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Add ML-ready official indicator dataset
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metadata
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
pretty_name: Rail Transportation Data | Africa (Nigeria official open data)

Rail Transportation Data | Africa (Nigeria official open data)

34 rows - 1 Africa country - 2024-2025 - Repackaged by Electric Sheep Africa

rows countries years indicators license

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

Geographic coverage

1 Africa country:

Country Rows First year Last year Name
NGA 34 2024 2025 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-150-1321:rail-transport-data-q4-2024-xlsx:0
country_iso3 string ISO3 country code. NGA
country_name string Country name. Nigeria
source_sheet string Workbook sheet name, when the source is a spreadsheet. Rail_Transport_Data_Q4 2024.xlsx::Sheet2
d_1 float64 Source column. 2.0
quantity_of_passengers_number string Source column. Volume of Goods/Cargos (Tons)
d_723995 float64 Source column. 54099.0
d_748345 float64 Source column. 79750.0
d_815262 float64 Source column. 55630.0
d_602509 float64 Source column. 10634.0
d_2890111 float64 Source column. 200113.0
d_647055 float64 Source column. 18484.0
d_108238_2236916601 float64 Source column. 8691.1313518221
d_130258 float64 Source column. 24528.0
d_134817_03 float64 Source column. 35736.48
d_1020368_2536916601 float64 Source column. 87439.6113518221
d_424460 float64 Source column. 9071.0
d_565385 float64 Source column. 27695.0
d_696841 float64 Source column. 51726.0
d_1027772 float64 Source column. 53946.0
d_2714458 float64 Source column. 142438.0
d_953099 float64 Source column. 39379.0
d_422393 float64 Source column. 31197.0
d_500348 float64 Source column. 33312.0
d_1337108 float64 Source column. 53136.0
d_3212948 float64 Source column. 157024.0
d_441725 float64 Source column. 59966.0
d_474117 float64 Source column. 56936.0
d_594348 float64 Source column. 69003.0
d_672198 float64 Source column. 119286.0
d_2182388 float64 Source column. 305191.0
d_675293 float64 Source column. 160650.0
d_689263 float64 Source column. 143759.0
d_743205 float64 Source column. 96401.0
d_1037113 float64 Source column. 94750.0
d_3144874 float64 Source column. 495560.0
d_39_54602027704335 float64 Source column. -1.7126378357070986
d_54_286832153621404 float64 Source column. -20.569052529215497
source_period_start_year Int64 First year inferred from source resource metadata. 2024
source_period_end_year Int64 Last year inferred from source resource metadata. 2025
source_period_label string Human-readable period inferred from source resource metadata. 2024-2025
source_provider string Publishing organization. National Bureau of Statistics, Nigeria
source_dataset string Source package title. Rail Transportation Data
source_resource string Source resource title. Rail Transport Q4 2024-Q1 2025
source_package_id string CKAN package UUID. NGA-NBS-RAIL
source_resource_id string CKAN resource UUID. nbs-nada-150-1321
source_url string Original source resource URL. https://microdata.nigerianstat.gov.ng/index.php/catalog/150/download/132
license_id string Source license identifier. other-open
retrieved_at string UTC retrieval timestamp. 2026-07-19T10:27:56Z
d_929553 string Source column. ``
d_10_3710974599682 float64 Source column. ``
d_37_65180447598302 float64 Source column. ``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-rail-transportation-data-833d5dce")
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_rail_transportation_data_833d5dce_2025,
  title        = {Rail Transportation Data | Africa (Nigeria official open data)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2025},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/150/related-materials},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-rail-transportation-data-833d5dce}}
}

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/150/download/1321