electricsheepafrica/africa-learning-levels-in-kenya-2014

Learning levels in Kenya (2014)

Publisher: Uwezo at Twaweza East Africa (inactive) · Source: HDX · License: cc-by · Updated: 2025-02-19

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Abstract

These datasets contains data relevant to learner achievement in Kenya. This ranges from school enrollment, socioeconomic indicators, school infrastructure among others.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the date column(s). Geographic scope: KEN.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Subnational administrative unit observations
Rows (total)	4,377
Columns	218 (201 numeric, 16 categorical, 1 datetime)
Train split	3,501 rows
Test split	875 rows
Geographic scope	KEN
Publisher	Uwezo at Twaweza East Africa (inactive)
HDX last updated	2025-02-19

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Variables

Geographic — id_districtname (nyeri south, vihiga, laikipia east), id_district (range 1.0–200.0), headteacher_sex (range 1.0–2.0), q408_community, q706_boy and 11 others.

Temporal — date.

Demographic — id_village (range 1.0–4742.0), persondesignation (HEADTEACHER, HEAD TEACHER, DEPUTY HEADTEACHER), q301_male, q301_female, q302_male and 23 others.

Identifier / Metadata — id_database (KE14), q500_video, prova_code, prova_name (RIFT VALLEY, EASTERN, NYANZA), division_name and 2 others.

Other — use_telephone (range 1.0–2.0), q101_a_1 (range 1.0–1.0), q101_b_std1 (range 1.0–10.0), q101_b_std2 (range 1.0–9.0), q101_b_std3 (range 1.0–6.0) and 161 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-learning-levels-in-kenya-2014")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
id_village	int64	0.0%	1.0 – 4742.0 (mean 2397.0877)
id_districtname	object	0.0%	nyeri south, vihiga, laikipia east
id_district	int64	0.0%	1.0 – 200.0 (mean 104.0299)
id_database	object	0.0%	KE14
persondesignation	object	6.0%	HEADTEACHER, HEAD TEACHER, DEPUTY HEADTEACHER
headteacher_sex	float64	8.5%	1.0 – 2.0 (mean 1.1842)
use_telephone	float64	18.6%	1.0 – 2.0 (mean 1.0317)
date	datetime64[ns]	7.9%
q101_a_1	float64	6.0%	1.0 – 1.0 (mean 1.0)
q101_b_std1	float64	3.9%	1.0 – 10.0 (mean 1.4478)
q101_b_std2	float64	4.2%	1.0 – 9.0 (mean 1.4624)
q101_b_std3	float64	4.8%	1.0 – 6.0 (mean 1.4826)
q101_b_std4	float64	5.3%	1.0 – 10.0 (mean 1.5516)
q101_b_std5	float64	6.0%	1.0 – 9.0 (mean 1.5546)
q101_b_std6	float64	6.9%	1.0 – 9.0 (mean 1.5786)
q101_b_std7	float64	8.0%	1.0 – 10.0 (mean 1.6348)
q101_b_std8	float64	9.0%	1.0 – 10.0 (mean 1.4989)
q201a_b_std1	float64	5.0%	0.0 – 228.0 (mean 25.4463)
q201a_b_std2	float64	5.0%	1.0 – 137.0 (mean 24.8258)
q201a_b_std3	float64	5.2%	0.0 – 267.0 (mean 25.4211)
q201a_b_std4	float64	5.9%	0.0 – 226.0 (mean 26.2427)
q201a_b_std5	float64	6.5%	0.0 – 233.0 (mean 25.654)
q201a_b_std6	float64	7.5%	0.0 – 298.0 (mean 25.1761)
q201a_b_std7	float64	8.9%	0.0 – 288.0 (mean 25.3161)
q201a_b_std8	float64	10.0%
q201a_b_std_specunit	float64	20.0%
q201a_g_std1	float64	4.8%
q201a_g_std2	float64	4.9%
q201a_g_std3	float64	5.0%
q201a_g_std4	float64	5.8%
q201a_g_std5	float64	6.4%
q201a_g_std6	float64	7.3%
q201a_g_std7	float64	8.8%
q201a_g_std8	float64	10.0%
q201a_g_std_specunit	float64	20.9%
q201b_b_std1	float64	74.6%
q201b_b_std2	float64	73.7%
q201b_b_std3	float64	73.7%
q201b_b_std4	float64	71.2%
q201b_b_std5	float64	71.4%
q201b_b_std6	float64	71.3%
q201b_b_std7	float64	70.4%
q201b_b_std8	float64	77.6%
q201b_b_specunit	float64	73.6%
q201b_g_std1	float64	74.8%
q201b_g_std2	float64	74.0%
q201b_g_std3	float64	74.0%
q201b_g_std4	float64	71.4%
q201b_g_std5	float64	71.4%
q201b_g_std6	float64	71.3%
q201b_g_std7	float64	70.5%
q201b_g_std8	float64	77.8%
q201b_g_std_specunit	float64	73.9%
q301_tot	float64	3.8%
q301_male	float64	3.9%
q301_female	float64	5.9%
q302_tot	float64	5.2%
q302_male	float64	41.2%
q302_female	float64	8.5%
q303_tot	float64	4.8%
q303_male	float64	10.7%
q303_female	float64	10.0%
q304_tot	float64	23.7%
q304_male	float64	35.9%
q304_female	float64	33.4%
q305_tot	float64	6.2%
q305_male	float64	9.3%
q305_female	float64	18.2%
q306_tr_male	float64	5.1%
q306_tr_female	float64	8.8%
q306_untr_male	float64	54.3%
q306_untr_female	float64	56.3%
q307_tr_male	float64	34.1%
q307_tr_female	float64	35.9%
q307_untr_male	float64	49.8%
q307_untr_female	float64	53.2%
q308_tr_male	float64	52.8%
q308_tr_female	float64	36.5%
q308_untr_male	float64	63.1%
q308_untr_female	float64	61.8%
q401	float64	39.9%
q402	float64	44.1%
q403	float64	44.1%
q404	float64	45.7%
q405	float64	5.1%
q406	float64	6.4%
q407	float64	31.3%
q408_pupil	float64	9.9%
q408_teacher	float64	15.5%
q408_community	float64	16.5%
q409	float64	6.1%
q410	float64	4.1%
q500_tv	float64	52.6%
q500_radio	float64	26.2%
q500_lcd	float64	57.8%
q500_mobile	float64	53.8%
q500_video	float64	58.2%
q500_dvd	float64	56.7%
q501	float64	4.0%
q502	float64	4.5%
q601	float64	8.3%
q602	float64	3.1%
q603	float64	2.7%
q604	float64	3.7%
q605	float64	49.5%
q606	float64	67.8%
q701	float64	4.1%
q702	float64	4.7%
q703	float64	55.4%
q704	float64	43.0%
q705	float64	9.3%
q706_boy	float64	5.6%
q706_girl	float64	5.7%
q707_boy	float64	44.3%
q707_girl	float64	44.5%
q708	float64	10.8%
q801	float64	5.1%
q802	float64	3.5%
q803	float64	3.3%
q804	float64	3.0%
q805	float64	3.8%
q806	object	63.8%	WILD ANIMALS, LIGHTENING, INSECURITY
q901_year	float64	27.3%
q901_sex	float64	24.5%
q902	float64	39.5%
delete	int64	0.0%
addedby	object	0.0%	DEO36, DEO23, DEO22
updatedby	object	0.2%	DEO36, DEO22, DEO23
s903	float64	28.0%
s904	float64	25.6%
s905	float64	27.5%
s906	float64	25.6%
s907	float64	32.9%
s908	object	33.4%	KEMI, MINISTRY OF EDUCATION, kemi
s1000_stream	object	25.8%	2, 1, 2A
s1000_sex	float64	24.5%
s1000_boy	float64	10.3%
s1000_girl	float64	10.0%
s1001	float64	2.4%
s1002	float64	2.3%
s1003	float64	2.6%
s1004	float64	2.0%
s1005	float64	2.3%
s1006	float64	4.5%
s1007	float64	3.6%
s1008	float64	2.4%
s1100_1	float64	20.5%
s1100_2	float64	6.7%
s1100_3	float64	6.9%
s1100_4	float64	7.0%
email	object	65.1%	1, [email protected], [email protected]
q102_pre_sch	float64	10.0%
s1201a_b_std1	float64	4.4%
s1201a_b_std2	float64	3.8%
s1201a_b_std3	float64	4.6%
s1201a_b_std4	float64	5.3%
s1201a_b_std5	float64	5.7%
s1201a_b_std6	float64	7.0%
s1201a_b_std7	float64	8.2%
s1201a_b_std8	float64	9.8%
s1201a_g_std1	float64	4.4%
s1201a_g_std2	float64	3.6%
s1201a_g_std3	float64	4.6%
s1201a_g_std4	float64	5.2%
s1201a_g_std5	float64	5.8%
s1201a_g_std6	float64	6.9%
s1201a_g_std7	float64	8.0%
s1201a_g_std8	float64	10.0%
s1201b_b_std1	float64	72.1%
s1201b_b_std2	float64	70.9%
s1201b_b_std3	float64	70.7%
s1201b_b_std4	float64	67.3%
s1201b_b_std5	float64	67.4%
s1201b_b_std6	float64	67.1%
s1201b_b_std7	float64	65.8%
s1201b_b_std8	float64	74.3%
s1201b_g_std1	float64	72.5%
s1201b_g_std2	float64	71.2%
s1201b_g_std3	float64	70.9%
s1201b_g_std4	float64	67.6%
s1201b_g_std5	float64	67.6%
s1201b_g_std6	float64	67.2%
s1201b_g_std7	float64	66.1%
s1201b_g_std8	float64	74.6%
s1202a_std1	float64	31.0%
s1202a_std2	float64	28.0%
s1202a_std3	float64	34.3%
s1202a_std4	float64	37.3%
s1202a_std5	float64	37.1%
s1202a_std6	float64	38.1%
s1202a_std7	float64	37.8%
s1202a_std8	float64	37.8%
s1202b_std1	float64	78.4%
s1202b_std2	float64	77.8%
s1202b_std3	float64	78.3%
s1202b_std4	float64	77.8%
s1202b_std5	float64	78.3%
s1202b_std6	float64	78.0%
s1202b_std7	float64	77.3%
s1301	float64	13.7%
s1302	float64	1.9%
s1303	float64	2.1%
s1304	float64	1.7%
s1305	float64	2.1%
s1306_female	float64	4.5%
s1306_male	float64	5.1%
s1307_female	float64	45.0%
s1307_male	float64	46.2%
s1308	float64	4.0%
prova_code	int64	0.0%
prova_name	object	0.0%	RIFT VALLEY, EASTERN, NYANZA
division_name	object	0.0%
location	object	0.0%
sub_location	object	0.0%
county	object	0.0%
district_nohhlds	int64	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id_village	1.0	4742.0	2397.0877	2429.0
id_district	1.0	200.0	104.0299	121.0
headteacher_sex	1.0	2.0	1.1842	1.0
use_telephone	1.0	2.0	1.0317	1.0
q101_a_1	1.0	1.0	1.0	1.0
q101_b_std1	1.0	10.0	1.4478	1.0
q101_b_std2	1.0	9.0	1.4624	1.0
q101_b_std3	1.0	6.0	1.4826	1.0
q101_b_std4	1.0	10.0	1.5516	1.0
q101_b_std5	1.0	9.0	1.5546	1.0
q101_b_std6	1.0	9.0	1.5786	1.0
q101_b_std7	1.0	10.0	1.6348	1.0
q101_b_std8	1.0	10.0	1.4989	1.0
q201a_b_std1	0.0	228.0	25.4463	22.0
q201a_b_std2	1.0	137.0	24.8258	22.0

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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. 215 column(s) with >80% missing values were removed: q101_a_2, q101_a_3, q101_a_4, q101_a_5, q101_a_6, q101_a_7.... 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Uwezo at Twaweza East Africa (inactive) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: q201a_g_std_specunit, q201b_b_std1, q201b_b_std2, q201b_b_std3, q201b_b_std4, q201b_b_std5, q201b_b_std6, q201b_b_std7....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_learning_levels_in_kenya_2014,
  title     = {Learning levels in Kenya (2014)},
  author    = {Uwezo at Twaweza East Africa (inactive)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/learning-levels-in-kenya-2014},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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