electricsheepafrica/africa-kenya-2021-kenya-time-use-survey-8f94f8e5

2021 Kenya Time Use Survey | Africa (Kenya National Bureau of Statistics)

22 rows - 1 Africa country/area - 2021 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 22 rows from Kenya National Bureau of Statistics, covering 2021 Kenya Time Use Survey. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

Source-provided context: Document, Questionnaire [doc/qst]

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	22
Countries/areas	1
First period	2021
Last period	2021
Indicators	0
Columns	33
Source format	XLSX

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
KEN	22	2021	2021	Kenya

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	knbs-nada-127-765:sheet1:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	KEN
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Kenya
source_sheet	string	Source column from the original resource.	Sheet1
year	int64	Observation year.	2021
t02	string	Source column from the original resource.	ACTIVITY Day of the week for which activities recorded: (This is the ...
column_2	double	Source column from the original resource.	``
t01	string	Source column from the original resource.	Time period
t03	string	Source column from the original resource.	What were you doing yesterday morning between 3 a.m and 4 a.m?
t04	string	Source column from the original resource.	Did you do the activities at the same time?/Did you do anything else?
t05	string	Source column from the original resource.	How long did the activity take?
t06	string	Source column from the original resource.	Whom did you do this for?
column_8	double	Source column from the original resource.	``
t07	string	Source column from the original resource.	Did you do the activities MAINLY for payment or for profit?
t08	string	Source column from the original resource.	Please clasify the activity described in T03
column_11	double	Source column from the original resource.	``
t09	string	Source column from the original resource.	Who was with you?
column_13	double	Source column from the original resource.	``
t10	string	Source column from the original resource.	Where were you when you did the activity?
column_15	double	Source column from the original resource.	``
t11	string	Source column from the original resource.	Activity Code of the Main Activity in T03
t12	string	Source column from the original resource.	If Yes in T04, enter the code of the simultaneous activities (At most...
source_period_start_year	int64	Start year inferred from source metadata.	2021
source_period_end_year	int64	End year inferred from source metadata.	2021
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2021
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	Kenya National Bureau of Statistics
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	2021 Kenya Time Use Survey
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Kenya Time Use Questionnaire
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	DDI-KEN-KNBS-KTUS-2021-V001
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	knbs-nada-127-765
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://statistics.knbs.or.ke/nada/index.php/catalog/127/download/765
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	other-open
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-07T16:10:13Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-kenya-2021-kenya-time-use-survey-8f94f8e5")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "KEN"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: Kenya National Bureau of Statistics
• Publisher: Kenya National Bureau of Statistics
• Portal: https://statistics.knbs.or.ke/nada
• Resource: Kenya Time Use Questionnaire
• License: other-open
• Retrieved/generated: 2026-08-07T16:10:14Z
• Hugging Face repo: electricsheepafrica/africa-kenya-2021-kenya-time-use-survey-8f94f8e5

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Track mobility over time
• Compare routes or geographies
• Join with economic and population data
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_kenya_2021_kenya_time_use_survey_8f94f8e5_2021,
  title        = {2021 Kenya Time Use Survey | Africa (Kenya National Bureau of Statistics)},
  author       = {Kenya National Bureau of Statistics},
  year         = {2021},
  url          = {https://statistics.knbs.or.ke/nada/index.php/catalog/127/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-kenya-2021-kenya-time-use-survey-8f94f8e5}}
}

License

Released under other-open.

Original data is published by Kenya National Bureau of Statistics. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://statistics.knbs.or.ke/nada/index.php/catalog/127/related-materials