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electricsheepafrica/africa-sierra-leone-slihs2018-consexp-csv-1890210d

Slihs2018 Consexp CSV | Africa (Statistics Sierra Leone) 6,810 rows - 1 Africa country/area - 2018 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 6,810 rows from Statistics Sierra Leone, covering Slihs2018 Consexp 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-sierra-leone-slihs2018-consexp-csv-1890210d.

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Slihs2018 Consexp CSV | Africa (Statistics Sierra Leone)

6,810 rows - 1 Africa country/area - 2018 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 6,810 rows from Statistics Sierra Leone, covering Slihs2018 Consexp 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 Consexp 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

DimensionValue
Rows6,810
Countries/areas1
First period2018
Last period2018
Indicators0
Columns57
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
SL6,81020182018Sierra Leone

Indicators, Variables, Or Resource Contents

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

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.res-2351692470590047350:0
country_iso3dictionary<values=string, indices=int8, ordered=0>ISO3 country or area code.SL
country_namedictionary<values=string, indices=int8, ordered=0>Country or area name.Sierra Leone
yearint64Observation year.2018
clusterint64Source column from the original resource.19
hhnoint64Source column from the original resource.24
regionstringSource column from the original resource.East
provincestringSource column from the original resource.East
districtstringSource column from the original resource.Kailahun
stratumint64Source column from the original resource.112
wta_hhdoubleSource column from the original resource.102.39156
wta_popdoubleSource column from the original resource.307.17468
monthint64Source column from the original resource.1
hhsizeint64Source column from the original resource.3
hhadeqdoubleSource column from the original resource.2.519999980926514
hhnchildint64Source column from the original resource.0
hhhageint64Source column from the original resource.44
hhhsexint64Source column from the original resource.1
hhhedustringSource column from the original resource.secondary incomplete
hhhempstringSource column from the original resource.Transportation and storage
foodexpdoubleSource column from the original resource.7013.349975585938
foodowndoubleSource column from the original resource.0.0
foodgiftdoubleSource column from the original resource.592.0
hltheducdoubleSource column from the original resource.1030.0
purchasedoubleSource column from the original resource.5796.75
housingdoubleSource column from the original resource.287.22998046875
durablesdoubleSource column from the original resource.1877.218769550323
consexpdoubleSource column from the original resource.16596.549
rururbstringSource column from the original resource.urban
areastringSource column from the original resource.E urban
marketstringSource column from the original resource.Kenema City
sectorstringSource column from the original resource.other urban
fdpi_spatialdoubleSource column from the original resource.0.99647206
nfpi_spatialdoubleSource column from the original resource.1.0301874
pi_temporalrdoubleSource column from the original resource.0.92683643
welfare_fooddoubleSource column from the original resource.3267.7622
welfare_nonfdoubleSource column from the original resource.3736.7817
welfaredoubleSource column from the original resource.7004.5439
hhexpdoubleSource column from the original resource.17651.451
pchhexpdoubleSource column from the original resource.5883.8169
poor_extint64Source column from the original resource.0
poor_foodint64Source column from the original resource.0
poor_absint64Source column from the original resource.0
poor_intint64Source column from the original resource.0
decileint64Source column from the original resource.9
quintileint64Source column from the original resource.5
source_period_start_yearint64Start year inferred from source metadata.2018
source_period_end_yearint64End year inferred from source metadata.2018
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2018
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.Statistics Sierra Leone
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.slihs2018_consexp.csv
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.slihs2018_consexp.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.stats-sl-slihs2018-consexp-csv
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.res-2351692470590047350
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://www.statistics.sl/images/StatisticsSL/Documents/SLIHS2018/SLI...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.other
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-08-09T11:46:10Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sierra-leone-slihs2018-consexp-csv-1890210d")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

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

Time-Series Pattern

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

Pivot For Analysis

python
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

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

bibtex
@misc{electric_sheep_africa_africa_sierra_leone_slihs2018_consexp_csv_1890210d_2018,
  title        = {Slihs2018 Consexp 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-consexp-csv-1890210d}}
}

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