source column.	107.19731435356294
d_107_57013224969026	float64	Source column.	106.7067957330012
d_108_83448011318875	float64	Source column.	106.9789009956514
d_109_23219946189951	float64	Source column.	106.65155429492002
d_108_958744578791	float64	Source column.	107.20800957290018
d_109_34741255113057	float64	Source column.	108.7167154802438
d_109_8276489361123	float64	Source column.	108.53206302551106
d_110_19854726988869	float64	Source column.	108.83624687491708
d_111_85327587200268	float64	Source column.	108.61286300673169
d_115_28082480641726	float64	Source column.	108.91429509966764
d_119_27862469390165	float64	Source column.	109.95618674014857
source_period_start_year	Int64	First year inferred from source resource metadata.	2022
source_period_end_year	Int64	Last year inferred from source resource metadata.	2022
source_period_label	string	Human-readable period inferred from source resource metadata.	2022
source_provider	string	Publishing organization.	Uganda Bureau of Statistics
source_dataset	string	Source package title.	CPI Composite May 2022
source_resource	string	Source resource title.	CPI Composite May 2022
source_package_id	string	CKAN package UUID.	cpi-composite-may-2022-ubos-stat-b070e2ef6cef5c
source_resource_id	string	CKAN resource UUID.	ubos-stat-b070e2ef6cef5c
source_url	string	Original source resource URL.	https://www.ubos.org/wp-content/uploads/statistics/CPI_Composite_May_202
license_id	string	Source license identifier.	other-open
retrieved_at	string	UTC retrieval timestamp.	2026-07-21T21:37:51Z
row_labels	string	Source column.	``
weights	float64	Source column.	``
2017	float64	Source column.	``
2017_2	float64	Source column.	``
2017_3	float64	Source column.	``
2017_4	float64	Source column.	``
2017_5	float64	Source column.	``
2017_6	float64	Source column.	``
2018	float64	Source column.	``
2018_2	float64	Source column.	``
2018_3	float64	Source column.	``
2018_4	float64	Source column.	``
2018_5	float64	Source column.	``
2018_6	float64	Source column.	``
2018_7	float64	Source column.	``
2018_8	float64	Source column.	``
2018_9	float64	Source column.	``
2018_10	float64	Source column.	``
2018_11	float64	Source column.	``
2018_12	float64	Source column.	``
2019	float64	Source column.	``
2019_2	float64	Source column.	``
2019_3	float64	Source column.	``
2019_4	float64	Source column.	``
2019_5	float64	Source column.	``
2019_6	float64	Source column.	``
2019_7	float64	Source column.	``
2019_8	float64	Source column.	``
2019_9	float64	Source column.	``
2019_10	float64	Source column.	``
2019_11	float64	Source column.	``
2019_12	float64	Source column.	``
2020	float64	Source column.	``
2020_2	float64	Source column.	``
2020_3	float64	Source column.	``
2020_4	float64	Source column.	``
2020_5	float64	Source column.	``
2020_6	float64	Source column.	``
2020_7	float64	Source column.	``
2020_8	float64	Source column.	``
2020_9	float64	Source column.	``
2020_10	float64	Source column.	``
2020_11	float64	Source column.	``
2020_12	float64	Source column.	``
2021	float64	Source column.	``
2021_2	float64	Source column.	``
2021_3	float64	Source column.	``
2021_4	float64	Source column.	``
2021_5	float64	Source column.	``
2021_6	float64	Source column.	``
2021_7	float64	Source column.	``
2021_8	float64	Source column.	``
2021_9	float64	Source column.	``
2021_10	float64	Source column.	``
2021_11	float64	Source column.	``
2021_12	float64	Source column.	``
2022	float64	Source column.	``
2022_2	float64	Source column.	``
2022_3	float64	Source column.	``
2022_4	float64	Source column.	``
2022_5	float64	Source column.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-cpi-composite-may-2022-c08429b8")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "UGA"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_uganda_cpi_composite_may_2022_c08429b8_2022,
  title        = {CPI Composite May 2022 | Africa (Uganda official open data)},
  author       = {Uganda Bureau of Statistics},
  year         = {2022},
  url          = {https://www.ubos.org/explore-statistics/0/},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-uganda-cpi-composite-may-2022-c08429b8}}
}

License

Released under Other open license.

Original data (c) Uganda Bureau of Statistics. 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 Hugging Face. 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 2026-07-21 via the Electric Sheep pipeline. Source URL: https://www.ubos.org/wp-content/uploads/statistics/CPI_Composite_May_2022.xlsx