electricsheepafrica/africa-nigeria-transport-fare-watch-c72b90c9

Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria)

73 rows - 1 Africa country/area - 2026 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 73 rows from National Bureau of Statistics, Nigeria, covering Transport Fare Watch. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

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	73
Countries/areas	1
First period	2026
Last period	2026
Indicators	0
Columns	28
Source format	ZIP

Geographic Coverage

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

Area	Rows	First year	Last year	Name
NGA	73	2026	2026	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-161-1429:transport-cost-watch-may-2026-xl:0
country_iso3	string	ISO3 country or area code.	NGA
country_name	string	Country or area name.	Nigeria
source_sheet	string	Source column from the original resource.	TRANSPORT COST Watch MAY_2026_.xlsx::Transport May 2026
year	int64	Observation year.	2026
zone	string	Source column from the original resource.	Air fare charg.for specified routes single journey
average_of_may_25	double	Source column from the original resource.	130361.8462114865
average_of_apr_26	double	Source column from the original resource.	157355.8675448459
average_of_may_26	double	Source column from the original resource.	157552.18763797908
mom	double	Source column from the original resource.	0.1247618510807778
yoy	double	Source column from the original resource.	20.857591555110066
source_period_start_year	int64	Start year inferred from source metadata.	2026
source_period_end_year	int64	End year inferred from source metadata.	2026
source_period_label	string	Source column from the original resource.	2026
source_provider	string	Publishing organization.	National Bureau of Statistics, Nigeria
source_dataset	string	Source dataset or package title.	Transport Fare Watch
source_resource	string	Source resource title, table name, or file name.	Transport Fare Watch Report May 2026
source_package_id	string	Source package identifier.	NGA-NBS-TRANSFW
source_resource_id	string	Source resource identifier.	nbs-nada-161-1429
source_url	string	Original source URL or download URL.	https://microdata.nigerianstat.gov.ng/index.php/catalog/161/download/...
license_id	string	Source license identifier.	other-open
retrieved_at	string	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-07-19T10:27:56Z
state	string	Source column from the original resource.	``
air_fare_charg_for_specified_routes_single_journey	double	Source column from the original resource.	``
bus_journey_intercity_state_route_charg_per_per	double	Source column from the original resource.	``
bus_journey_within_city_per_drop_constant_rou	double	Source column from the original resource.	``
journey_by_motorcycle_okada_per_drop	double	Source column from the original resource.	``
water_transport_water_way_passenger_transportat	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-transport-fare-watch-c72b90c9")
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

• Source: National Bureau of Statistics, Nigeria
• Publisher: National Bureau of Statistics, Nigeria
• Portal: https://microdata.nigerianstat.gov.ng
• Resource: Transport Fare Watch Report May 2026
• License: other-open
• Retrieved/generated: 2026-07-19T10:28:34Z
• Hugging Face repo: electricsheepafrica/africa-nigeria-transport-fare-watch-c72b90c9

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

• Track mobility over time
• Compare routes or geographies
• Join with economic and population 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_transport_fare_watch_c72b90c9_2026,
  title        = {Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2026},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/161/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-transport-fare-watch-c72b90c9}}
}

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/161/related-materials