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electricsheepafrica/africa-uganda-cipi-excel-tables-for-february-2026-ae518649

Cipi Excel Tables for February 2026 | Africa (Uganda Bureau of Statistics) 8,112 rows - 1 Africa country/area - 2017-2026 - 1 indicator - Engineered by Electric Sheep Africa TL;DR This dataset contains 8,112 rows from Uganda Bureau of Statistics, covering Cipi Excel Tables for February 2026. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-uganda-cipi-excel-tables-for-february-2026-ae518649.

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Cipi Excel Tables for February 2026 | Africa (Uganda Bureau of Statistics)

8,112 rows - 1 Africa country/area - 2017-2026 - 1 indicator - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 8,112 rows from Uganda Bureau of Statistics, covering Cipi Excel Tables for February 2026. 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: CIPI Excel Tables For February 2026 - Last Updated on 31st March 2026

How To Read This Dataset

  • —One row means: one indicator observation for one geography, time period, and optional source dimensions.
  • —Primary geography column: country_iso3.
  • —Best time column: year.
  • —Time coverage basis: year.
  • —Recommended join keys: country_iso3, year, indicator_id.

Coverage

DimensionValue
Rows8,112
Countries/areas1
First period2017
Last period2026
Indicators1
Columns22
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
UGA8,11220172026Uganda

Indicators, Variables, Or Resource Contents

  • —cipi-excel-tables-for-february-2026-ae518649 - CIPI Excel Tables For February 2026(sourceunitsunspecified)

Schema

ColumnTypeDescriptionExample
indicator_idstringStable source or Electric Sheep Africa indicator identifier.cipi-excel-tables-for-february-2026-ae518649
indicator_namestringHuman-readable indicator name.CIPI Excel Tables For February 2026
country_iso3stringISO3 country or area code.UGA
source_sheetstringSource column from the original resource.ALL CONSTRUCTION
country_namestringCountry or area name.Uganda
yearint64Observation year.2017
valuedoubleNumeric observation value.96.75513309771502
unitstringMeasurement unit, when supplied by the source.source_units_unspecified
dimension_s_nostringSource dimension retained during long-form normalization.1
dimension_selected_productsstringSource dimension retained during long-form normalization.Aggregate, hardcore, crushed or broken stone
dimension_weightsstringSource dimension retained during long-form normalization.30.449629219361256
source_period_start_yearint64Start year inferred from source metadata.2026
source_period_end_yearint64End year inferred from source metadata.2026
source_period_labelstringSource column from the original resource.2026
source_providerstringPublishing organization.Uganda Bureau of Statistics
source_datasetstringSource dataset or package title.CIPI Excel Tables For February 2026
source_resourcestringSource resource title, table name, or file name.CIPI Excel Tables For February 2026
source_package_idstringSource package identifier.cipi-excel-tables-for-february-2026-ubos-stat-f37f5b3ff32138
source_resource_idstringSource resource identifier.ubos-stat-f37f5b3ff32138
source_urlstringOriginal source URL or download URL.https://www.ubos.org/wp-content/uploads/statistics/03_2026CIPI_FEBRUA...
license_idstringSource license identifier.other-open
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-21T21:37:51Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-uganda-cipi-excel-tables-for-february-2026-ae518649")
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

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
  • —Pivot to geography x period or indicator x period matrices
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_uganda_cipi_excel_tables_for_february_2026_ae518649_2026,
  title        = {Cipi Excel Tables for February 2026 | 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-cipi-excel-tables-for-february-2026-ae518649}}
}

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/