electricsheepafrica/africa-mauritius-imports-of-goods-by-country-of-origin-65a4cbf5

Imports of Goods by Country of Origin | Africa (MDPA)

76 rows - 1 Africa country/area - 2017-2021 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 76 rows from MDPA, covering Imports of Goods by Country of Origin. 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 goods imported by country of origin from year 1956 to 2022 and by quater from year 2017 to 2021

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	76
Countries/areas	1
First period	2017
Last period	2021
Indicators	0
Columns	41
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	76	2017	2021	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.	d622bf71-b467-440d-9e7a-da3bac92ebd9:table-9:0
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.	Table 9
all_countries	string	Source column from the original resource.	Europe
d_41171	double	Source column from the original resource.	11172.0
d_44332	double	Source column from the original resource.	10889.0
d_43353	double	Source column from the original resource.	11359.0
d_52011	double	Source column from the original resource.	13135.0
d_180867	double	Source column from the original resource.	46555.0
d_39090	double	Source column from the original resource.	9886.0
d_47987	double	Source column from the original resource.	12466.0
d_50463	double	Source column from the original resource.	12691.0
d_54898	double	Source column from the original resource.	12989.0
d_192438	double	Source column from the original resource.	48032.0
d_46264	double	Source column from the original resource.	12322.0
d_48405	double	Source column from the original resource.	12366.0
d_51349	double	Source column from the original resource.	13955.0
d_52621	double	Source column from the original resource.	13690.0
d_198639	double	Source column from the original resource.	52333.0
d_43963	double	Source column from the original resource.	11232.0
d_34206	double	Source column from the original resource.	9331.0
d_40241	double	Source column from the original resource.	10772.0
d_47312	double	Source column from the original resource.	12693.0
d_165722	double	Source column from the original resource.	44028.0
d_44164	double	Source column from the original resource.	11214.0
d_48231	double	Source column from the original resource.	11251.0
d_54768	double	Source column from the original resource.	13490.0
d_67673	double	Source column from the original resource.	15544.0
d_214836	double	Source column from the original resource.	51499.0
source_period_start_year	int64	Start year inferred from source metadata.	2017
source_period_end_year	int64	End year inferred from source metadata.	2021
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2017-2021
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	MDPA
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Imports of goods by country of Origin
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Source_file_2017-2021.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	0c0d967f-8824-41b1-97c1-9ac3f19373ff
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	d622bf71-b467-440d-9e7a-da3bac92ebd9
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/0c0d967f-8824-41b1-97c1-9ac3f19373ff/r...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	CC-BY-SA-4.0
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-08T16:26:20Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-imports-of-goods-by-country-of-origin-65a4cbf5")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MU"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

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: MDPA
• Publisher: MDPA
• Portal: https://data.govmu.org
• Resource: Source_file_2017-2021.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T16:30:56Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-imports-of-goods-by-country-of-origin-65a4cbf5

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

@misc{electric_sheep_africa_africa_mauritius_imports_of_goods_by_country_of_origin_65a4cbf5_2021,
  title        = {Imports of Goods by Country of Origin | Africa (MDPA)},
  author       = {MDPA},
  year         = {2021},
  url          = {https://data.govmu.org/dataset/imports-goods-country-origin},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-imports-of-goods-by-country-of-origin-65a4cbf5}}
}

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/imports-goods-country-origin