electricsheepafrica/africa-sierra-leone-slihs2018-o-csv-864d788c

Slihs2018 O CSV | Africa (Statistics Sierra Leone)

21,548 rows - 1 Africa country/area - 2003-2018 - 32 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 21,548 rows from Statistics Sierra Leone, covering Slihs2018 O 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 O 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 indicator observation for one geography, time period, and optional source dimensions.
• —Primary geography column: country_iso3.
• —Best time column: year.
• —Time coverage basis: year.
• —Recommended join keys: country_iso3, year, indicator_id.

Coverage

Geographic Coverage

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

Indicators, Variables, Or Resource Contents

• —slihs2018-o-csv-cluster-259211b8 - slihs2018o.csv - cluster(sourceunits_unspecified)
• —slihs2018-o-csv-hhno-05d0a150 - slihs2018o.csv - hhno(sourceunits_unspecified)
• —slihs2018-o-csv-line-6007ce29 - slihs2018o.csv - line(sourceunits_unspecified)
• —slihs2018-o-csv-stratum-40ac966c - slihs2018o.csv - stratum(sourceunits_unspecified)
• —slihs2018-o-csv-pweight-fc7f0e4d - slihs2018o.csv - pweight(sourceunits_unspecified)
• —slihs2018-o-csv-b2-serial-160cce06 - slihs2018o.csv - b2 serial(sourceunits_unspecified)
• —slihs2018-o-csv-o3-4867f430 - slihs2018o.csv - o3(sourceunits_unspecified)
• —slihs2018-o-csv-o4-0b62245c - slihs2018o.csv - o4(sourceunits_unspecified)
• —slihs2018-o-csv-o6-4c7b099c - slihs2018o.csv - o6(sourceunits_unspecified)
• —slihs2018-o-csv-o9-9c03eb9a - slihs2018o.csv - o9(sourceunits_unspecified)
• —slihs2018-o-csv-o10a-371140f9 - slihs2018o.csv - o10a(sourceunits_unspecified)
• —slihs2018-o-csv-o10b-2aae000c - slihs2018o.csv - o10b(sourceunits_unspecified)
• —slihs2018-o-csv-o12-11a05289 - slihs2018o.csv - o12(sourceunits_unspecified)
• —slihs2018-o-csv-o13-2d862a97 - slihs2018o.csv - o13(sourceunits_unspecified)
• —slihs2018-o-csv-o16-84a6d1c0 - slihs2018o.csv - o16(sourceunits_unspecified)
• —slihs2018-o-csv-o17-ebb1faf1 - slihs2018o.csv - o17(sourceunits_unspecified)
• —slihs2018-o-csv-o24-d829ddd0 - slihs2018o.csv - o24(sourceunits_unspecified)
• —slihs2018-o-csv-o25-467bf20f - slihs2018o.csv - o25(sourceunits_unspecified)
• —slihs2018-o-csv-o26a-b19df585 - slihs2018o.csv - o26a(sourceunits_unspecified)
• —slihs2018-o-csv-o27-b6b83a4e - slihs2018o.csv - o27(sourceunits_unspecified)
• —... 12 more indicators

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sierra-leone-slihs2018-o-csv-864d788c")
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_o.csv
• —License: Source-specific or other license
• —Retrieved/generated: 2026-08-09T11:47:52Z
• —Hugging Face repo: electricsheepafrica/africa-sierra-leone-slihs2018-o-csv-864d788c

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
• —Pivot to geography x period or indicator x period matrices
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_sierra_leone_slihs2018_o_csv_864d788c_2018,
  title        = {Slihs2018 O 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-o-csv-864d788c}}
}

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