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Year
stringdate
1960-01-01 00:00:00
2024-01-01 00:00:00
carbon_dioxide_co2_emissions_from_transport_energy_mt_co2e_
float64
0
59.7
mortality_caused_by_road_traffic_injury_per_100_000_population_
float64
6.6
44
railways_passengers_carried_million_passenger_km_
float64
0
73.6k
1960-01-01
2.661
22.5
1,797
1961-01-01
2.661
22.5
1,797
1962-01-01
2.661
22.5
1,797
1963-01-01
2.661
22.5
1,797
1964-01-01
2.661
22.5
1,797
1965-01-01
2.661
22.5
1,797
1966-01-01
2.661
22.5
1,797
1967-01-01
2.661
22.5
1,797
1968-01-01
2.661
22.5
1,797
1969-01-01
2.661
22.5
1,797
1970-01-01
2.661
22.5
1,797
1971-01-01
2.661
22.5
1,797
1972-01-01
2.6399
22.5
1,797
1973-01-01
2.9807
22.5
1,797
1974-01-01
3.2823
22.5
1,797
1975-01-01
3.6534
22.5
1,797
1976-01-01
4.1244
22.5
1,797
1977-01-01
4.5409
22.5
1,797
1978-01-01
5.1943
22.5
1,797
1979-01-01
5.6681
22.5
1,797
1980-01-01
6.2158
22.5
1,797
1981-01-01
6.7851
22.5
1,797
1982-01-01
7.0963
22.5
1,797
1983-01-01
7.6044
22.5
1,797
1984-01-01
8.1588
22.5
1,797
1985-01-01
12.4098
22.5
1,797
1986-01-01
12.5378
22.5
1,797
1987-01-01
12.6655
22.5
1,797
1988-01-01
12.7079
22.5
1,797
1989-01-01
12.9295
22.5
1,797
1990-01-01
15.8086
22.5
1,797
1991-01-01
16.8803
22.5
1,797
1992-01-01
16.8012
22.5
1,797
1993-01-01
16.9609
22.5
1,797
1994-01-01
15.5212
22.5
1,797
1995-01-01
15.11
22.5
1,797
1996-01-01
14.9641
22.5
1,826
1997-01-01
15.0132
22.5
1,360
1998-01-01
15.4611
22.5
1,163
1999-01-01
16.4715
22.5
1,069
2000-01-01
16.8469
22.5
1,142
2001-01-01
17.0412
22.7
981
2002-01-01
19.3579
22.8
954.868
2003-01-01
21.7604
22.7
964
2004-01-01
22.025
22.5
950
2005-01-01
24.1619
22.1
929
2006-01-01
25.6265
22.2
821
2007-01-01
27.6012
22.3
757.531
2008-01-01
29.1924
22.4
937.081
2009-01-01
30.5164
22.5
1,141
2010-01-01
31.7105
22.3
1,045
2011-01-01
34.2189
22.3
1,040
2012-01-01
38.6433
21.4
1,141
2013-01-01
40.2637
21.1
1,163.5
2014-01-01
43.5052
21.2
1,186
2015-01-01
46.4091
21.2
1,269
2016-01-01
45.1858
21.1
1,337
2017-01-01
44.6934
21
1,550
2018-01-01
45.4601
21
1,602
2019-01-01
45.6034
20.9
1,454
2020-01-01
40.4659
20.9
348
2021-01-01
43.3215
20.9
348
2022-01-01
44.4738
20.9
348
2023-01-01
46.5578
20.9
348
2024-01-01
46.5578
20.9
348
1960-01-01
1.0262
25.1
null
1961-01-01
1.0262
25.1
null
1962-01-01
1.0262
25.1
null
1963-01-01
1.0262
25.1
null
1964-01-01
1.0262
25.1
null
1965-01-01
1.0262
25.1
null
1966-01-01
1.0262
25.1
null
1967-01-01
1.0262
25.1
null
1968-01-01
1.0262
25.1
null
1969-01-01
1.0262
25.1
null
1970-01-01
1.0262
25.1
null
1971-01-01
1.0262
25.1
null
1972-01-01
1.7155
25.1
null
1973-01-01
1.4475
25.1
null
1974-01-01
1.3019
25.1
null
1975-01-01
1.0984
25.1
null
1976-01-01
1.107
25.1
null
1977-01-01
0.6488
25.1
null
1978-01-01
1.333
25.1
null
1979-01-01
1.0552
25.1
null
1980-01-01
0.9961
25.1
null
1981-01-01
0.8719
25.1
null
1982-01-01
0.8559
25.1
null
1983-01-01
0.7771
25.1
null
1984-01-01
0.7024
25.1
null
1985-01-01
1.0394
25.1
null
1986-01-01
1.0261
25.1
null
1987-01-01
1.042
25.1
null
1988-01-01
1.2429
25.1
null
1989-01-01
1.304
25.1
null
1990-01-01
1.0165
25.1
null
1991-01-01
1.2574
25.1
null
1992-01-01
1.187
25.1
null
1993-01-01
1.2119
25.1
null
1994-01-01
1.541
25.1
null
End of preview. Expand in Data Studio

Africa Transport All | Africa (World Bank)

Size category: 1K<n<10K - Formats: csv - Sector: infrastructure_transport - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Master Datacard for Transportation Indicators for African Countries This repository contains time-series datasets for key transportation indicators for 54 African countries. The data is sourced from The World Bank and has been cleaned, processed, and organized for analysis. Each country has its own set of files, including a main CSV dataset and a corresponding datacard in Markdown format. The data covers the period from 1960 to 2024, where available. Repository… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-transport-all.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-transport-all
Sector infrastructure_transport
Topic tags infrastructure_transport
Modalities tabular, text
Formats csv
Size category 1K<n<10K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2025-06-21 10:58:21+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-transport-all")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: country, upstream_publisher, license, language.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_transport_all_2026,
  title        = {Africa Transport All | Africa (World Bank)},
  author       = {World Bank open data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-transport-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-transport-all}}
}

License

Released under Source-specific or other license.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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