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indicator_id
stringlengths
4
30
country_id
stringclasses
1 value
year
int64
1.97k
2.03k
value
float64
0
8.9M
esa_source
stringclasses
1 value
esa_processed
stringdate
2026-04-04 00:00:00
2026-04-04 00:00:00
AIR.1.GLAST.M
COG
2,010
70.127449
HDX
2026-04-04
ROFST.MOD.2.GPIA
COG
2,015
0.917808
HDX
2026-04-04
OAEPG.H.2.RUR
COG
2,015
54.443562
HDX
2026-04-04
CR.MOD.2.GPIA
COG
1,988
0.54493
HDX
2026-04-04
CR.3.URB.Q4.GPIA
COG
2,005
0.95085
HDX
2026-04-04
AIR.1.GLAST
COG
1,987
71.981651
HDX
2026-04-04
ROFST.2T3.CP
COG
1,974
44.450439
HDX
2026-04-04
CR.MOD.1.M
COG
1,995
61.975662
HDX
2026-04-04
FHLANGILP.G2T3.F
COG
2,014
14.71665
HDX
2026-04-04
YEARS.FC.COMP.1T3
COG
2,018
10
HDX
2026-04-04
ROFST.H.1.RUR.Q1.F
COG
2,005
13.82066
HDX
2026-04-04
OAEPG.H.2.Q1.GPIA
COG
2,015
0.89611
HDX
2026-04-04
PREPFUTURE.1.READ.GPIA
COG
2,014
1.11473
HDX
2026-04-04
ROFST.H.3.RUR.Q2.F
COG
2,015
44.655079
HDX
2026-04-04
ROFST.H.3.M
COG
2,015
20.06756
HDX
2026-04-04
CR.1.Q4.M
COG
2,015
92.397987
HDX
2026-04-04
ROFST.MOD.1.F
COG
2,010
23.200001
HDX
2026-04-04
ROFST.MOD.2.M
COG
2,020
35.299999
HDX
2026-04-04
NARA.AGM1.M.WPIA
COG
2,005
0.54163
HDX
2026-04-04
CR.MOD.1
COG
1,987
65.169998
HDX
2026-04-04
EA.3T8.AG25T99.M.LPIA
COG
2,015
0.22689
HDX
2026-04-04
MATH.PRIMARY.M
COG
2,014
4.96
HDX
2026-04-04
ROFST.MOD.1.F
COG
2,015
19.6
HDX
2026-04-04
ROFST.H.2
COG
2,012
9.77366
HDX
2026-04-04
ROFST.H.1.Q3.M.LPIA
COG
2,012
1.41425
HDX
2026-04-04
ROFST.H.3.Q4.GPIA
COG
2,015
1.19862
HDX
2026-04-04
ROFST.MOD.1
COG
2,011
22.1
HDX
2026-04-04
CR.MOD.1.F
COG
2,006
63.865894
HDX
2026-04-04
CR.MOD.3.F
COG
1,999
9.626543
HDX
2026-04-04
AIR.2.GPV.GLAST.GPIA
COG
1,972
0.31404
HDX
2026-04-04
CR.2.RUR.Q1.F
COG
2,012
4.41617
HDX
2026-04-04
AIR.2.GPV.GLAST.F
COG
2,003
19.00631
HDX
2026-04-04
NER.02.M.CP
COG
2,004
6.85035
HDX
2026-04-04
CR.2.Q1.M
COG
2,015
6.11077
HDX
2026-04-04
CR.MOD.3.GPIA
COG
2,004
0.601365
HDX
2026-04-04
CR.3.URB
COG
2,012
19.57868
HDX
2026-04-04
EA.4T8.AG25T99.GPIA
COG
1,984
0.21725
HDX
2026-04-04
ROFST.H.1.Q3.GPIA
COG
2,015
0.79615
HDX
2026-04-04
PRYA.12MO.AG15T64.GPIA
COG
2,009
1.00156
HDX
2026-04-04
NER.02.F.CP
COG
2,010
11.62547
HDX
2026-04-04
CR.MOD.3
COG
1,996
15.02
HDX
2026-04-04
CR.1.Q4.F
COG
2,005
78.948227
HDX
2026-04-04
ROFST.H.2.Q2.M.LPIA
COG
2,005
0.46375
HDX
2026-04-04
OAEPG.1
COG
2,010
21.451071
HDX
2026-04-04
CR.1.RUR.Q4
COG
2,015
83.796272
HDX
2026-04-04
CR.MOD.2.GPIA
COG
2,013
0.860229
HDX
2026-04-04
READ.G2.F
COG
2,019
65.8
HDX
2026-04-04
YEARS.FC.FREE.02
COG
1,999
3
HDX
2026-04-04
CR.MOD.1.GPIA
COG
2,024
0.989114
HDX
2026-04-04
ROFST.H.1.URB.M
COG
2,005
4.22279
HDX
2026-04-04
CR.3.M.LPIA
COG
2,015
0.12053
HDX
2026-04-04
CR.1.RUR.Q3.GPIA
COG
2,015
0.9108
HDX
2026-04-04
GER.5T8.M
COG
1,983
9.14598
HDX
2026-04-04
SCHBSP.1.WELEC
COG
2,018
24.104481
HDX
2026-04-04
CR.1.URB.Q3.GPIA
COG
2,005
1.17724
HDX
2026-04-04
CR.1.URB.Q4
COG
2,015
93.023666
HDX
2026-04-04
ROFST.H.1.M.WPIA
COG
2,015
1.98318
HDX
2026-04-04
CR.2.M
COG
2,012
36.901291
HDX
2026-04-04
CR.1.Q5.F
COG
2,005
88.38884
HDX
2026-04-04
ROFST.MOD.1.F
COG
2,002
41.799999
HDX
2026-04-04
AIR.1.GLAST.M
COG
2,007
73.755241
HDX
2026-04-04
ROFST.H.3.WPIA
COG
2,015
1.78364
HDX
2026-04-04
CR.3.RUR.Q3.F
COG
2,012
7.41558
HDX
2026-04-04
CR.2.RUR.Q3.F
COG
2,012
21.393511
HDX
2026-04-04
ROFST.MOD.2.F
COG
2,001
35.299999
HDX
2026-04-04
AIR.1.GLAST.M
COG
1,994
55.491909
HDX
2026-04-04
XGDP.FSGOV.FFNTR
COG
1,998
4.06827
HDX
2026-04-04
ROFST.H.1.F.WPIA
COG
2,005
1.91094
HDX
2026-04-04
CR.MOD.3.GPIA
COG
1,994
0.364723
HDX
2026-04-04
LR.AG15T24
COG
1,984
87.5
HDX
2026-04-04
TRTP.1.GPIA
COG
2,003
1.33844
HDX
2026-04-04
ROFST.H.3.URB.Q4.GPIA
COG
2,015
1.20252
HDX
2026-04-04
LR.GALP.AG15T24.F
COG
2,021
79.5
HDX
2026-04-04
EA.2T8.AG25T99.M.WPIA
COG
2,015
0.16746
HDX
2026-04-04
ROFST.H.1.RUR.Q2.F
COG
2,012
4.00938
HDX
2026-04-04
XUNIT.PPPCONST.3.FSGOV.FFNTR
COG
2,002
1,630.716431
HDX
2026-04-04
NARA.AGM1.Q2.F
COG
2,012
48.570938
HDX
2026-04-04
ROFST.1.GPIA.CP
COG
2,023
0.98132
HDX
2026-04-04
CR.MOD.1.M
COG
1,984
71.386444
HDX
2026-04-04
ROFST.MOD.1
COG
2,000
51.799999
HDX
2026-04-04
ODAFLOW.VOLUMESCHOLARSHIP
COG
2,024
5,141,203
HDX
2026-04-04
XGDP.FSGOV
COG
1,978
8.41331
HDX
2026-04-04
CR.2.URB.Q5.F
COG
2,012
59.653809
HDX
2026-04-04
ROFST.H.1.M.WPIA
COG
2,012
1.84669
HDX
2026-04-04
ROFST.MOD.1.M
COG
2,013
22.4
HDX
2026-04-04
CR.MOD.2
COG
1,994
27.66
HDX
2026-04-04
CR.1.URB.Q5.M
COG
2,015
93.516632
HDX
2026-04-04
NER.02.M.CP
COG
2,011
10.51104
HDX
2026-04-04
CR.3.URB.M
COG
2,012
20.257509
HDX
2026-04-04
SCHBSP.3.WELEC
COG
2,018
79.853477
HDX
2026-04-04
CR.MOD.3
COG
2,004
12.25
HDX
2026-04-04
ADMI.ENDOFLOWERSEC.MAT
COG
2,014
0
HDX
2026-04-04
LR.AG15T24.URB
COG
2,005
89.74
HDX
2026-04-04
CR.MOD.1.M
COG
1,996
60.876984
HDX
2026-04-04
XGOVEXP.IMF
COG
2,013
14.724898
HDX
2026-04-04
GER.5T8.M
COG
1,990
7.70603
HDX
2026-04-04
ONTRACK.THREE.DOMAINS.F
COG
2,015
65.1
HDX
2026-04-04
GER.5T8.F
COG
1,983
1.4116
HDX
2026-04-04
AIR.2.GPV.GLAST
COG
2,004
38.42065
HDX
2026-04-04
EA.1T8.AG25T99.F.LPIA
COG
2,015
0.48454
HDX
2026-04-04
End of preview. Expand in Data Studio

Congo - Education Indicators

Publisher: UNESCO · Source: HDX · License: cc-by-igo · Updated: 2026-03-02


Abstract

Education indicators for Congo.

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 country-level aggregates. Data was last updated on HDX on 2026-03-02. Geographic scope: COG.

Curated into ML-ready Parquet format by Electric Sheep Africa.


Dataset Characteristics

Domain Education
Unit of observation Country-level aggregates
Rows (total) 4,082
Columns 6 (2 numeric, 4 categorical, 0 datetime)
Train split 3,265 rows
Test split 816 rows
Geographic scope COG
Publisher UNESCO
HDX last updated 2026-03-02

Variables

Geographiccountry_id (COG), year (range 1970.0–2025.0).

Outcome / Measurementvalue (range 0.0–8900204.0).

Identifier / Metadataindicator_id (CR.MOD.1.F, CR.MOD.1, CR.MOD.3.M), esa_source (HDX), esa_processed (2026-04-04).


Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unesco-data-for-congo")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
indicator_id object 0.0% CR.MOD.1.F, CR.MOD.1, CR.MOD.3.M
country_id object 0.0% COG
year int64 0.0% 1970.0 – 2025.0 (mean 2007.1761)
value float64 0.0% 0.0 – 8900204.0 (mean 18667.4915)
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-04

Numeric Summary

Column Min Max Mean Median
year 1970.0 2025.0 2007.1761 2012.0
value 0.0 8900204.0 18667.4915 10.5884

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 2 column(s) with >80% missing values were removed: magnitude, qualifier. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.


Limitations

  • Data originates from UNESCO and has not been independently validated by ESA.
  • Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_unesco_data_for_congo,
  title     = {Congo - Education Indicators},
  author    = {UNESCO},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unesco-data-for-congo},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.

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