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 |
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
Geographic — country_id (COG), year (range 1970.0–2025.0).
Outcome / Measurement — value (range 0.0–8900204.0).
Identifier / Metadata — indicator_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.
- Downloads last month
- 36