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indicator_id
stringlengths
4
30
country_id
stringclasses
1 value
year
int64
1.97k
2.03k
value
float64
0
4.59M
esa_source
stringclasses
1 value
esa_processed
stringdate
2026-04-04 00:00:00
2026-04-04 00:00:00
SCHBSP.3.WCOMPUT
BDI
2,018
17.123289
HDX
2026-04-04
XUNIT.GDPCAP.02.FSGOV.FFNTR
BDI
1,979
0
HDX
2026-04-04
ADMI.ENDOFPRIM.MAT
BDI
2,016
1
HDX
2026-04-04
NARA.AGM1.RUR.Q2.GPIA
BDI
2,017
1.06952
HDX
2026-04-04
ROFST.H.2.Q3
BDI
2,010
21.724369
HDX
2026-04-04
ROFST.H.3.URB.F
BDI
2,010
51.478119
HDX
2026-04-04
EA.1T8.AG25T99.RUR.M
BDI
2,017
17.070089
HDX
2026-04-04
TRTP.2.F
BDI
2,019
58.326118
HDX
2026-04-04
LR.AG25T64
BDI
2,008
40.990002
HDX
2026-04-04
ROFST.H.2.Q5.M.LPIA
BDI
2,010
0.69012
HDX
2026-04-04
OAEPG.H.2.F
BDI
2,017
64.824432
HDX
2026-04-04
ROFST.MOD.1.M
BDI
2,010
16.9
HDX
2026-04-04
ROFST.H.2.Q5.GPIA
BDI
2,000
1.02357
HDX
2026-04-04
EA.5T8.AG25T99.URB
BDI
2,008
13.71642
HDX
2026-04-04
ROFST.MOD.1.F
BDI
2,023
17.1
HDX
2026-04-04
ROFST.H.3.Q5.LPIA
BDI
2,010
0.71458
HDX
2026-04-04
CR.1.WPIA
BDI
2,000
0.24382
HDX
2026-04-04
EA.S1T8.AG25T99.M
BDI
1,990
34.677349
HDX
2026-04-04
TRTP.2T3
BDI
2,019
72.139245
HDX
2026-04-04
OAEPG.2.GPV
BDI
2,008
91.338371
HDX
2026-04-04
OAEPG.H.2.RUR.F
BDI
2,017
69.223846
HDX
2026-04-04
NARA.AGM1.RUR.Q4
BDI
2,000
24.260361
HDX
2026-04-04
ROFST.H.3.LPIA
BDI
2,010
1.12596
HDX
2026-04-04
XUNIT.PPPCONST.1.FSGOV.FFNTR
BDI
2,009
177.298004
HDX
2026-04-04
ROFST.MOD.2
BDI
2,008
44.5
HDX
2026-04-04
AIR.1.GLAST.M
BDI
1,977
14.06301
HDX
2026-04-04
AIR.2.GPV.GLAST.GPIA
BDI
2,018
1.191517
HDX
2026-04-04
CR.2.URB.WPIA
BDI
2,017
0.36168
HDX
2026-04-04
ROFST.H.1.GPIA
BDI
2,010
1.0617
HDX
2026-04-04
CR.1.F.WPIA
BDI
2,000
0.29024
HDX
2026-04-04
ROFST.H.1.URB
BDI
2,017
6.09193
HDX
2026-04-04
LR.AG15T99.M.LPIA
BDI
2,014
0.78
HDX
2026-04-04
LR.AG15T24.RUR
BDI
2,010
70.98
HDX
2026-04-04
QUTP.2.GPIA
BDI
2,019
0.98078
HDX
2026-04-04
XGDP.FSGOV
BDI
1,996
4.08324
HDX
2026-04-04
QUTP.2T3
BDI
2,018
96.27148
HDX
2026-04-04
ROFST.1T2.CP
BDI
2,009
17.86665
HDX
2026-04-04
XGDP.FSGOV.FFNTR
BDI
2,009
6.171957
HDX
2026-04-04
CR.MOD.3
BDI
2,010
4.87
HDX
2026-04-04
ROFST.H.2.RUR.Q1.M
BDI
2,017
47.553249
HDX
2026-04-04
CR.2.Q3.GPIA
BDI
2,000
0
HDX
2026-04-04
ROFST.1.F.CP
BDI
2,011
3.811714
HDX
2026-04-04
ROFST.MOD.3.F
BDI
2,019
59.900002
HDX
2026-04-04
CR.MOD.2.F
BDI
2,013
11.923572
HDX
2026-04-04
ADMI.GRADE2OR3PRIM.READ
BDI
2,015
1
HDX
2026-04-04
LR.AG65T99.URB.GPIA
BDI
2,017
0.45
HDX
2026-04-04
ROFST.3.F.CP
BDI
2,012
69.752533
HDX
2026-04-04
EA.S1T8.AG25T99.RUR.M
BDI
2,014
43.567829
HDX
2026-04-04
XGDP.FSHH.FFNTR
BDI
2,007
1.04618
HDX
2026-04-04
YEARS.FC.COMP.1T3
BDI
2,015
0
HDX
2026-04-04
OAEPG.H.1.URB.F.WPIA
BDI
2,017
1.63786
HDX
2026-04-04
SCHBSP.2.WTOILA
BDI
2,017
82.467029
HDX
2026-04-04
NARA.AGM1.RUR.Q5.M
BDI
2,000
32
HDX
2026-04-04
ROFST.H.2.URB.Q1
BDI
2,017
23.92277
HDX
2026-04-04
NER.0.M.CP
BDI
2,012
3.95266
HDX
2026-04-04
ROFST.H.1.Q2.GPIA
BDI
2,017
0.90447
HDX
2026-04-04
TRTP.02
BDI
2,016
100
HDX
2026-04-04
ROFST.1.F.CP
BDI
2,017
15.59691
HDX
2026-04-04
ROFST.MOD.1.GPIA
BDI
2,019
0.875
HDX
2026-04-04
CR.2.RUR.GPIA
BDI
2,000
0.50409
HDX
2026-04-04
ROFST.H.3
BDI
2,017
47.573441
HDX
2026-04-04
ROFST.H.1.URB.F
BDI
2,010
8.20079
HDX
2026-04-04
XGOVEXP.IMF
BDI
2,002
12.48608
HDX
2026-04-04
NERA.AGM1.CP
BDI
2,017
34.917897
HDX
2026-04-04
TRTP.02.F
BDI
2,012
68.208579
HDX
2026-04-04
CR.1.RUR.Q2.GPIA
BDI
2,000
0.98529
HDX
2026-04-04
PTRHC.02.QUALIFIED
BDI
2,019
48.550707
HDX
2026-04-04
LR.AG15T24.URB.M
BDI
2,008
75.860001
HDX
2026-04-04
QUTP.2.GPIA
BDI
2,018
0.97022
HDX
2026-04-04
LR.AG65T99
BDI
1,979
2.19
HDX
2026-04-04
CR.MOD.3.GPIA
BDI
1,982
0.166388
HDX
2026-04-04
CR.2.RUR.WPIA
BDI
2,010
0.16092
HDX
2026-04-04
CR.3.GPIA
BDI
2,017
0.77784
HDX
2026-04-04
OAEPG.H.1.URB.Q1
BDI
2,010
78.75
HDX
2026-04-04
GER.5T8.M
BDI
2,002
2.23677
HDX
2026-04-04
LR.AG25T64.RUR
BDI
2,008
37.52
HDX
2026-04-04
ROFST.H.2.Q4.GPIA
BDI
2,000
1.17537
HDX
2026-04-04
ROFST.1.GPIA.CP
BDI
2,013
1.028611
HDX
2026-04-04
AIR.1.GLAST.M
BDI
1,993
54.07938
HDX
2026-04-04
CR.MOD.2.M
BDI
2,014
22.053288
HDX
2026-04-04
CR.MOD.1.F
BDI
1,989
9.125428
HDX
2026-04-04
CR.1.URB.M
BDI
2,000
29.09091
HDX
2026-04-04
SCHBSP.3.WELEC
BDI
2,017
100
HDX
2026-04-04
ADMI.ENDOFPRIM.READ
BDI
2,021
1
HDX
2026-04-04
OAEPG.H.1.URB.WPIA
BDI
2,017
1.663
HDX
2026-04-04
CR.MOD.2.M
BDI
2,001
5.116454
HDX
2026-04-04
CR.MOD.3.M
BDI
1,993
2.575668
HDX
2026-04-04
CR.MOD.3
BDI
1,992
1.75
HDX
2026-04-04
ROFST.MOD.2.GPIA
BDI
2,024
0.910256
HDX
2026-04-04
CR.MOD.1.M
BDI
2,008
25.569679
HDX
2026-04-04
XGOVEXP.IMF
BDI
2,025
10.29074
HDX
2026-04-04
CR.MOD.1
BDI
1,990
9.37
HDX
2026-04-04
CR.2.Q5.M
BDI
2,010
28.43712
HDX
2026-04-04
ROFST.H.1.F.WPIA
BDI
2,010
1.63026
HDX
2026-04-04
ROFST.MOD.1.F
BDI
2,019
18.200001
HDX
2026-04-04
CR.2.Q3
BDI
2,000
1.02041
HDX
2026-04-04
QUTP.3
BDI
2,014
75.029493
HDX
2026-04-04
NARA.AGM1.RUR.Q4.GPIA
BDI
2,010
1.51379
HDX
2026-04-04
OAEPG.1.M
BDI
2,006
50.155781
HDX
2026-04-04
SCHBSP.3.WWATA
BDI
2,019
39.93784
HDX
2026-04-04
End of preview. Expand in Data Studio

Burundi - Education Indicators

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


Abstract

Education indicators for Burundi.

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: BDI.

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


Dataset Characteristics

Domain Education
Unit of observation Country-level aggregates
Rows (total) 5,816
Columns 6 (2 numeric, 4 categorical, 0 datetime)
Train split 4,652 rows
Test split 1,163 rows
Geographic scope BDI
Publisher UNESCO
HDX last updated 2026-03-02

Variables

Geographiccountry_id (BDI), year (range 1971.0–2025.0).

Outcome / Measurementvalue (range 0.0–4593813.0).

Identifier / Metadataindicator_id (CR.MOD.2.F, CR.MOD.2.M, CR.MOD.1.F), esa_source (HDX), esa_processed (2026-04-04).


Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unesco-data-for-burundi")
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.2.F, CR.MOD.2.M, CR.MOD.1.F
country_id object 0.0% BDI
year int64 0.0% 1971.0 – 2025.0 (mean 2009.1482)
value float64 0.0% 0.0 – 4593813.0 (mean 6236.5591)
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-04

Numeric Summary

Column Min Max Mean Median
year 1971.0 2025.0 2009.1482 2012.0
value 0.0 4593813.0 6236.5591 8.8083

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_burundi,
  title     = {Burundi - Education Indicators},
  author    = {UNESCO},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unesco-data-for-burundi},
  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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