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electricsheepafrica/africa-nigeria-transport-fare-watch-a2d2cfe6

Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria) 73 rows - 1 Africa country/area - 2025 - 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-transport-fare-watch-a2d2cfe6.

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

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

rows countries period indicators license

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: Table [tbl]

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

DimensionValue
Rows73
Countries/areas1
First period2025
Last period2025
Indicators0
Columns28
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
NGA7320252025Nigeria

Indicators, Variables, Or Resource Contents

  • —This repo preserves one source tabular resource with its usable columns kept together.

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.nbs-nada-161-1236:transport-march-2025:0
country_iso3stringISO3 country or area code.NGA
country_namestringCountry or area name.Nigeria
source_sheetstringSource column from the original resource.Transport March 2025
yearint64Observation year.2025
zonestringSource column from the original resource.Air fare charg.for specified routes single journey
average_of_mar_24doubleSource column from the original resource.88964.86486486487
average_of_feb_25doubleSource column from the original resource.126586.38375263065
average_of_mar_24_2doubleSource column from the original resource.128432.80177064869
momdoubleSource column from the original resource.1.4586229286920878
yoydoubleSource column from the original resource.44.3635102079169
source_period_start_yearint64Start year inferred from source metadata.2025
source_period_end_yearint64End year inferred from source metadata.2025
source_period_labelstringSource column from the original resource.2025
source_providerstringPublishing organization.National Bureau of Statistics, Nigeria
source_datasetstringSource dataset or package title.Transport Fare Watch
source_resourcestringSource resource title, table name, or file name.Transport Fare watch March 2025 Tables
source_package_idstringSource package identifier.NGA-NBS-TRANSFW
source_resource_idstringSource resource identifier.nbs-nada-161-1236
source_urlstringOriginal source URL or download URL.https://microdata.nigerianstat.gov.ng/index.php/catalog/161/download/...
license_idstringSource license identifier.other-open
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-19T04:13:01Z
statestringSource column from the original resource.``
air_fare_charg_for_specified_routes_single_journeydoubleSource column from the original resource.``
bus_journey_intercity_state_route_charg_per_perdoubleSource column from the original resource.``
bus_journey_within_city_per_drop_constant_roudoubleSource column from the original resource.``
journey_by_motorcycle_okada_per_dropdoubleSource column from the original resource.``
water_transport_water_way_passenger_transportatdoubleSource column from the original resource.``

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-transport-fare-watch-a2d2cfe6")
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"] == "NGA"]

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

  • —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

bibtex
@misc{electric_sheep_africa_africa_nigeria_transport_fare_watch_a2d2cfe6_2025,
  title        = {Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2025},
  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-a2d2cfe6}}
}

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