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indicator_id
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31
country_id
stringclasses
1 value
year
int64
1.97k
2.03k
value
float64
0
20.8M
esa_source
stringclasses
1 value
esa_processed
stringdate
2026-04-04 00:00:00
2026-04-04 00:00:00
EA.3T8.AG25T99.GPIA
EGY
2,022
0.835391
HDX
2026-04-04
ROFST.H.2.URB.Q1.M
EGY
2,017
6.81
HDX
2026-04-04
ROFST.1T2.CP
EGY
2,018
10.840786
HDX
2026-04-04
EA.3T8.AG25T99.M.LPIA
EGY
2,017
0.782429
HDX
2026-04-04
PRYA.12MO.AG15T24.GPIA
EGY
2,017
1.00939
HDX
2026-04-04
EA.1T8.AG25T99.RUR
EGY
2,012
44.552935
HDX
2026-04-04
CR.1.RUR.Q4.GPIA
EGY
2,009
0.9954
HDX
2026-04-04
ROFST.H.2.LPIA
EGY
2,009
1.51427
HDX
2026-04-04
NERA.AGM1.GPIA.CP
EGY
2,000
0.95016
HDX
2026-04-04
CR.1.URB.Q3.M
EGY
2,014
80.314713
HDX
2026-04-04
EA.5T8.AG25T99.M.LPIA
EGY
2,008
0.38215
HDX
2026-04-04
ROFST.H.1.RUR.Q4.F
EGY
2,005
0.61949
HDX
2026-04-04
PRYA.12MO.AG15T24.F
EGY
2,021
53.514049
HDX
2026-04-04
ICTSKILLSNTWK.AG25T74.M
EGY
2,013
22.2
HDX
2026-04-04
ROFST.H.3.Q4.F.LPIA
EGY
2,022
1.36674
HDX
2026-04-04
ROFST.2.CP
EGY
2,004
20.25284
HDX
2026-04-04
ICTSKILLHLTHINF.M.LPIA
EGY
2,016
0.52657
HDX
2026-04-04
EA.S1T8.AG25T99.Q1.F
EGY
2,021
43.951611
HDX
2026-04-04
SCHBSP.3.WINTERN
EGY
2,024
56.811349
HDX
2026-04-04
ROFST.H.3.Q1.GPIA
EGY
2,014
1.23664
HDX
2026-04-04
ICTSKILLONLSFT.M
EGY
2,018
1
HDX
2026-04-04
ICTSKILLONLSFT.URB
EGY
2,014
0.1
HDX
2026-04-04
ICTSKILLSNTWK.URB.M
EGY
2,017
51.7
HDX
2026-04-04
CR.2.URB.Q5.M
EGY
2,017
94.65
HDX
2026-04-04
NER.0.M.CP
EGY
2,012
19.682551
HDX
2026-04-04
LR.AG65T99.URB.M
EGY
2,010
58.209999
HDX
2026-04-04
ICTSKILLONLSFT
EGY
2,022
3.6
HDX
2026-04-04
ICTSKILLGSINF.AG25T74.M
EGY
2,019
23.7
HDX
2026-04-04
NARA.AGM1.Q1.LPIA
EGY
2,014
1.21674
HDX
2026-04-04
ICTSKILLONLSFT.F.LPIA
EGY
2,017
0.444444
HDX
2026-04-04
CR.1.URB
EGY
2,021
97.457832
HDX
2026-04-04
ROFST.H.1.URB.M.WPIA
EGY
2,005
1.92939
HDX
2026-04-04
ICTSKILLEPRS.M.LPIA
EGY
2,014
0.571429
HDX
2026-04-04
ICTSKILLONLCNS.URB.M
EGY
2,018
1.7
HDX
2026-04-04
ICTSKILLONLSFT.AG25T74.F
EGY
2,022
2.2
HDX
2026-04-04
NARA.AGM1.RUR.Q1
EGY
2,014
55.103939
HDX
2026-04-04
EA.5T8.AG25T99.URB.M
EGY
2,009
24.947346
HDX
2026-04-04
ICTSKILLSOFT.M.LPIA
EGY
2,015
0.275862
HDX
2026-04-04
ROFST.H.3.Q2.LPIA
EGY
2,005
0.82011
HDX
2026-04-04
CR.1.RUR.Q4.F
EGY
2,009
97.795723
HDX
2026-04-04
TRTP.2T3.M
EGY
2,018
79.85817
HDX
2026-04-04
EA.1T8.AG25T99
EGY
2,013
53.768093
HDX
2026-04-04
ICTSKILLHLTHINF.AG15T24.M
EGY
2,020
28.6
HDX
2026-04-04
CR.1.RUR
EGY
2,022
95.75
HDX
2026-04-04
ICTSKILLINTBNK.AG15T24.F
EGY
2,016
0.1
HDX
2026-04-04
EA.3T8.AG25T99.URB.F
EGY
2,013
49.492016
HDX
2026-04-04
NARA.AGM1.RUR.Q2.M
EGY
2,005
48.592911
HDX
2026-04-04
ICTSKILLONLCNS.AGUNDER15Y.F
EGY
2,014
0.2
HDX
2026-04-04
PRYA.12MO.AG25T54.M
EGY
2,014
0.522625
HDX
2026-04-04
PRYA.12MO.AG25T54.F
EGY
2,010
0.254255
HDX
2026-04-04
NARA.AGM1.Q1.M
EGY
2,005
41.014019
HDX
2026-04-04
NARA.AGM1.Q2.F
EGY
2,009
47.093391
HDX
2026-04-04
ROFST.1.M.CP
EGY
2,019
6.23883
HDX
2026-04-04
ICTSKILLVOIP.RUR.M
EGY
2,022
48.2
HDX
2026-04-04
ICTSKILLHLTHINF.URB
EGY
2,020
39.1
HDX
2026-04-04
ICTSKILLONLCNS.AG15T24.M
EGY
2,016
0.2
HDX
2026-04-04
ROFST.H.3.Q5.LPIA
EGY
2,005
1.14233
HDX
2026-04-04
ROFST.H.1.URB.F
EGY
2,009
3.1969
HDX
2026-04-04
ICTSKILLINTBNK.RUR.GPIA
EGY
2,022
0.522727
HDX
2026-04-04
NARA.AGM1.URB.Q1.GPIA
EGY
2,009
0.91929
HDX
2026-04-04
ROFST.MOD.1.M
EGY
2,017
8.9
HDX
2026-04-04
ROFST.H.1.RUR.Q3
EGY
2,009
2.0861
HDX
2026-04-04
CR.2.RUR.Q3
EGY
2,021
90.56031
HDX
2026-04-04
LR.AG65T99
EGY
2,017
32.91
HDX
2026-04-04
ICTSKILLSOFT.AG25T74.M
EGY
2,014
0.7
HDX
2026-04-04
CR.2.RUR.F
EGY
2,015
80.8
HDX
2026-04-04
ADMI.ENDOFLOWERSEC.READ
EGY
2,022
0
HDX
2026-04-04
ICTSKILLFONLCRS.M.LPIA
EGY
2,020
0.50505
HDX
2026-04-04
EA.3T8.AG25T99.URB
EGY
2,013
53.52442
HDX
2026-04-04
LR.AG65T99.F.LPIA
EGY
2,005
0.28
HDX
2026-04-04
EA.S1T8.AG25T99.RUR
EGY
2,021
70.748817
HDX
2026-04-04
CR.2.Q3.F
EGY
2,009
76.041847
HDX
2026-04-04
ROFST.MOD.2.GPIA
EGY
2,019
1.040541
HDX
2026-04-04
EA.1T8.AG25T99.Q1.F
EGY
2,021
33.646729
HDX
2026-04-04
CR.1.Q2.LPIA
EGY
2,021
1.05675
HDX
2026-04-04
ROFST.MOD.2.GPIA
EGY
2,017
1.023529
HDX
2026-04-04
CR.MOD.1.M
EGY
2,015
91.745354
HDX
2026-04-04
EA.1T8.AG25T99.GPIA
EGY
2,016
0.776207
HDX
2026-04-04
CR.1.RUR.Q2
EGY
2,009
83.315521
HDX
2026-04-04
CR.2.RUR.Q5.M
EGY
2,009
95.763893
HDX
2026-04-04
ICTSKILLHLTHINF.RUR.M
EGY
2,017
13.9
HDX
2026-04-04
ROFST.MOD.1.F
EGY
2,007
24.5
HDX
2026-04-04
XGDP.FSGOV.FFNTR
EGY
2,006
3.869153
HDX
2026-04-04
CR.3.URB.Q2.GPIA
EGY
2,021
0.94312
HDX
2026-04-04
ICTSKILLDLDONLD.AG25T74
EGY
2,015
2.9
HDX
2026-04-04
ICTSKILLFONLCRS.GPIA
EGY
2,014
0.8
HDX
2026-04-04
ADMI.GRADE2OR3PRIM.MAT
EGY
2,024
1
HDX
2026-04-04
ICTSKILLGSINF.RUR.F
EGY
2,022
37.8
HDX
2026-04-04
ROFST.H.1.Q5
EGY
2,009
0.99521
HDX
2026-04-04
EA.5T8.AG25T99.RUR
EGY
2,015
8.407926
HDX
2026-04-04
ROFST.1T3.F.CP
EGY
2,019
14.5668
HDX
2026-04-04
PRYA.12MO.AG15T24.M
EGY
2,022
57.680459
HDX
2026-04-04
NARA.AGM1.Q4.F
EGY
2,005
51.000919
HDX
2026-04-04
ICTSKILLFONLCRS.URB.F
EGY
2,022
7.2
HDX
2026-04-04
NARA.AGM1.M.WPIA
EGY
2,005
0.67056
HDX
2026-04-04
LR.AG65T99.F
EGY
1,976
4.76
HDX
2026-04-04
CR.1.Q3.F
EGY
2,015
92.5
HDX
2026-04-04
SCHBSP.1.WINTERN
EGY
2,016
47.583193
HDX
2026-04-04
ICTSKILLONLCNS.M.LPIA
EGY
2,022
0.621622
HDX
2026-04-04
EA.S1T8.AG25T99.URB
EGY
2,020
81.995701
HDX
2026-04-04
End of preview. Expand in Data Studio

Egypt - Education Indicators

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


Abstract

Education indicators for Egypt.

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: EGY.

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


Dataset Characteristics

Domain Education
Unit of observation Country-level aggregates
Rows (total) 10,511
Columns 6 (2 numeric, 4 categorical, 0 datetime)
Train split 8,408 rows
Test split 2,102 rows
Geographic scope EGY
Publisher UNESCO
HDX last updated 2026-03-02

Variables

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

Outcome / Measurementvalue (range 0.0–21865568.0).

Identifier / Metadataindicator_id (GER.5T8, GER.5T8.M, GER.5T8.GPIA), esa_source (HDX), esa_processed (2026-04-04).


Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unesco-data-for-egypt")
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% GER.5T8, GER.5T8.M, GER.5T8.GPIA
country_id object 0.0% EGY
year int64 0.0% 1971.0 – 2025.0 (mean 2013.2051)
value float64 0.0% 0.0 – 21865568.0 (mean 24857.0103)
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 2013.2051 2015.0
value 0.0 21865568.0 24857.0103 6.4512

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_egypt,
  title     = {Egypt - Education Indicators},
  author    = {UNESCO},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/unesco-data-for-egypt},
  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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