Spotify-Africa Music Research
Collection
Comprehensive African music dataset: 1,320+ tracks, 650+ artists, 67 years, 5 regions, 52 enrichment features • 12 items • Updated • 1
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
track_id: string
track_name: string
artist_id: string
artist_name: string
artist_popularity: int64
artist_followers: int64
artist_genres: string
album_id: string
album_name: string
album_type: string
album_release_date: string
release_year: int64
track_number: int64
disc_number: int64
duration_ms: int64
duration_min: double
explicit: bool
popularity: int64
preview_url: double
spotify_url: string
isrc: string
available_markets: int64
collected_date: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 2892
to
{'artist_id': Value('string'), 'artist_name': Value('string'), 'genres': Value('string'), 'popularity': Value('int64'), 'followers': Value('int64'), 'spotify_url': Value('string'), 'collected_date': Value('string'), 'related_artists': Value('string'), 'genres_str': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 220, in _generate_tables
yield Key(file_idx, batch_idx), self._cast_table(pa_table)
~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
track_id: string
track_name: string
artist_id: string
artist_name: string
artist_popularity: int64
artist_followers: int64
artist_genres: string
album_id: string
album_name: string
album_type: string
album_release_date: string
release_year: int64
track_number: int64
disc_number: int64
duration_ms: int64
duration_min: double
explicit: bool
popularity: int64
preview_url: double
spotify_url: string
isrc: string
available_markets: int64
collected_date: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 2892
to
{'artist_id': Value('string'), 'artist_name': Value('string'), 'genres': Value('string'), 'popularity': Value('int64'), 'followers': Value('int64'), 'spotify_url': Value('string'), 'collected_date': Value('string'), 'related_artists': Value('string'), 'genres_str': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Spotify-Africa Music Dataset | Africa (Electric Sheep Africa metadata inventory)
Size category: 1K<n<10K - Formats: parquet - Sector: culture_language - Engineered by Electric Sheep Africa
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: Spotify-Africa Music Dataset 🎵🌍 A comprehensive, research-grade dataset documenting African music from Spotify spanning 1,600+ tracks, 650+ artists, and 67 years of musical history (1958-2025). Dataset Summary This dataset provides rich metadata about African music across multiple genres, regions, and time periods. It includes track-level information, artist metadata, temporal trends, regional summaries, and network relationships. The data was collected via the Spotify… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Spotify-Africa-Dataset.
Dataset Profile
| Field | Value |
|---|---|
| Hugging Face repo | electricsheepafrica/Spotify-Africa-Dataset |
| Sector | culture_language |
| Topic tags | music, spotify, afrobeats, amapiano, music-analysis, cultural-studies |
| Modalities | tabular, text |
| Formats | parquet |
| Size category | 1K<n<10K |
| Countries | Africa-wide or source-defined African coverage |
| ISO3 coverage | not declared |
| Last modified on HF | 2025-10-31 08:49:43+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/Spotify-Africa-Dataset")
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.
- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
Source And Provenance
- Source context: Electric Sheep Africa metadata inventory
- Publisher/source attribution: Public dataset metadata
- License: CC BY 4.0
- Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/Spotify-Africa-Dataset
- Inventory retrieved at:
2026-07-16T16:00:34Z
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_spotify_africa_dataset_2026,
title = {Spotify-Africa Music Dataset | Africa (Electric Sheep Africa metadata inventory)},
author = {Public dataset metadata},
year = {2026},
url = {https://huggingface.co/datasets/electricsheepafrica/Spotify-Africa-Dataset},
publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/Spotify-Africa-Dataset}}
}
License
Released under CC BY 4.0.
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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