Datasetpublic

electricsheepafrica/africa-uganda-index-of-industrial-production-q2-fy-2025-26-excel-tables-e40ea539

Index of Industrial Production Q2 Fy 2025 26 Excel Tables | Africa (Uganda Bureau of Statistics) 195 rows - 1 Africa country/area - 2025-2026 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 195 rows from Uganda Bureau of Statistics, covering Index of Industrial Production Q2 Fy 2025 26 Excel Tables. 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-index-of-industrial-production-q2-fy-2025-26-excel-tables-e40ea539.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

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Dataset Card

Index of Industrial Production Q2 Fy 2025 26 Excel Tables | Africa (Uganda Bureau of Statistics)

195 rows - 1 Africa country/area - 2025-2026 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 195 rows from Uganda Bureau of Statistics, covering Index of Industrial Production Q2 Fy 2025 26 Excel Tables. 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: Index of Industrial Production Q2 FY 2025/26 Excel Tables - Last Updated on 19th June 2026

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	195
Countries/areas	1
First period	2025
Last period	2026
Indicators	0
Columns	129
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
`UGA`	195	2025	2026	`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-2c087fa7fcd938:quarterly-indicesf: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.	`Quarterly_IndicesF`
`section`	`string`	Source column from the original resource.	`Section C`
`division`	`string`	Source column from the original resource.	``
`class`	`double`	Source column from the original resource.	``
`total_industrial_sector`	`string`	Source column from the original resource.	`MANUFACTURING SECTOR`
`d_999_9999999999999`	`double`	Source column from the original resource.	`775.6006421701388`
`d_95_26184685091573`	`double`	Source column from the original resource.	`94.82245219875072`
`d_99_45560574061498`	`double`	Source column from the original resource.	`99.28752766963925`
`d_102_70987674469654`	`double`	Source column from the original resource.	`102.9985872715538`
`d_102_57267066377271`	`double`	Source column from the original resource.	`102.89143286005624`
`d_99_83436720666093`	`double`	Source column from the original resource.	`99.00809719952538`
`d_99_46906635508317`	`double`	Source column from the original resource.	`98.42463349346389`
`d_103_38189053010285`	`double`	Source column from the original resource.	`103.23989461255974`
`d_105_39912790523016`	`double`	Source column from the original resource.	`105.94001663093574`
`d_107_45176932067413`	`double`	Source column from the original resource.	`107.35137413881952`
`d_112_80860174897617`	`double`	Source column from the original resource.	`114.83072665423374`
`d_115_03286124436124`	`double`	Source column from the original resource.	`116.96481232746426`
`d_113_34056790900156`	`double`	Source column from the original resource.	`114.43465003007788`
`d_119_10610622714573`	`double`	Source column from the original resource.	`121.00141097894172`
`d_119_53613306694986`	`double`	Source column from the original resource.	`121.40690254230374`
`d_118_40290210224828`	`double`	Source column from the original resource.	`119.951380305588`
`d_107_29347861881304`	`double`	Source column from the original resource.	`107.85892198599473`
`d_120_27393750865268`	`double`	Source column from the original resource.	`121.83111965415158`
`d_121_5690715164947`	`double`	Source column from the original resource.	`122.96180561059556`
`d_126_46894598380665`	`double`	Source column from the original resource.	`128.07978287488297`
`d_129_5167667656794`	`double`	Source column from the original resource.	`131.07800508265197`
`d_126_30908179010396`	`double`	Source column from the original resource.	`124.90308734276095`
`d_129_98529602501037`	`double`	Source column from the original resource.	`129.24321155253404`
`d_133_52622091267963`	`double`	Source column from the original resource.	`133.27890361067952`
`d_134_07260453757155`	`double`	Source column from the original resource.	`132.93292313510517`
`d_135_1774206715722`	`double`	Source column from the original resource.	`135.3820354600928`
`d_138_61593772252874`	`double`	Source column from the original resource.	`139.01108315258705`
`d_141_21147447890678`	`double`	Source column from the original resource.	`141.48101532453484`
`d_141_51046496639196`	`double`	Source column from the original resource.	`140.42349954282176`
`d_149_30735837203974`	`double`	Source column from the original resource.	`147.19249948396822`
`d_149_5578149267383`	`double`	Source column from the original resource.	`147.87168376154497`
`d_152_57931634631174`	`double`	Source column from the original resource.	`152.03013421905897`
`d_152_78816913069411`	`double`	Source column from the original resource.	`152.72534748795505`
`d_159_17637208894507`	`double`	Source column from the original resource.	`157.12750594136972`
`d_159_1209268307068`	`double`	Source column from the original resource.	`158.11204792094773`
`d_161_30245073383927`	`double`	Source column from the original resource.	`160.026000140244`
`d_163_09011480320058`	`double`	Source column from the original resource.	`161.60980072024003`
`d_163_23993805212592`	`double`	Source column from the original resource.	`159.40042730696834`
`d_165_65737043453666`	`double`	Source column from the original resource.	`163.21272031899335`
`source_period_start_year`	`int64`	Start year inferred from source metadata.	`2025`
`source_period_end_year`	`int64`	End year inferred from source metadata.	`2026`
`source_period_label`	`string`	Source column from the original resource.	`2025-2026`
`source_provider`	`string`	Publishing organization.	`Uganda Bureau of Statistics`
`source_dataset`	`string`	Source dataset or package title.	`Index of Industrial Production Q2 FY 2025/26 Excel Tables`
`source_resource`	`string`	Source resource title, table name, or file name.	`Index of Industrial Production Q2 FY 2025/26 Excel Tables`
`source_package_id`	`string`	Source package identifier.	`index-of-industrial-production-q2-fy-2025-26-excel-tables-ubos-stat-2...`
`source_resource_id`	`string`	Source resource identifier.	`ubos-stat-2c087fa7fcd938`
`source_url`	`string`	Original source URL or download URL.	`https://www.ubos.org/wp-content/uploads/statistics/06_2026Index_of_In...`
`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`
`d_4_402348923869241`	`double`	Source column from the original resource.	``
`d_3_272084041767201`	`double`	Source column from the original resource.	``
`d_0_1335860632613617`	`double`	Source column from the original resource.	``
`d_2_6696228531357917`	`double`	Source column from the original resource.	``
`d_0_36590691341947945`	`double`	Source column from the original resource.	``
`d_3_9337095625807166`	`double`	Source column from the original resource.	``
`d_1_9512482938585123`	`double`	Source column from the original resource.	``
`d_1_9474937376043613`	`double`	Source column from the original resource.	``
`d_4_985336641889404`	`double`	Source column from the original resource.	``
`d_1_971710898726073`	`double`	Source column from the original resource.	``
`d_1_4711390441421628`	`double`	Source column from the original resource.	``
`d_5_086914971851186`	`double`	Source column from the original resource.	``
`d_0_36104516672222076`	`double`	Source column from the original resource.	``
`d_0_9480237779374079`	`double`	Source column from the original resource.	``
`d_9_382729043112107`	`double`	Source column from the original resource.	``
`d_12_098087467138583`	`double`	Source column from the original resource.	``
`d_1_076820161266312`	`double`	Source column from the original resource.	``
`d_4_030527177833321`	`double`	Source column from the original resource.	``
`d_2_4099360978804896`	`double`	Source column from the original resource.	``
`d_2_476656154780912`	`double`	Source column from the original resource.	``
`d_2_910490823625352`	`double`	Source column from the original resource.	``
`d_2_724096490873819`	`double`	Source column from the original resource.	``
`d_0_40919575283211884`	`double`	Source column from the original resource.	``
`d_0_8240431651277902`	`double`	Source column from the original resource.	``
`d_2_543706658903318`	`double`	Source column from the original resource.	``
`d_1_8724663260393726`	`double`	Source column from the original resource.	``
`d_0_21173243080174586`	`double`	Source column from the original resource.	``
`d_5_50976453048861`	`double`	Source column from the original resource.	``
`d_0_16774562046330743`	`double`	Source column from the original resource.	``
`d_2_0202898932787576`	`double`	Source column from the original resource.	``
`d_0_1368814524691686`	`double`	Source column from the original resource.	``
`d_4_181084827835406`	`double`	Source column from the original resource.	``
`d_0_034832593249007004`	`double`	Source column from the original resource.	``
`d_1_3709849147959403`	`double`	Source column from the original resource.	``
`d_1_1082683872615746`	`double`	Source column from the original resource.	``
`d_0_09186531575267054`	`double`	Source column from the original resource.	``
`d_1_4809074367810666`	`double`	Source column from the original resource.	``
`d_4_799949304889253`	`double`	Source column from the original resource.	``
`d_0_013534294389884849`	`double`	Source column from the original resource.	``
`d_0_654283508758084`	`double`	Source column from the original resource.	``
`d_2_7555656133029913`	`double`	Source column from the original resource.	``
`d_7_630039962335715`	`double`	Source column from the original resource.	``
`d_13_41073751137236`	`double`	Source column from the original resource.	``
`d_11_269837158632583`	`double`	Source column from the original resource.	``
`d_7_5346354012643815`	`double`	Source column from the original resource.	``
`d_10_846109822250511`	`double`	Source column from the original resource.	``
`d_5_963668739502609`	`double`	Source column from the original resource.	``
`d_2_9296331686718133`	`double`	Source column from the original resource.	``
`d_5_335326442905767`	`double`	Source column from the original resource.	``
`d_0_9804965660448914`	`double`	Source column from the original resource.	``
`d_1_70068948809498`	`double`	Source column from the original resource.	``
`d_6_812370084132596`	`double`	Source column from the original resource.	``
`d_20_71261779648337`	`double`	Source column from the original resource.	``
`d_5_017832130936185`	`double`	Source column from the original resource.	``
`d_6_922998097730584`	`double`	Source column from the original resource.	``
`d_5_580243334815663`	`double`	Source column from the original resource.	``
`d_3_5175660153210515`	`double`	Source column from the original resource.	``
`d_7_021141121273729`	`double`	Source column from the original resource.	``
`d_6_63970615250031`	`double`	Source column from the original resource.	``
`d_5_755613776602701`	`double`	Source column from the original resource.	``
`d_5_547636263556058`	`double`	Source column from the original resource.	``
`d_10_452883055667783`	`double`	Source column from the original resource.	``
`d_7_893664598736265`	`double`	Source column from the original resource.	``
`d_8_050225315863415`	`double`	Source column from the original resource.	``
`d_7_969519545413519`	`double`	Source column from the original resource.	``
`d_6_609864258875973`	`double`	Source column from the original resource.	``
`d_6_394257570995563`	`double`	Source column from the original resource.	``
`d_5_717114610559989`	`double`	Source column from the original resource.	``
`d_6_742633105115786`	`double`	Source column from the original resource.	``
`d_2_552870071011654`	`double`	Source column from the original resource.	``
`d_4_107846613276806`	`double`	Source column from the original resource.	``

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-index-of-industrial-production-q2-fy-2025-26-excel-tables-e40ea539")
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

—Source: Uganda Bureau of Statistics
—Publisher: Uganda Bureau of Statistics
—Portal: https://www.ubos.org
—Resource: Index of Industrial Production Q2 FY 2025/26 Excel Tables
—License: other-open
—Retrieved/generated: 2026-07-21T22:01:18Z
—Hugging Face repo: electricsheepafrica/africa-uganda-index-of-industrial-production-q2-fy-2025-26-excel-tables-e40ea539

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_index_of_industrial_production_q2_fy_2025_26_excel_tables_e40ea539_2026,
  title        = {Index of Industrial Production Q2 Fy 2025 26 Excel Tables | Africa (Uganda Bureau of Statistics)},
  author       = {Uganda Bureau of Statistics},
  year         = {2026},
  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-index-of-industrial-production-q2-fy-2025-26-excel-tables-e40ea539}}
}

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/