Datasetpublic

electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-f5643a05

Mobile Banking and Mobile Payments | Africa (MDPA) 33 rows - 1 Africa country/area - 2023 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 33 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-f5643a05.

sourceHugging Facecc-by-sa-4.0updated 2mo agoView on Hugging Face

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Dataset Card

Mobile Banking and Mobile Payments | Africa (MDPA)

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

TL;DR

This dataset contains 33 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 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	33
Countries/areas	1
First period	2023
Last period	2023
Indicators	0
Columns	156
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
`MU`	33	2023	2023	`Mauritius`

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.	`72557da3-372e-4403-85d9-efd741663bf0:31-32-33:0`
`country_iso3`	`dictionary<values=string, indices=int8, ordered=0>`	ISO3 country or area code.	`MU`
`country_name`	`dictionary<values=string, indices=int8, ordered=0>`	Country or area name.	`Mauritius`
`source_sheet`	`string`	Source column from the original resource.	`31-32-33`
`year`	`int64`	Observation year.	`2023`
`number_of_transactions`	`string`	Source column from the original resource.	`Value of Transactions (Rs million) 2`
`d_4736872`	`string`	Source column from the original resource.	`9717.931`
`d_4319467`	`string`	Source column from the original resource.	`8695.531`
`d_4841422`	`string`	Source column from the original resource.	`9537`
`d_4758541`	`string`	Source column from the original resource.	`9328.38`
`d_4845776`	`string`	Source column from the original resource.	`9365.379`
`d_4496701`	`string`	Source column from the original resource.	`8566.686`
`d_4733299`	`string`	Source column from the original resource.	`9187.151`
`d_4753864`	`string`	Source column from the original resource.	`9327.463`
`d_4589854`	`string`	Source column from the original resource.	`8899.062`
`d_5016549`	`string`	Source column from the original resource.	`10019.85`
`d_4831238`	`string`	Source column from the original resource.	`9953.074`
`d_6407067`	`string`	Source column from the original resource.	`14412.458`
`d_4875444`	`string`	Source column from the original resource.	`10301.35`
`d_4576070`	`string`	Source column from the original resource.	`9300.485`
`d_5159362`	`string`	Source column from the original resource.	`10679.24`
`d_5247975`	`string`	Source column from the original resource.	`11267.70279`
`d_4677566`	`string`	Source column from the original resource.	`9276.966`
`d_5215652`	`string`	Source column from the original resource.	`10612.598168220002`
`d_5146740`	`string`	Source column from the original resource.	`10549.572`
`d_4946438`	`string`	Source column from the original resource.	`9942`
`d_5139787`	`string`	Source column from the original resource.	`10729.645`
`d_5093468`	`string`	Source column from the original resource.	`10840.106`
`d_6796552`	`string`	Source column from the original resource.	`15747.024`
`d_5089885`	`string`	Source column from the original resource.	`11116.937`
`d_4795824`	`string`	Source column from the original resource.	`12597.385472539`
`d_5439117`	`string`	Source column from the original resource.	`11425`
`d_5556138`	`string`	Source column from the original resource.	`11616.533`
`d_5635041`	`string`	Source column from the original resource.	`11411.8463010512`
`d_5320280`	`string`	Source column from the original resource.	`10729.509122245256`
`d_5507836`	`string`	Source column from the original resource.	`11263.0062579179`
`d_5233474`	`string`	Source column from the original resource.	`10996.251`
`d_5283765`	`string`	Source column from the original resource.	`10655`
`d_5542287`	`string`	Source column from the original resource.	`11325.604`
`d_5430649`	`string`	Source column from the original resource.	`11628.968`
`d_7185702`	`string`	Source column from the original resource.	`17038.289164287296`
`d_5576038`	`string`	Source column from the original resource.	`11990.63`
`d_5217581`	`string`	Source column from the original resource.	`11039`
`d_5980306`	`string`	Source column from the original resource.	`12689.2040794632`
`d_5385116`	`string`	Source column from the original resource.	`11416`
`d_5476327`	`string`	Source column from the original resource.	`11568.88887716`
`d_5381144`	`string`	Source column from the original resource.	`11032.510691`
`d_5583771`	`string`	Source column from the original resource.	`11767`
`d_5722712`	`string`	Source column from the original resource.	`12212`
`d_5278224`	`string`	Source column from the original resource.	`10979.01516`
`d_5641964`	`string`	Source column from the original resource.	`12170`
`d_5639078`	`string`	Source column from the original resource.	`12319`
`d_7340347`	`string`	Source column from the original resource.	`17686.962684089995`
`d_5541738`	`string`	Source column from the original resource.	`12299.68105725`
`d_5436047`	`string`	Source column from the original resource.	`11863`
`d_5734387`	`string`	Source column from the original resource.	`12300`
`d_5520603`	`string`	Source column from the original resource.	`12047`
`d_6001113`	`string`	Source column from the original resource.	`12894.22292939`
`d_5408488`	`string`	Source column from the original resource.	`11442`
`d_5762671`	`string`	Source column from the original resource.	`12705.69796049`
`d_6034651`	`string`	Source column from the original resource.	`13047`
`d_5574065`	`string`	Source column from the original resource.	`11945`
`d_6189540`	`string`	Source column from the original resource.	`13772.597299`
`d_5990000`	`string`	Source column from the original resource.	`13412`
`d_8031505`	`string`	Source column from the original resource.	`19581.84666339`
`d_6197949`	`string`	Source column from the original resource.	`13905`
`d_5467258`	`string`	Source column from the original resource.	`12044.143946`
`d_6180864`	`string`	Source column from the original resource.	`13521`
`d_5874355`	`string`	Source column from the original resource.	`12691.390219`
`d_6477234`	`string`	Source column from the original resource.	`13828`
`d_5857453`	`string`	Source column from the original resource.	`12433.66978`
`d_6305140`	`string`	Source column from the original resource.	`13739`
`d_6378216`	`string`	Source column from the original resource.	`13832`
`d_6056687`	`string`	Source column from the original resource.	`12928`
`d_6754415`	`string`	Source column from the original resource.	`14819`
`d_6368444`	`string`	Source column from the original resource.	`14337`
`d_8181811`	`string`	Source column from the original resource.	`19660`
`d_6383125`	`string`	Source column from the original resource.	`14085`
`d_6109633`	`string`	Source column from the original resource.	`13453`
`d_6979691`	`string`	Source column from the original resource.	`15346`
`d_6781194`	`string`	Source column from the original resource.	``
`d_7170154`	`string`	Source column from the original resource.	``
`d_6256345`	`string`	Source column from the original resource.	``
`d_7092455`	`string`	Source column from the original resource.	``
`d_6979838`	`string`	Source column from the original resource.	``
`d_6511710`	`string`	Source column from the original resource.	``
`d_7300253`	`string`	Source column from the original resource.	``
`d_6950183`	`string`	Source column from the original resource.	``
`d_8741586`	`string`	Source column from the original resource.	``
`d_6933706`	`string`	Source column from the original resource.	``
`d_6547750`	`string`	Source column from the original resource.	``
`d_7382070`	`string`	Source column from the original resource.	``
`d_7541784`	`string`	Source column from the original resource.	``
`d_7489177`	`string`	Source column from the original resource.	``
`d_6826339`	`string`	Source column from the original resource.	``
`d_7573108`	`string`	Source column from the original resource.	``
`d_7493801`	`string`	Source column from the original resource.	``
`d_7230248`	`string`	Source column from the original resource.	``
`d_7884889`	`string`	Source column from the original resource.	``
`d_7291162`	`string`	Source column from the original resource.	``
`d_9844856`	`string`	Source column from the original resource.	``
`d_7816420`	`string`	Source column from the original resource.	``
`d_7442364`	`string`	Source column from the original resource.	``
`d_6161186`	`string`	Source column from the original resource.	``
`d_3476151`	`string`	Source column from the original resource.	``
`d_4918106`	`string`	Source column from the original resource.	``
`d_7209048`	`string`	Source column from the original resource.	``
`d_7512766`	`string`	Source column from the original resource.	``
`d_7698972`	`string`	Source column from the original resource.	``
`d_7498068`	`string`	Source column from the original resource.	``
`d_7944895`	`string`	Source column from the original resource.	``
`d_7996753`	`string`	Source column from the original resource.	``
`d_10479089`	`string`	Source column from the original resource.	``
`d_7580807`	`string`	Source column from the original resource.	``
`d_7574366`	`string`	Source column from the original resource.	``
`d_6141863`	`string`	Source column from the original resource.	``
`d_6034299`	`string`	Source column from the original resource.	``
`d_8229121`	`string`	Source column from the original resource.	``
`d_7636198`	`string`	Source column from the original resource.	``
`d_8219719`	`string`	Source column from the original resource.	``
`d_8533111`	`string`	Source column from the original resource.	``
`d_8227080`	`string`	Source column from the original resource.	``
`d_8832962`	`string`	Source column from the original resource.	``
`d_9124192`	`string`	Source column from the original resource.	``
`d_11445777`	`string`	Source column from the original resource.	``
`d_8933304`	`string`	Source column from the original resource.	``
`d_8033854`	`string`	Source column from the original resource.	``
`d_9472740`	`string`	Source column from the original resource.	``
`d_8936973`	`string`	Source column from the original resource.	``
`d_9911230`	`string`	Source column from the original resource.	``
`d_9203444`	`string`	Source column from the original resource.	``
`d_9201531`	`string`	Source column from the original resource.	``
`d_10403312`	`string`	Source column from the original resource.	``
`d_9749477`	`string`	Source column from the original resource.	``
`d_10808319`	`string`	Source column from the original resource.	``
`d_10588925`	`string`	Source column from the original resource.	``
`d_13073319`	`string`	Source column from the original resource.	``
`d_10310571`	`string`	Source column from the original resource.	``
`d_9541962`	`string`	Source column from the original resource.	``
`d_10848370`	`string`	Source column from the original resource.	``
`d_10593186`	`string`	Source column from the original resource.	``
`d_11665899`	`string`	Source column from the original resource.	``
`d_10454613`	`string`	Source column from the original resource.	``
`d_11462453`	`string`	Source column from the original resource.	``
`d_11661946`	`string`	Source column from the original resource.	``
`source_period_start_year`	`int64`	Start year inferred from source metadata.	``
`source_period_end_year`	`int64`	End year inferred from source metadata.	``
`source_period_label`	`dictionary<values=string, indices=int8, ordered=0>`	Source column from the original resource.	``
`source_provider`	`dictionary<values=string, indices=int8, ordered=0>`	Publishing organization.	``
`source_dataset`	`dictionary<values=string, indices=int8, ordered=0>`	Source dataset or package title.	``
`source_resource`	`dictionary<values=string, indices=int8, ordered=0>`	Source resource title, table name, or file name.	``
`source_package_id`	`dictionary<values=string, indices=int8, ordered=0>`	Source package identifier.	``
`source_resource_id`	`dictionary<values=string, indices=int8, ordered=0>`	Source resource identifier.	``
`source_url`	`dictionary<values=string, indices=int8, ordered=0>`	Original source URL or download URL.	``
`license_id`	`dictionary<values=string, indices=int8, ordered=0>`	Source license identifier.	``
`retrieved_at`	`dictionary<values=string, indices=int8, ordered=0>`	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	``

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-f5643a05")
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

—Source: MDPA
—Publisher: MDPA
—Portal: https://data.govmu.org
—Resource: msb_september_2023_source_file_0.xlsx
—License: CC BY-SA 4.0
—Retrieved/generated: 2026-08-08T16:30:05Z
—Hugging Face repo: electricsheepafrica/africa-mauritius-mobile-banking-and-mobile-payments-f5643a05

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

bibtex

@misc{electric_sheep_africa_africa_mauritius_mobile_banking_and_mobile_payments_f5643a05_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-f5643a05}}
}

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