license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- zip
- africa
- nigeria
- official-statistics
- open-data
- economics
pretty_name: Automotive Gas Oil (Diesel) Price Watch | Africa (Nigeria official open data)
Automotive Gas Oil (Diesel) Price Watch | Africa (Nigeria official open data)
43 rows - 1 Africa country - 2026 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official ZIP resource from Nigeria 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: Automotive Gas Oil (Diesel) Price Watch
- Publisher: National Bureau of Statistics, Nigeria
- Resource: AGO Report February 2026
- Format:
ZIP - License: Other open license
- Packaging mode:
tabular_resource
Geographic coverage
1 Africa country:
| Country | Rows | First year | Last year | Name |
|---|---|---|---|---|
NGA |
43 | 2026 | 2026 | Nigeria |
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. | nbs-nada-158-1375:0 |
country_iso3 |
string |
ISO3 country code. | NGA |
country_name |
string |
Country name. | Nigeria |
year |
Int64 |
Observation year. | 2026 |
north_central |
string |
Source column. | Abuja |
d_1674_302457755102 |
float64 |
Source column. | 2080.0 |
d_1334_9231464149664 |
float64 |
Source column. | 1332.3665533333333 |
d_1382_1663798577308 |
float64 |
Source column. | 1409.6666666666667 |
d_15_335562357368769 |
float64 |
Source column. | -32.2275641025641 |
d_3_5837221750995445 |
float64 |
Source column. | 5.801715236692406 |
south_east |
string |
Source column. | North Central |
d_1383_7686593824135 |
float64 |
Source column. | 1382.1663798577308 |
source_period_start_year |
Int64 |
First year inferred from source resource metadata. | 2026 |
source_period_end_year |
Int64 |
Last year inferred from source resource metadata. | 2026 |
source_period_label |
string |
Human-readable period inferred from source resource metadata. | 2026 |
source_provider |
string |
Publishing organization. | National Bureau of Statistics, Nigeria |
source_dataset |
string |
Source package title. | Automotive Gas Oil (Diesel) Price Watch |
source_resource |
string |
Source resource title. | AGO Report February 2026 |
source_package_id |
string |
CKAN package UUID. | NGA-NBS-AGO |
source_resource_id |
string |
CKAN resource UUID. | nbs-nada-158-1375 |
source_url |
string |
Original source resource URL. | https://microdata.nigerianstat.gov.ng/index.php/catalog/158/download/137 |
license_id |
string |
Source license identifier. | other-open |
retrieved_at |
string |
UTC retrieval timestamp. | 2026-07-19T04:13:01Z |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-nigeria-automotive-gas-oil-diesel-price-watch-f7913487")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
sample_country = df[df["country_iso3"] == "NGA"]
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_nigeria_automotive_gas_oil_diesel_price_watch_f7913487_2026,
title = {Automotive Gas Oil (Diesel) Price Watch | Africa (Nigeria official open data)},
author = {National Bureau of Statistics, Nigeria},
year = {2026},
url = {https://microdata.nigerianstat.gov.ng/index.php/catalog/158/related-materials},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-automotive-gas-oil-diesel-price-watch-f7913487}}
}
License
Released under Other open license.
Original data (c) National Bureau of Statistics, Nigeria. 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
Provenance: ingested 2026-07-19 via the Electric Sheep pipeline. Source URL: https://microdata.nigerianstat.gov.ng/index.php/catalog/158/download/1375