electricsheepafrica/africa-zambia-cfs-2022-2023-53eeb8c8
Cfs 2022 2023 | Africa (Zambia Statistics Agency) 4,304 rows - 1 Africa country/area - 2022-2023 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 4,304 rows from Zambia Statistics Agency, covering Cfs 2022 2023. 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-zambia-cfs-2022-2023-53eeb8c8.
Cfs 2022 2023 | Africa (Zambia Statistics Agency)
4,304 rows - 1 Africa country/area - 2022-2023 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
TL;DR
This dataset contains 4,304 rows from Zambia Statistics Agency, covering Cfs 2022 2023. 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.
Source-provided context: CFS 2022_2023
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:
not detected. - Time coverage basis: source metadata.
- Recommended join keys:
country_iso3where available plus source-specific keys.
Coverage
Geographic Coverage
Top areas shown below, sorted by row count when available:
Indicators, Variables, Or Resource Contents
- This repo preserves one source tabular resource with its usable columns kept together.
Schema
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-zambia-cfs-2022-2023-53eeb8c8")
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"] == "ZMB"]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
- No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
- 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: Zambia Statistics Agency
- Publisher: Zambia Statistics Agency
- Portal: https://www.zamstats.gov.zm
- Resource: CFS 2022_2023
- License: other-open
- Retrieved/generated:
2026-07-22T09:45:30Z - Hugging Face repo: electricsheepafrica/africa-zambia-cfs-2022-2023-53eeb8c8
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
- Check missingness before modeling
- Use
country_iso3as the safest geography join key when present
Citation
@misc{electric_sheep_africa_africa_zambia_cfs_2022_2023_53eeb8c8_2023,
title = {Cfs 2022 2023 | Africa (Zambia Statistics Agency)},
author = {Zambia Statistics Agency},
year = {2023},
url = {https://www.zamstats.gov.zm/cfs-2022_2023/},
publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-zambia-cfs-2022-2023-53eeb8c8}}
}License
Released under other-open.
Original data is published by Zambia Statistics Agency. 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.zamstats.gov.zm/cfs-2022_2023/
