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electricsheepafrica
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africa-nigeria-national-household-kerosene-price-watch-291f6b7b

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
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Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
tabular
africa
open-data
official-statistics
nigeria
national-bureau-of-statistics-nigeria
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africa-nigeria-national-household-kerosene-price-watch-291f6b7b / README.md
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metadata 
license: other
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
multilinguality: multilingual
size_categories:
  - n<1K
tags:
  - tabular
  - africa
  - open-data
  - official-statistics
  - nigeria
  - national-bureau-of-statistics-nigeria
  - economics
  - national-economy
  - nhk-report-october-2025
  - nhk-report-oct-2025-zip
  - '0'
  - document
  - nhk
  - report-doc
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
pretty_name: >-
  National Household Kerosene Price Watch | Africa (National Bureau of
  Statistics, Nigeria)

National Household Kerosene Price Watch | Africa (National Bureau of Statistics, Nigeria)

51 rows - 1 Africa country/area - 2025 - source table - Engineered by Electric Sheep Africa

rows countries period indicators license

TL;DR

This dataset contains 51 rows from National Bureau of Statistics, Nigeria, covering National Household Kerosene Price Watch. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.

Source-provided context: Document, Report [doc/rep]

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 51
Countries/areas 1
First period 2025
Last period 2025
Indicators 0
Columns 27
Source format ZIP

Geographic Coverage

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

Area Rows First year Last year Name
NGA 51 2025 2025 Nigeria

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. nbs-nada-159-1329:0
country_iso3 string ISO3 country or area code. NGA
country_name string Country or area name. Nigeria
year int64 Observation year. 2025
north_central string Source column from the original resource. Abuja
d_2242_674201064777 double Source column from the original resource. 2875.0
d_1790_5719366323756 double Source column from the original resource. 1655.9835
d_2702_800502298753 double Source column from the original resource. 2153.49191666667
d_50_946211487155026 double Source column from the original resource. 30.043078126483152
d_20_5168588917426 double Source column from the original resource. -25.09593333333322
north_central_2 string Source column from the original resource. Abuja
d_6176_202336834174 double Source column from the original resource. 6600.75
d_7861_645143417122 double Source column from the original resource. 8301.92575
d_10045_895609535155 double Source column from the original resource. 9813.9676666667
d_27_783630859337308 double Source column from the original resource. 18.21314671077008
d_62_65489797221451 double Source column from the original resource. 48.679584390663166
source_period_start_year int64 Start year inferred from source metadata. 2025
source_period_end_year int64 End year inferred from source metadata. 2025
source_period_label string Source column from the original resource. 2025
source_provider string Publishing organization. National Bureau of Statistics, Nigeria
source_dataset string Source dataset or package title. National Household Kerosene Price Watch
source_resource string Source resource title, table name, or file name. NHK Report October 2025
source_package_id string Source package identifier. NGA-NBS-NHK
source_resource_id string Source resource identifier. nbs-nada-159-1329
source_url string Original source URL or download URL. https://microdata.nigerianstat.gov.ng/index.php/catalog/159/download/...
license_id string Source license identifier. other-open
retrieved_at string UTC source retrieval timestamp from the Electric Sheep Africa pipeline. 2026-07-19T04:13:01Z

Usage 

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-national-household-kerosene-price-watch-291f6b7b")
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"] == "NGA"]

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

  • 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

  • Build time-series dashboards
  • Compare economic indicators
  • Join with population or sector data
  • 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 

@misc{electric_sheep_africa_africa_nigeria_national_household_kerosene_price_watch_291f6b7b_2025,
  title        = {National Household Kerosene Price Watch | Africa (National Bureau of Statistics, Nigeria)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2025},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/159/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-national-household-kerosene-price-watch-291f6b7b}}
}

License

Released under other-open.

Original data is published by National Bureau of Statistics, Nigeria. 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://microdata.nigerianstat.gov.ng/index.php/catalog/159/related-materials