Standardize Electric Sheep Africa dataset card

README.md

@@ -5,89 +5,108 @@ language:
 task_categories:
 - tabular-regression
 - time-series-forecasting
-multilinguality: monolingual
+multilinguality: multilingual
 size_categories:
 - n<1K
 tags:
-- tabular
-- csv
-- africa
-- mauritius
-- official-statistics
-- open-data
-- economics
+- "tabular"
+- "africa"
+- "open-data"
+- "official-statistics"
+- "mauritius"
+- "mdpa"
+- "economics"
+- "finance-and-trade"
+- "america"
+- "europe"
+- "oceania"
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-00000-of-00001.parquet
-pretty_name: "EOE imports by country of origin 2015 - 2019 | Africa (Mauritius official open data)"
+pretty_name: "Eoe Imports by Country of Origin 2015 2019 | Africa (MDPA)"
 ---
 
-# EOE imports by country of origin 2015 - 2019 | Africa (Mauritius official open data)
+# Eoe Imports by Country of Origin 2015 2019 | Africa (MDPA)
 
-785 rows - 1 Africa country - 2015-2019 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
+**785 rows** - **1 Africa country/area** - **2015-2019** - **4 indicators** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
 ![rows](https://img.shields.io/badge/rows-785-blue)
 ![countries](https://img.shields.io/badge/countries-1-green)
-![years](https://img.shields.io/badge/years-2015-2019-orange)
+![period](https://img.shields.io/badge/period-2015--2019-orange)
 ![indicators](https://img.shields.io/badge/indicators-4-purple)
-![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
 ## TL;DR
 
-This dataset packages one official `CSV` resource from **Mauritius** as
-ML-ready Parquet. The source file is the provenance boundary; all usable
-indicators or tabular columns from the resource stay together in this repo.
+This dataset contains **785 rows** from **MDPA**, covering **Eoe Imports by Country of Origin 2015 2019**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
 
-## About the source
+## What This Dataset Measures
 
-- **Source:** [EOE imports by country of origin 2015 - 2019](https://data.govmu.org/dataset/eoe-imports-by-country-of-origin-2015-2019)
-- **Publisher:** MDPA
-- **Resource:** [EOE imports by country of origin, 2015- 2019.csv](https://data.govmu.org/dataset/7e9224be-32a5-4958-80e6-a4010f09a2b2/resource/2a98d9ae-fef9-485f-be22-c72441698863/download/eoe-imports-by-country-of-origin-2015-2019.csv)
-- **Format:** `CSV`
-- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
-- **Packaging mode:** `indicator_long`
+Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.
+
+Source-provided context: Dataset shows the Export Oriented Enterprise Sector imports by country of origin Quarterwise 2015 - 2019
+
+## 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
+
+| Dimension | Value |
+|---|---:|
+| Rows | 785 |
+| Countries/areas | 1 |
+| First period | 2015 |
+| Last period | 2019 |
+| Indicators | 4 |
+| Columns | 20 |
+| Source format | CSV |
 
-## Geographic coverage
+## Geographic Coverage
 
-1 Africa country:
+Top areas shown below, sorted by row count when available:
 
-| Country | Rows | First year | Last year | Name |
-|---------|-----:|-----------:|----------:|------|
+| Area | Rows | First year | Last year | Name |
+|------|-----:|-----------:|----------:|------|
 | `MU` | 785 | 2015 | 2019 | `Mauritius` |
 
-## Indicators or Resource Contents
+## Indicators, Variables, Or Resource Contents
 
-- `eoe-imports-by-country-of-origin-2015-2019-qr1-35538d24` - EOE imports by country of origin 2015 - 2019 - qr1
-- `eoe-imports-by-country-of-origin-2015-2019-qr2-f44c6866` - EOE imports by country of origin 2015 - 2019 - qr2
-- `eoe-imports-by-country-of-origin-2015-2019-qr3-d289843d` - EOE imports by country of origin 2015 - 2019 - qr3
-- `eoe-imports-by-country-of-origin-2015-2019-qr4-0d06dc9e` - EOE imports by country of origin 2015 - 2019 - qr4
+- `eoe-imports-by-country-of-origin-2015-2019-qr1-35538d24` - EOE imports by country of origin 2015 - 2019 - qr1(source_units_unspecified)
+- `eoe-imports-by-country-of-origin-2015-2019-qr2-f44c6866` - EOE imports by country of origin 2015 - 2019 - qr2(source_units_unspecified)
+- `eoe-imports-by-country-of-origin-2015-2019-qr3-d289843d` - EOE imports by country of origin 2015 - 2019 - qr3(source_units_unspecified)
+- `eoe-imports-by-country-of-origin-2015-2019-qr4-0d06dc9e` - EOE imports by country of origin 2015 - 2019 - qr4(source_units_unspecified)
 
 ## Schema
 
 | Column | Type | Description | Example |
 |--------|------|-------------|---------|
-| `indicator_id` | `string` | Stable indicator identifier. | `eoe-imports-by-country-of-origin-2015-2019-qr1-35538d24` |
+| `indicator_id` | `string` | Stable source or Electric Sheep Africa indicator identifier. | `eoe-imports-by-country-of-origin-2015-2019-qr1-35538d24` |
 | `indicator_name` | `string` | Human-readable indicator name. | `EOE imports by country of origin 2015 - 2019 - qr1` |
-| `country_iso3` | `string` | ISO3 country code. | `MU` |
-| `country_name` | `string` | Country name. | `Mauritius` |
-| `year` | `Int64` | Observation year. | `2015` |
-| `value` | `float64` | Numeric observation value. | `2185.0` |
-| `unit` | `string` | Measurement unit, when available. | `source_units_unspecified` |
-| `dimension_region` | `string` | Source dimension. | `Europe` |
-| `dimension_country_of_origin` | `string` | Source dimension. | `Europe` |
-| `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2015` |
-| `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2019` |
-| `source_period_label` | `category` | Human-readable period inferred from source resource metadata. | `2015-2019` |
-| `source_provider` | `category` | Publishing organization. | `MDPA` |
-| `source_dataset` | `category` | Source package title. | `EOE imports by country of origin 2015 - 2019` |
-| `source_resource` | `category` | Source resource title. | `EOE imports by country of origin, 2015- 2019.csv` |
-| `source_package_id` | `category` | CKAN package UUID. | `7e9224be-32a5-4958-80e6-a4010f09a2b2` |
-| `source_resource_id` | `category` | CKAN resource UUID. | `2a98d9ae-fef9-485f-be22-c72441698863` |
-| `source_url` | `category` | Original source resource URL. | `https://data.govmu.org/dataset/7e9224be-32a5-4958-80e6-a4010f09a2b2/reso` |
-| `license_id` | `category` | Source license identifier. | `cc-by` |
-| `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-08T16:26:20Z` |
+| `country_iso3` | `string` | ISO3 country or area code. | `MU` |
+| `country_name` | `string` | Country or area name. | `Mauritius` |
+| `year` | `int64` | Observation year. | `2015` |
+| `value` | `double` | Numeric observation value. | `2185.0` |
+| `unit` | `string` | Measurement unit, when supplied by the source. | `source_units_unspecified` |
+| `dimension_region` | `string` | Source dimension retained during long-form normalization. | `Europe` |
+| `dimension_country_of_origin` | `string` | Source dimension retained during long-form normalization. | `Europe` |
+| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2015` |
+| `source_period_end_year` | `int64` | End year inferred from source metadata. | `2019` |
+| `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `2015-2019` |
+| `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. | `EOE imports by country of origin 2015 - 2019` |
+| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `EOE imports by country of origin, 2015- 2019.csv` |
+| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `7e9224be-32a5-4958-80e6-a4010f09a2b2` |
+| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `2a98d9ae-fef9-485f-be22-c72441698863` |
+| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://data.govmu.org/dataset/7e9224be-32a5-4958-80e6-a4010f09a2b2/r...` |
+| `license_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source license identifier. | `cc-by` |
+| `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
 
@@ -99,29 +118,78 @@ df = ds["train"].to_pandas()
 print(df.head())
 ```
 
-### Filter to one country
+### Inspect Columns
 
 ```python
-sample_country = df[df["country_iso3"] == "MU"]
+print(df.info())
+print(df.head())
 ```
 
-### Work with indicators
+### Filter By Geography
 
 ```python
-if "indicator_id" in df.columns:
-    print(df["indicator_id"].value_counts().head())
-    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
+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](https://data.govmu.org/dataset/eoe-imports-by-country-of-origin-2015-2019)
+- **Publisher:** MDPA
+- **Portal:** [https://data.govmu.org](https://data.govmu.org)
+- **Resource:** [EOE imports by country of origin, 2015- 2019.csv](https://data.govmu.org/dataset/7e9224be-32a5-4958-80e6-a4010f09a2b2/resource/2a98d9ae-fef9-485f-be22-c72441698863/download/eoe-imports-by-country-of-origin-2015-2019.csv)
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Retrieved/generated:** `2026-08-08T16:52:27Z`
+- **Hugging Face repo:** [electricsheepafrica/africa-mauritius-eoe-imports-by-country-of-origin-2015-2019-aa5e6a98](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-eoe-imports-by-country-of-origin-2015-2019-aa5e6a98)
+
+## 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_eoe_imports_by_country_of_origin_2015_2019_aa5e6a98_2019,
-  title        = {EOE imports by country of origin 2015 - 2019 | Africa (Mauritius official open data)},
+  title        = {Eoe Imports by Country of Origin 2015 2019 | Africa (MDPA)},
   author       = {MDPA},
   year         = {2019},
   url          = {https://data.govmu.org/dataset/eoe-imports-by-country-of-origin-2015-2019},
-  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
   howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-eoe-imports-by-country-of-origin-2015-2019-aa5e6a98}}
 }
 ```
@@ -130,20 +198,14 @@ if "indicator_id" in df.columns:
 
 Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
 
-Original data (c) MDPA. When using this dataset, please cite both the
-original source above and the Electric Sheep Africa repackaging.
-
-## About Electric Sheep
+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.
 
-Electric Sheep Africa is part of the Electric Sheep mission: a unified,
-ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
-open sources, normalize the schemas, package as Parquet, and publish with
-consistent dataset cards so researchers and developers can use `load_dataset()`
-to start working in seconds.
+## About Electric Sheep Africa
 
-Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
 
 ---
 
-Provenance: ingested 2026-08-08 via the Electric Sheep pipeline. Source URL:
-https://data.govmu.org/dataset/7e9224be-32a5-4958-80e6-a4010f09a2b2/resource/2a98d9ae-fef9-485f-be22-c72441698863/download/eoe-imports-by-country-of-origin-2015-2019.csv
+Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/eoe-imports-by-country-of-origin-2015-2019