CoolFace
Datasetpublic

electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-abe06f49

Mobile Banking and Mobile Payments | Africa (MDPA) 224 rows - 1 Africa country/area - 2019-2023 - 2 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 224 rows from MDPA, covering Mobile Banking and Mobile Payments. 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-abe06f49.

sourceHugging Facecc-by-sa-4.0updated 2mo agoView on Hugging Face
0likes9downloads
Dataset Card

Mobile Banking and Mobile Payments | Africa (MDPA)

224 rows - 1 Africa country/area - 2019-2023 - 2 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 224 rows from MDPA, covering Mobile Banking and Mobile Payments. 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: The data shows number of monthly Mobile Banking and Mobile Payments from year 2019 to August 2023

How To Read This Dataset

  • —One row means: one indicator observation for one geography, time period, and optional source dimensions.
  • —Primary geography column: country_iso3.
  • —Best time column: year.
  • —Time coverage basis: year.
  • —Recommended join keys: country_iso3, year, indicator_id.

Coverage

DimensionValue
Rows224
Countries/areas1
First period2019
Last period2023
Indicators2
Columns20
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU22420192023Mauritius

Indicators, Variables, Or Resource Contents

  • —mobile-banking-and-mobile-payments-value-95443e14 - Mobile Banking and Mobile Payments - value(sourceunitsunspecified)
  • —mobile-banking-and-mobile-payments-number-a81eda37 - Mobile Banking and Mobile Payments - number(sourceunitsunspecified)

Schema

ColumnTypeDescriptionExample
indicator_idstringStable source or Electric Sheep Africa indicator identifier.mobile-banking-and-mobile-payments-value-95443e14
indicator_namestringHuman-readable indicator name.Mobile Banking and Mobile Payments - value
country_iso3stringISO3 country or area code.MU
country_namestringCountry or area name.Mauritius
yearint64Observation year.2019
valuedoubleNumeric observation value.291526.0
unitstringMeasurement unit, when supplied by the source.source_units_unspecified
dimension_monthstringSource dimension retained during long-form normalization.january
dimension_transactionstringSource dimension retained during long-form normalization.Value of Transaction
source_period_start_yearint64Start year inferred from source metadata.2019
source_period_end_yearint64End year inferred from source metadata.2023
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2019-2023
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Mobile Banking and Mobile Payments
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.internetBanking_Transaction.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.63292598-7815-423e-9b58-182406e425c4
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.538101cf-6847-4b0c-bb96-c27e4741efd2
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/63292598-7815-423e-9b58-182406e425c4/r...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.CC-BY-SA-4.0
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-08-08T16:26:20Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-abe06f49")
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"] == "MU"]

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

  • —Build time-series dashboards
  • —Compare economic indicators
  • —Join with population or sector data
  • —Build time-series views and period-over-period comparisons
  • —Pivot to geography x period or indicator x period matrices
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_mauritius_mobile_banking_and_mobile_payments_abe06f49_2023,
  title        = {Mobile Banking and Mobile Payments | Africa (MDPA)},
  author       = {MDPA},
  year         = {2023},
  url          = {https://data.govmu.org/dataset/mobile-banking-and-mobile-payments},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-abe06f49}}
}

License

Released under CC BY-SA 4.0.

Original data is published by MDPA. 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 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/mobile-banking-and-mobile-payments