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electricsheepafrica/africa-mauritius-budget-data-for-procurement-policy-office-2016-2017-9b375106

Budget Data for Procurement Policy Office 2016 2017 | Africa (MDPA) 36 rows - 1 Africa country/area - 2016-2017 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 36 rows from MDPA, covering Budget Data for Procurement Policy Office 2016 2017. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures Economic… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-budget-data-for-procurement-policy-office-2016-2017-9b375106.

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Budget Data for Procurement Policy Office 2016 2017 | Africa (MDPA)

36 rows - 1 Africa country/area - 2016-2017 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 36 rows from MDPA, covering Budget Data for Procurement Policy Office 2016 2017. 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: Budget Data 2016-2017- Procurement Policy Office

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

DimensionValue
Rows36
Countries/areas1
First period2016
Last period2017
Indicators0
Columns24
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU3620162017Mauritius

Indicators, Variables, Or Resource Contents

  • —This repo preserves one source tabular resource with its usable columns kept together.

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.050fb8e2-65c8-434f-a88d-842806085716:0
country_iso3dictionary<values=string, indices=int8, ordered=0>ISO3 country or area code.MU
country_namedictionary<values=string, indices=int8, ordered=0>Country or area name.Mauritius
headstringSource column from the original resource.MOFED
subheadstringSource column from the original resource.PPO
expensetypestringSource column from the original resource.Recurrent Expenditure
itemnoint64Source column from the original resource.21110
categorystringSource column from the original resource.Compensation of Employees
subcategorystringSource column from the original resource.Personal Emoluments
startfinancialyearstringSource column from the original resource.1-Jul-16
endfinancialyearstringSource column from the original resource.30-Jun-17
financialstatusstringSource column from the original resource.Estimates
amountint64Source column from the original resource.12650000
source_period_start_yearint64Start year inferred from source metadata.2016
source_period_end_yearint64End year inferred from source metadata.2017
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2016-2017
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Budget Data for Procurement Policy Office 2016-2017
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.DATA_Budget_PPO_2016_2017.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.9ced0378-a1a0-4f4e-9ba5-6b7535c96a9e
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.050fb8e2-65c8-434f-a88d-842806085716
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/9ced0378-a1a0-4f4e-9ba5-6b7535c96a9e/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-budget-data-for-procurement-policy-office-2016-2017-9b375106")
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

  • —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

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
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_mauritius_budget_data_for_procurement_policy_office_2016_2017_9b375106_2017,
  title        = {Budget Data for Procurement Policy Office 2016 2017 | Africa (MDPA)},
  author       = {MDPA},
  year         = {2017},
  url          = {https://data.govmu.org/dataset/budget-data-procurement-policy-office-2016-2017},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-budget-data-for-procurement-policy-office-2016-2017-9b375106}}
}

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/budget-data-procurement-policy-office-2016-2017