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indicator_id
stringclasses
179 values
country_id
stringclasses
1 value
year
int64
1.98k
2.03k
value
float64
0
2M
esa_source
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1 value
esa_processed
stringdate
2026-04-04 00:00:00
2026-04-04 00:00:00
CR.MOD.1.F
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2,019
80.305336
HDX
2026-04-04
ROFST.MOD.1.GPIA
GNQ
2,019
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HDX
2026-04-04
CR.MOD.3.GPIA
GNQ
1,996
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HDX
2026-04-04
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2026-04-04
CR.MOD.2.M
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2026-04-04
CR.MOD.1.GPIA
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2026-04-04
AIR.1.GLAST.F
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2026-04-04
CR.MOD.1.GPIA
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HDX
2026-04-04
ROFST.1.F.CP
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2026-04-04
YEARS.FC.FREE.1T3
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HDX
2026-04-04
CR.MOD.2
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2,000
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HDX
2026-04-04
ROFST.MOD.2
GNQ
2,000
45.900002
HDX
2026-04-04
ROFST.MOD.2.GPIA
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2,008
1.029877
HDX
2026-04-04
ROFST.MOD.2.GPIA
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2,001
1.177551
HDX
2026-04-04
CR.MOD.1.F
GNQ
1,986
36.41703
HDX
2026-04-04
AIR.2.GPV.GLAST
GNQ
2,005
12.82519
HDX
2026-04-04
ROFST.1.CP
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HDX
2026-04-04
CR.MOD.3
GNQ
2,008
6.48
HDX
2026-04-04
CR.MOD.1
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1,984
45.639999
HDX
2026-04-04
CR.MOD.2.M
GNQ
2,008
33.343742
HDX
2026-04-04
ROFST.MOD.2.F
GNQ
2,010
59.599998
HDX
2026-04-04
ROFST.MOD.2.F
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66.900002
HDX
2026-04-04
ROFST.1.M.CP
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HDX
2026-04-04
ROFST.1.CP
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HDX
2026-04-04
CR.MOD.1.GPIA
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HDX
2026-04-04
OAEPG.1.GPIA
GNQ
2,008
0.93715
HDX
2026-04-04
TRTP.1.GPIA
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0.73579
HDX
2026-04-04
ROFST.MOD.2.GPIA
GNQ
2,013
0.992188
HDX
2026-04-04
ADMI.ENDOFPRIM.MAT
GNQ
2,021
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HDX
2026-04-04
CR.MOD.2.M
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HDX
2026-04-04
CR.MOD.1
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70.580002
HDX
2026-04-04
ROFST.MOD.3.F
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2,021
77.599998
HDX
2026-04-04
ROFST.MOD.3
GNQ
2,010
69.5
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2026-04-04
ROFST.MOD.1.F
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HDX
2026-04-04
ROFST.MOD.1.F
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63.299999
HDX
2026-04-04
ODAFLOW.VOLUMESCHOLARSHIP
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2026-04-04
TRTP.1
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50.965519
HDX
2026-04-04
CR.MOD.1
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HDX
2026-04-04
ROFST.MOD.1.GPIA
GNQ
2,006
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HDX
2026-04-04
ROFST.1.GPIA.CP
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2,008
0.99448
HDX
2026-04-04
CR.MOD.3
GNQ
1,998
5.44
HDX
2026-04-04
CR.MOD.1.M
GNQ
2,017
60.081989
HDX
2026-04-04
AIR.1.GLAST.F
GNQ
2,000
31.66527
HDX
2026-04-04
ADMI.GRADE2OR3PRIM.MAT
GNQ
2,014
0
HDX
2026-04-04
CR.2.RUR
GNQ
2,000
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HDX
2026-04-04
CR.MOD.2
GNQ
1,993
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HDX
2026-04-04
ROFST.MOD.2
GNQ
2,011
60.900002
HDX
2026-04-04
CR.MOD.2
GNQ
1,991
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HDX
2026-04-04
ROFST.MOD.1.F
GNQ
2,009
61.200001
HDX
2026-04-04
ROFST.MOD.3.M
GNQ
2,002
62.200001
HDX
2026-04-04
ROFST.MOD.2.M
GNQ
2,018
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HDX
2026-04-04
YEARS.FC.FREE.1T3
GNQ
2,008
6
HDX
2026-04-04
CR.MOD.2.GPIA
GNQ
2,019
0.61609
HDX
2026-04-04
CR.MOD.1
GNQ
1,989
50.099998
HDX
2026-04-04
NERA.AGM1.F.CP
GNQ
2,015
33.99474
HDX
2026-04-04
ROFST.MOD.3
GNQ
2,009
68.400002
HDX
2026-04-04
CR.MOD.2
GNQ
2,004
24.68
HDX
2026-04-04
OAEPG.2.GPV
GNQ
2,012
61.13068
HDX
2026-04-04
NER.0.CP
GNQ
2,015
16.579069
HDX
2026-04-04
ROFST.MOD.2.F
GNQ
2,020
67.099998
HDX
2026-04-04
YEARS.FC.COMP.02
GNQ
2,008
0
HDX
2026-04-04
CR.MOD.1.M
GNQ
2,021
60.627686
HDX
2026-04-04
CR.MOD.3.F
GNQ
2,022
6.00041
HDX
2026-04-04
ROFST.MOD.3.F
GNQ
2,004
61.599998
HDX
2026-04-04
ROFST.1.GPIA.CP
GNQ
2,002
1.15313
HDX
2026-04-04
CR.MOD.3
GNQ
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HDX
2026-04-04
CR.MOD.3.F
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HDX
2026-04-04
CR.MOD.1.F
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2,008
67.647224
HDX
2026-04-04
CR.MOD.2.M
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1,988
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HDX
2026-04-04
QUTP.1
GNQ
2,015
61.104198
HDX
2026-04-04
GER.5T8
GNQ
1,991
1.39267
HDX
2026-04-04
OAEPG.1.GPIA
GNQ
2,002
1.16038
HDX
2026-04-04
NER.02.GPIA.CP
GNQ
1,999
1.04653
HDX
2026-04-04
YEARS.FC.COMP.1T3
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HDX
2026-04-04
YEARS.FC.COMP.02
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2,001
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CR.MOD.2.GPIA
GNQ
2,025
0.660012
HDX
2026-04-04
ROFST.MOD.2
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2,006
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2026-04-04
ADMI.GRADE2OR3PRIM.MAT
GNQ
2,020
0
HDX
2026-04-04
CR.MOD.3.GPIA
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2,020
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HDX
2026-04-04
ROFST.3.M.CP
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2,001
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HDX
2026-04-04
CR.MOD.1.M
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1,987
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HDX
2026-04-04
ROFST.MOD.1.M
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2,021
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HDX
2026-04-04
AIR.1.GLAST.M
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2,011
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HDX
2026-04-04
TRTP.1.F
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2,008
41.918758
HDX
2026-04-04
AIR.1.GLAST.GPIA
GNQ
2,012
1.06257
HDX
2026-04-04
NER.02.M.CP
GNQ
2,000
19.20661
HDX
2026-04-04
ROFST.MOD.3.F
GNQ
2,011
70.599998
HDX
2026-04-04
CR.MOD.1.GPIA
GNQ
1,986
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HDX
2026-04-04
YEARS.FC.FREE.02
GNQ
2,009
0
HDX
2026-04-04
CR.MOD.1.GPIA
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2,016
1.221762
HDX
2026-04-04
OAEPG.1.GPIA
GNQ
2,015
0.8681
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2026-04-04
ODAFLOW.VOLUMESCHOLARSHIP
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2,020
551,577
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2026-04-04
CR.MOD.3.GPIA
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2,025
0.615685
HDX
2026-04-04
CR.MOD.2
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2,007
25.620001
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2026-04-04
CR.MOD.1.GPIA
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2,006
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2026-04-04
YEARS.FC.FREE.1T3
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2,014
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2026-04-04
CR.MOD.3.M
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2,010
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AIR.2.GPV.GLAST.GPIA
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2,015
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2026-04-04
CR.MOD.3
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2,010
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HDX
2026-04-04
End of preview. Expand in Data Studio

Equatorial Guinea - Education Indicators | Africa (original)

Size category: 1K<n<10K - Formats: parquet - Sector: economics_finance - 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: Equatorial Guinea - Education Indicators Publisher: UNESCO · Source: HDX · License: cc-by-igo · Updated: 2026-03-02 Abstract Education indicators for Equatorial Guinea. Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February) Each row in this dataset represents… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-equatorial-guinea.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-unesco-data-for-equatorial-guinea
Sector economics_finance
Topic tags humanitarian, hdx, electric-sheep-africa, demographics, education, indicators, socioeconomics, sustainable-development, sustainable-development-goals-sdg, gnq
Modalities tabular, text
Formats parquet
Size category 1K<n<10K
Countries Equatorial Guinea
ISO3 coverage GNQ
Last modified on HF 2026-04-14 21:34:43+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/africa-unesco-data-for-equatorial-guinea")
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: upstream_publisher.
  • 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_unesco_data_for_equatorial_guinea_2026,
  title        = {Equatorial Guinea - Education Indicators | Africa (original)},
  author       = {original},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-equatorial-guinea},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-equatorial-guinea}}
}

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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