electricsheepafrica/africa-nigeria-sectorial-distribution-of-value-added-tax-95ea2c3d

Sectorial Distribution of Value Added Tax | Africa (National Bureau of Statistics, Nigeria)

34 rows - 1 Africa country/area - 2026 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 34 rows from National Bureau of Statistics, Nigeria, covering Sectorial Distribution of Value Added Tax. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.

Source-provided context: Document, Report [doc/rep]

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	34
Countries/areas	1
First period	2026
Last period	2026
Indicators	0
Columns	49
Source format	ZIP

Geographic Coverage

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

Area	Rows	First year	Last year	Name
NGA	34	2026	2026	Nigeria

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.	nbs-nada-144-1421:0
country_iso3	string	ISO3 country or area code.	NGA
country_name	string	Country or area name.	Nigeria
year	int64	Observation year.	2026
s_no	double	Source column from the original resource.	``
classification	string	Source column from the original resource.	``
value_added_tax	double	Source column from the original resource.	``
value_added_tax_2	double	Source column from the original resource.	``
s_no_2	double	Source column from the original resource.	``
classification_2	string	Source column from the original resource.	``
vat	double	Source column from the original resource.	``
vat_2	double	Source column from the original resource.	``
s_no_3	double	Source column from the original resource.	``
classification_3	string	Source column from the original resource.	``
vat_3	double	Source column from the original resource.	``
vat_4	double	Source column from the original resource.	``
vat_5	double	Source column from the original resource.	``
vat_6	double	Source column from the original resource.	``
total	double	Source column from the original resource.	``
vat_7	double	Source column from the original resource.	``
vat_8	double	Source column from the original resource.	``
vat_9	double	Source column from the original resource.	``
vat_10	double	Source column from the original resource.	``
total_2	double	Source column from the original resource.	``
vat_11	double	Source column from the original resource.	``
vat_12	double	Source column from the original resource.	``
vat_13	double	Source column from the original resource.	``
vat_14	double	Source column from the original resource.	``
total_3	double	Source column from the original resource.	``
vat_15	double	Source column from the original resource.	``
vat_16	double	Source column from the original resource.	``
vat_17	double	Source column from the original resource.	``
vat_18	double	Source column from the original resource.	``
total_4	double	Source column from the original resource.	``
vat_19	double	Source column from the original resource.	``
qonq	double	Source column from the original resource.	``
yony	double	Source column from the original resource.	``
share	double	Source column from the original resource.	``
source_period_start_year	int64	Start year inferred from source metadata.	2026
source_period_end_year	int64	End year inferred from source metadata.	2026
source_period_label	string	Source column from the original resource.	2026
source_provider	string	Publishing organization.	National Bureau of Statistics, Nigeria
source_dataset	string	Source dataset or package title.	Sectorial Distribution of Value Added Tax
source_resource	string	Source resource title, table name, or file name.	Q1 2026 Value Added Tax Report
source_package_id	string	Source package identifier.	NGA-NBS-VAT
source_resource_id	string	Source resource identifier.	nbs-nada-144-1421
source_url	string	Original source URL or download URL.	https://microdata.nigerianstat.gov.ng/index.php/catalog/144/download/...
license_id	string	Source license identifier.	other-open
retrieved_at	string	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-07-19T04:13:01Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-sectorial-distribution-of-value-added-tax-95ea2c3d")
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"] == "NGA"]

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: National Bureau of Statistics, Nigeria
• Publisher: National Bureau of Statistics, Nigeria
• Portal: https://microdata.nigerianstat.gov.ng
• Resource: Q1 2026 Value Added Tax Report
• License: other-open
• Retrieved/generated: 2026-07-19T04:23:19Z
• Hugging Face repo: electricsheepafrica/africa-nigeria-sectorial-distribution-of-value-added-tax-95ea2c3d

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

• Build time-series dashboards
• Compare economic indicators
• Join with population or sector 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_nigeria_sectorial_distribution_of_value_added_tax_95ea2c3d_2026,
  title        = {Sectorial Distribution of Value Added Tax | Africa (National Bureau of Statistics, Nigeria)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2026},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/144/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-sectorial-distribution-of-value-added-tax-95ea2c3d}}
}

License

Released under other-open.

Original data is published by National Bureau of Statistics, Nigeria. 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 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://microdata.nigerianstat.gov.ng/index.php/catalog/144/related-materials