electricsheepafrica/africa-sierra-leone-slihs2018-u-csv-a14a4e36

Slihs2018 U CSV | Africa (Statistics Sierra Leone)

47,817 rows - 1 Africa country/area - 2018 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 47,817 rows from Statistics Sierra Leone, covering Slihs2018 U CSV. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

This dataset covers Slihs2018 U CSV from Statistics Sierra Leone. Use the source and schema sections below to confirm definitions, units, and collection methodology before sensitive analytical use.

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	47,817
Countries/areas	1
First period	2018
Last period	2018
Indicators	0
Columns	35
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
SL	47,817	2018	2018	Sierra Leone

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.	res-3257039981947020360:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	SL
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Sierra Leone
year	int64	Observation year.	2018
cluster	int64	Source column from the original resource.	1
hhno	int64	Source column from the original resource.	1
activity	int64	Source column from the original resource.	101
stratum	int64	Source column from the original resource.	111
pweight	double	Source column from the original resource.	247.86473
b3_serial	double	Source column from the original resource.	3543.0
u1	string	Source column from the original resource.	yes
u2a	double	Source column from the original resource.	2.0
u2b	double	Source column from the original resource.	4.0
u3	double	Source column from the original resource.	1.0
u4	string	Source column from the original resource.	communal bush
u4_other	string	Source column from the original resource.	``
u5	double	Source column from the original resource.	0.0
u6a	string	Source column from the original resource.	None sold
u6b	string	Source column from the original resource.	20
u7	double	Source column from the original resource.	``
u8a	string	Source column from the original resource.	Yes, do activity
u8b	string	Source column from the original resource.	Yes, do activity
u8c	string	Source column from the original resource.	Yes, do activity
u8d	string	Source column from the original resource.	Yes, do activity
source_period_start_year	int64	Start year inferred from source metadata.	2018
source_period_end_year	int64	End year inferred from source metadata.	2018
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2018
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	Statistics Sierra Leone
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	slihs2018_u.csv
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	slihs2018_u.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	stats-sl-slihs2018-u-csv
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	res-3257039981947020360
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://www.statistics.sl/images/StatisticsSL/Documents/SLIHS2018/SLI...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	other
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-09T11:46:10Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sierra-leone-slihs2018-u-csv-a14a4e36")
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"] == "SL"]

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: Statistics Sierra Leone
• Publisher: Statistics Sierra Leone
• Portal: https://www.statistics.sl
• Resource: slihs2018_u.csv
• License: Source-specific or other license
• Retrieved/generated: 2026-08-09T11:50:42Z
• Hugging Face repo: electricsheepafrica/africa-sierra-leone-slihs2018-u-csv-a14a4e36

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

• Profile the distribution of values
• Compare categories or geographies
• Join with complementary public datasets
• 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_sierra_leone_slihs2018_u_csv_a14a4e36_2018,
  title        = {Slihs2018 U CSV | Africa (Statistics Sierra Leone)},
  author       = {Statistics Sierra Leone},
  year         = {2018},
  url          = {https://www.statistics.sl},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-sierra-leone-slihs2018-u-csv-a14a4e36}}
}

License

Released under Source-specific or other license.

Original data is published by Statistics Sierra Leone. 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.

---

Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://www.statistics.sl