Datasetpublic

electricsheepafrica/africa-nigeria-postal-services-data-d218ed0f

Postal Services Data | Africa (National Bureau of Statistics, Nigeria) 292 rows - 1 Africa country/area - 2023-2025 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 292 rows from National Bureau of Statistics, Nigeria, covering Postal Services Data. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-postal-services-data-d218ed0f.

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Dataset Card

Postal Services Data | Africa (National Bureau of Statistics, Nigeria)

292 rows - 1 Africa country/area - 2023-2025 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 292 rows from National Bureau of Statistics, Nigeria, covering Postal Services Data. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

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: not detected.
—Time coverage basis: source metadata.
—Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	292
Countries/areas	1
First period	2023
Last period	2025
Indicators	0
Columns	94
Source format	ZIP

Geographic Coverage

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

Area	Rows	First year	Last year	Name
`NGA`	292	2023	2025	`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-183-1419:postal-data-2019-2023-xlsx-table:0`
`country_iso3`	`string`	ISO3 country or area code.	`NGA`
`country_name`	`string`	Country or area name.	`Nigeria`
`source_sheet`	`string`	Source column from the original resource.	`Postal Data 2019-2023.xlsx::TABLE 1`
`s_n`	`double`	Source column from the original resource.	`1.0`
`post_offices_postal_agencies`	`string`	Source column from the original resource.	`Branch Office (BO)`
`2019`	`double`	Source column from the original resource.	`58.0`
`2020`	`double`	Source column from the original resource.	`58.0`
`2021`	`double`	Source column from the original resource.	`58.0`
`2022`	`double`	Source column from the original resource.	`58.0`
`2023`	`double`	Source column from the original resource.	`58.0`
`source_period_start_year`	`int64`	Start year inferred from source metadata.	`2023`
`source_period_end_year`	`int64`	End year inferred from source metadata.	`2025`
`source_period_label`	`string`	Source column from the original resource.	`2023-2025`
`source_provider`	`string`	Publishing organization.	`National Bureau of Statistics, Nigeria`
`source_dataset`	`string`	Source dataset or package title.	`Postal Services Data`
`source_resource`	`string`	Source resource title, table name, or file name.	`Postal Service Data 2023-2025`
`source_package_id`	`string`	Source package identifier.	`NGA-NBS-POSTAL`
`source_resource_id`	`string`	Source resource identifier.	`nbs-nada-183-1419`
`source_url`	`string`	Original source URL or download URL.	`https://microdata.nigerianstat.gov.ng/index.php/catalog/183/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`
`territory`	`string`	Source column from the original resource.	``
`number_of_p_o_available`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented`	`double`	Source column from the original resource.	``
`number_of_p_o_private_rented`	`double`	Source column from the original resource.	``
`number_of_p_o_business_rented`	`double`	Source column from the original resource.	``
`number_of_p_o_not_rented`	`double`	Source column from the original resource.	``
`number_of_pmb_available`	`double`	Source column from the original resource.	``
`number_of_pmb_rented`	`double`	Source column from the original resource.	``
`number_of_pmb_not_rented`	`double`	Source column from the original resource.	``
`s_n_2`	`double`	Source column from the original resource.	``
`territory_2`	`string`	Source column from the original resource.	``
`number_of_p_o_available_2`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented_2`	`double`	Source column from the original resource.	``
`number_of_p_o_private_rented_2`	`double`	Source column from the original resource.	``
`number_of_p_o_business_rented_2`	`double`	Source column from the original resource.	``
`number_of_p_o_not_rented_2`	`double`	Source column from the original resource.	``
`number_of_pmb_available_2`	`double`	Source column from the original resource.	``
`number_of_pmb_rented_2`	`double`	Source column from the original resource.	``
`number_of_pmb_not_rented_2`	`double`	Source column from the original resource.	``
`s_n_3`	`double`	Source column from the original resource.	``
`territory_3`	`string`	Source column from the original resource.	``
`number_of_p_o_available_3`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented_3`	`double`	Source column from the original resource.	``
`number_of_p_o_private_rented_3`	`double`	Source column from the original resource.	``
`number_of_p_o_business_rented_3`	`double`	Source column from the original resource.	``
`number_of_p_o_not_rented_3`	`double`	Source column from the original resource.	``
`number_of_pmb_available_3`	`double`	Source column from the original resource.	``
`number_of_pmb_rented_3`	`double`	Source column from the original resource.	``
`number_of_pmb_not_rented_3`	`double`	Source column from the original resource.	``
`s_n_4`	`double`	Source column from the original resource.	``
`territory_4`	`string`	Source column from the original resource.	``
`total_number_of_boxes_installed`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented_4`	`double`	Source column from the original resource.	``
`number_of_p_o_private_rented_4`	`double`	Source column from the original resource.	``
`number_of_p_o_business_rented_4`	`double`	Source column from the original resource.	``
`number_of_p_o_not_rented_4`	`double`	Source column from the original resource.	``
`number_of_pmb_available_4`	`double`	Source column from the original resource.	``
`number_of_pmb_rented_4`	`double`	Source column from the original resource.	``
`number_of_pmb_not_rented_4`	`double`	Source column from the original resource.	``
`s_n_5`	`double`	Source column from the original resource.	``
`states`	`string`	Source column from the original resource.	``
`total_number_of_boxes_installed_2`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented_5`	`double`	Source column from the original resource.	``
`total_number_of_business_boxes`	`double`	Source column from the original resource.	``
`total_number_of_private_boxes`	`double`	Source column from the original resource.	``
`total_number_of_boxes_unrented`	`double`	Source column from the original resource.	``
`total_number_of_pmbs_available`	`double`	Source column from the original resource.	``
`total_number_of_pmbs_rented`	`double`	Source column from the original resource.	``
`total_number_of_pmbs_unrented`	`double`	Source column from the original resource.	``
`revenue_item`	`string`	Source column from the original resource.	``
`2024`	`double`	Source column from the original resource.	``
`s_n_6`	`double`	Source column from the original resource.	``
`states_2`	`string`	Source column from the original resource.	``
`total_number_of_boxes_installed_3`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented_6`	`double`	Source column from the original resource.	``
`total_number_of_business_boxes_2`	`double`	Source column from the original resource.	``
`total_number_of_private_boxes_2`	`double`	Source column from the original resource.	``
`total_number_of_boxes_not_rented`	`double`	Source column from the original resource.	``
`total_number_of_pmbs_available_2`	`double`	Source column from the original resource.	``
`total_number_of_pmbs_rented_2`	`double`	Source column from the original resource.	``
`total_number_of_pmbs_not_rented`	`double`	Source column from the original resource.	``
`2025`	`double`	Source column from the original resource.	``
`s_n_7`	`double`	Source column from the original resource.	``
`states_3`	`string`	Source column from the original resource.	``
`total_number_of_boxes_installed_4`	`double`	Source column from the original resource.	``
`total_number_of_boxes_rented_7`	`double`	Source column from the original resource.	``
`total_number_of_business_boxes_3`	`double`	Source column from the original resource.	``
`total_number_of_private_boxes_3`	`double`	Source column from the original resource.	``
`total_number_of_boxes_not_rented_2`	`double`	Source column from the original resource.	``
`total_number_of_pmb_available`	`double`	Source column from the original resource.	``
`total_number_of_pmb_rented`	`string`	Source column from the original resource.	``
`total_number_of_pmb_not_rented`	`double`	Source column from the original resource.	``

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-postal-services-data-d218ed0f")
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"] == "NGA"]

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

—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: National Bureau of Statistics, Nigeria
—Publisher: National Bureau of Statistics, Nigeria
—Portal: https://microdata.nigerianstat.gov.ng
—Resource: Postal Service Data 2023-2025
—License: other-open
—Retrieved/generated: 2026-07-19T04:21:51Z
—Hugging Face repo: electricsheepafrica/africa-nigeria-postal-services-data-d218ed0f

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

—Track mobility over time
—Compare routes or geographies
—Join with economic and population data
—Check missingness before modeling
—Use country_iso3 as the safest geography join key when present

Citation

bibtex

@misc{electric_sheep_africa_africa_nigeria_postal_services_data_d218ed0f_2025,
  title        = {Postal Services Data | Africa (National Bureau of Statistics, Nigeria)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2025},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/183/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-postal-services-data-d218ed0f}}
}

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.

Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://microdata.nigerianstat.gov.ng/index.php/catalog/183/related-materials