---
license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: multilingual
size_categories:
- n<1K
tags:
- "tabular"
- "africa"
- "open-data"
- "official-statistics"
- "nigeria"
- "national-bureau-of-statistics-nigeria"
- "transport"
- "national-economy"
- "transport-fare-watch-report-april-2026"
- "transport-fare-report-april-2026-zip"
- "0"
- "document"
- "report-doc"
- "rep"
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-00000-of-00001.parquet
pretty_name: "Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria)"
---

# Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria)

**73 rows** - **1 Africa country/area** - **2026** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![rows](https://img.shields.io/badge/rows-73-blue)
![countries](https://img.shields.io/badge/countries-1-green)
![period](https://img.shields.io/badge/period-2026-orange)
![indicators](https://img.shields.io/badge/indicators-0-purple)
![license](https://img.shields.io/badge/license-other-lightgrey)

## TL;DR

This dataset contains **73 rows** from **National Bureau of Statistics, Nigeria**, covering **Transport Fare Watch**. 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:** `year`.
- **Time coverage basis:** year.
- **Recommended join keys:** `country_iso3` where available plus source-specific keys.

## Coverage

| Dimension | Value |
|---|---:|
| Rows | 73 |
| Countries/areas | 1 |
| First period | 2026 |
| Last period | 2026 |
| Indicators | 0 |
| Columns | 28 |
| Source format | ZIP |

## Geographic Coverage

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

| Area | Rows | First year | Last year | Name |
|------|-----:|-----------:|----------:|------|
| `NGA` | 73 | 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-161-1414:transport-cost-watch-apr-2026-1:0` |
| `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. | `TRANSPORT COST Watch APR_2026_ (1).xlsx::Transport April 2026` |
| `year` | `int64` | Observation year. | `2026` |
| `zone` | `string` | Source column from the original resource. | `Air fare charg.for specified routes single journey` |
| `average_of_apr_25` | `double` | Source column from the original resource. | `130243.90022074323` |
| `average_of_mar_26` | `double` | Source column from the original resource. | `157224.05184667662` |
| `average_of_apr_26` | `double` | Source column from the original resource. | `157355.8675448459` |
| `mom` | `double` | Source column from the original resource. | `0.0838393977391154` |
| `yoy` | `double` | Source column from the original resource. | `20.816304854317224` |
| `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. | `Transport Fare Watch` |
| `source_resource` | `string` | Source resource title, table name, or file name. | `Transport Fare Watch Report April 2026` |
| `source_package_id` | `string` | Source package identifier. | `NGA-NBS-TRANSFW` |
| `source_resource_id` | `string` | Source resource identifier. | `nbs-nada-161-1414` |
| `source_url` | `string` | Original source URL or download URL. | `https://microdata.nigerianstat.gov.ng/index.php/catalog/161/download/...` |
| `license_id` | `string` | Source license identifier. | `other-open` |
| `retrieved_at` | `string` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-07-19T10:27:56Z` |
| `state` | `string` | Source column from the original resource. | `` |
| `air_fare_charg_for_specified_routes_single_journey` | `double` | Source column from the original resource. | `` |
| `bus_journey_intercity_state_route_charg_per_per` | `double` | Source column from the original resource. | `` |
| `bus_journey_within_city_per_drop_constant_rou` | `double` | Source column from the original resource. | `` |
| `journey_by_motorcycle_okada_per_drop` | `double` | Source column from the original resource. | `` |
| `water_transport_water_way_passenger_transportat` | `double` | Source column from the original resource. | `` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-transport-fare-watch-8438da63")
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

- 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](https://microdata.nigerianstat.gov.ng/index.php/catalog/161/related-materials)
- **Publisher:** National Bureau of Statistics, Nigeria
- **Portal:** [https://microdata.nigerianstat.gov.ng](https://microdata.nigerianstat.gov.ng)
- **Resource:** [Transport Fare Watch Report April 2026](https://microdata.nigerianstat.gov.ng/index.php/catalog/161/download/1414)
- **License:** other-open
- **Retrieved/generated:** `2026-07-19T10:28:31Z`
- **Hugging Face repo:** [electricsheepafrica/africa-nigeria-transport-fare-watch-8438da63](https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-transport-fare-watch-8438da63)

## 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
- 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_nigeria_transport_fare_watch_8438da63_2026,
  title        = {Transport Fare Watch | Africa (National Bureau of Statistics, Nigeria)},
  author       = {National Bureau of Statistics, Nigeria},
  year         = {2026},
  url          = {https://microdata.nigerianstat.gov.ng/index.php/catalog/161/related-materials},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-nigeria-transport-fare-watch-8438da63}}
}
```

## 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/161/related-materials