Standardize Electric Sheep Africa dataset card

README.md

@@ -5,120 +5,142 @@ language:
 task_categories:
 - tabular-classification
 - tabular-regression
-multilinguality: monolingual
+multilinguality: multilingual
 size_categories:
 - n<1K
 tags:
-- tabular
-- xlsx
-- africa
-- mauritius
-- official-statistics
-- open-data
+- "tabular"
+- "africa"
+- "open-data"
+- "official-statistics"
+- "mauritius"
+- "mdpa"
+- "climate"
+- "environment-and-natural-resources"
+- "commercial"
+- "domestic"
+- "industrial"
+- "religious"
+- "water"
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-00000-of-00001.parquet
-pretty_name: "Water sales by tariff of subscriber | Africa (Mauritius official open data)"
+pretty_name: "Water Sales by Tariff of Subscriber | Africa (MDPA)"
 ---
 
-# Water sales by tariff of subscriber | Africa (Mauritius official open data)
+# Water Sales by Tariff of Subscriber | Africa (MDPA)
 
-28 rows - 1 Africa country - 2016-2022 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
+**28 rows** - **1 Africa country/area** - **2016-2022** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
 ![rows](https://img.shields.io/badge/rows-28-blue)
 ![countries](https://img.shields.io/badge/countries-1-green)
-![years](https://img.shields.io/badge/years-2016-2022-orange)
+![period](https://img.shields.io/badge/period-2016--2022-orange)
 ![indicators](https://img.shields.io/badge/indicators-0-purple)
-![license](https://img.shields.io/badge/license-cc-by-sa-4.0-lightgrey)
+![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey)
 
 ## TL;DR
 
-This dataset packages one official `XLSX` 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 **28 rows** from **MDPA**, covering **Water Sales by Tariff of Subscriber**. 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:** [Water sales by tariff of subscriber](https://data.govmu.org/dataset/water-sales-tariff-subscriber)
-- **Publisher:** MDPA
-- **Resource:** [Energy_Water_Yr22_060623_Source_File.xlsx](https://data.govmu.org/dataset/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/resource/ff1be866-1f8f-4c16-af99-f14cd51ddfb5/download/energy_water_yr22_060623_source_file.xlsx)
-- **Format:** `XLSX`
-- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
-- **Packaging mode:** `tabular_resource`
+Climate and environment datasets help analysts study exposure, resource conditions, environmental pressure, and climate-related trends.
+
+Source-provided context: The data shows the water sales by tariff of subscribers for island of Mauritius for the year 2016 to 2022
+
+## 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 | 28 |
+| Countries/areas | 1 |
+| First period | 2016 |
+| Last period | 2022 |
+| Indicators | 0 |
+| Columns | 55 |
+| Source format | XLSX |
 
-## 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` | 28 | 2016 | 2022 | `Mauritius` |
 
-## Indicators or Resource Contents
+## Indicators, Variables, Or Resource Contents
 
-- This source file is packaged as a normalized tabular resource.
+- 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 for tabular resources. | `ff1be866-1f8f-4c16-af99-f14cd51ddfb5:tab16:0` |
-| `country_iso3` | `category` | ISO3 country code. | `MU` |
-| `country_name` | `category` | Country name. | `Mauritius` |
-| `source_sheet` | `string` | Workbook sheet name, when the source is a spreadsheet. | `TAB16` |
-| `column_1` | `string` | Source column. | `` |
-| `domestic` | `string` | Source column. | `Public Sector Agency` |
-| `d_365971` | `float64` | Source column. | `2587.0` |
-| `d_92_92495588253963` | `float64` | Source column. | `0.6568740716289817` |
-| `d_85053_399` | `float64` | Source column. | `4096.654` |
-| `d_69_77075636118678` | `float64` | Source column. | `3.360555268697507` |
-| `d_832555_79997` | `float64` | Source column. | `98681.707` |
-| `d_55_00829401135688` | `float64` | Source column. | `6.52005829806744` |
-| `d_232_40475064964164` | `float64` | Source column. | `1583.5539234634712` |
-| `d_9_788624672953988` | `float64` | Source column. | `24.088367482340463` |
-| `d_372734` | `float64` | Source column. | `2603.0` |
-| `d_92_94069244099789` | `float64` | Source column. | `0.649054345522323` |
-| `d_87497_492` | `float64` | Source column. | `4338.04` |
-| `d_68_68942650464635` | `float64` | Source column. | `3.40555452439958` |
-| `d_870695_6203399999` | `float64` | Source column. | `104306.187` |
-| `d_53_14584534898735` | `float64` | Source column. | `6.366680104672956` |
-| `d_234_74513191713126` | `float64` | Source column. | `1666.5539761813293` |
-| `d_9_951092316337478` | `float64` | Source column. | `24.04454246618289` |
-| `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2016` |
-| `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2022` |
-| `source_period_label` | `category` | Human-readable period inferred from source resource metadata. | `2016-2022` |
-| `source_provider` | `category` | Publishing organization. | `MDPA` |
-| `source_dataset` | `category` | Source package title. | `Water sales by tariff of subscriber` |
-| `source_resource` | `category` | Source resource title. | `Energy_Water_Yr22_060623_Source_File.xlsx` |
-| `source_package_id` | `category` | CKAN package UUID. | `9584b934-20df-4dd5-98e7-e3ff15f7aa8e` |
-| `source_resource_id` | `category` | CKAN resource UUID. | `ff1be866-1f8f-4c16-af99-f14cd51ddfb5` |
-| `source_url` | `category` | Original source resource URL. | `https://data.govmu.org/dataset/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/reso` |
-| `license_id` | `category` | Source license identifier. | `CC-BY-SA-4.0` |
-| `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-08T16:26:20Z` |
-| `type_of_tariff` | `string` | Source column. | `` |
-| `column_2` | `string` | Source column. | `` |
-| `no_of_consumers` | `float64` | Source column. | `` |
-| `volume_sold_thousand_m3` | `float64` | Source column. | `` |
-| `amount_collectible_rs_000` | `float64` | Source column. | `` |
-| `average_sales_price_per_m3` | `float64` | Source column. | `` |
-| `no_of_consumers_2` | `float64` | Source column. | `` |
-| `volume_sold_thousand_m3_2` | `float64` | Source column. | `` |
-| `amount_collectible_rs_000_2` | `float64` | Source column. | `` |
-| `average_sales_price_per_m3_2` | `float64` | Source column. | `` |
-| `no_of_consumers_3` | `float64` | Source column. | `` |
-| `volume_sold_thousand_m3_3` | `float64` | Source column. | `` |
-| `amount_collectible_rs_000_3` | `float64` | Source column. | `` |
-| `average_sales_price_per_m3_3` | `float64` | Source column. | `` |
-| `no_of_consumers_4` | `float64` | Source column. | `` |
-| `volume_sold_thousand_m3_4` | `float64` | Source column. | `` |
-| `amount_collectible_rs_000_4` | `float64` | Source column. | `` |
-| `average_sales_price_per_m3_4` | `float64` | Source column. | `` |
-| `no_of_consumers_5` | `float64` | Source column. | `` |
-| `volume_sold_thousand_m3_5` | `float64` | Source column. | `` |
-| `amount_collectible_rs_000_5` | `float64` | Source column. | `` |
-| `average_sales_price_per_m3_5` | `float64` | Source column. | `` |
+| `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `ff1be866-1f8f-4c16-af99-f14cd51ddfb5:tab16:0` |
+| `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. | `TAB16` |
+| `column_1` | `string` | Source column from the original resource. | `` |
+| `domestic` | `string` | Source column from the original resource. | `Public Sector Agency` |
+| `d_365971` | `double` | Source column from the original resource. | `2587.0` |
+| `d_92_92495588253963` | `double` | Source column from the original resource. | `0.6568740716289817` |
+| `d_85053_399` | `double` | Source column from the original resource. | `4096.654` |
+| `d_69_77075636118678` | `double` | Source column from the original resource. | `3.360555268697507` |
+| `d_832555_79997` | `double` | Source column from the original resource. | `98681.707` |
+| `d_55_00829401135688` | `double` | Source column from the original resource. | `6.52005829806744` |
+| `d_232_40475064964164` | `double` | Source column from the original resource. | `1583.5539234634712` |
+| `d_9_788624672953988` | `double` | Source column from the original resource. | `24.088367482340463` |
+| `d_372734` | `double` | Source column from the original resource. | `2603.0` |
+| `d_92_94069244099789` | `double` | Source column from the original resource. | `0.649054345522323` |
+| `d_87497_492` | `double` | Source column from the original resource. | `4338.04` |
+| `d_68_68942650464635` | `double` | Source column from the original resource. | `3.40555452439958` |
+| `d_870695_6203399999` | `double` | Source column from the original resource. | `104306.187` |
+| `d_53_14584534898735` | `double` | Source column from the original resource. | `6.366680104672956` |
+| `d_234_74513191713126` | `double` | Source column from the original resource. | `1666.5539761813293` |
+| `d_9_951092316337478` | `double` | Source column from the original resource. | `24.04454246618289` |
+| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2016` |
+| `source_period_end_year` | `int64` | End year inferred from source metadata. | `2022` |
+| `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `2016-2022` |
+| `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. | `Water sales by tariff of subscriber` |
+| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `Energy_Water_Yr22_060623_Source_File.xlsx` |
+| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `9584b934-20df-4dd5-98e7-e3ff15f7aa8e` |
+| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `ff1be866-1f8f-4c16-af99-f14cd51ddfb5` |
+| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://data.govmu.org/dataset/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/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` |
+| `type_of_tariff` | `string` | Source column from the original resource. | `` |
+| `column_2` | `string` | Source column from the original resource. | `` |
+| `no_of_consumers` | `double` | Source column from the original resource. | `` |
+| `volume_sold_thousand_m3` | `double` | Source column from the original resource. | `` |
+| `amount_collectible_rs_000` | `double` | Source column from the original resource. | `` |
+| `average_sales_price_per_m3` | `double` | Source column from the original resource. | `` |
+| `no_of_consumers_2` | `double` | Source column from the original resource. | `` |
+| `volume_sold_thousand_m3_2` | `double` | Source column from the original resource. | `` |
+| `amount_collectible_rs_000_2` | `double` | Source column from the original resource. | `` |
+| `average_sales_price_per_m3_2` | `double` | Source column from the original resource. | `` |
+| `no_of_consumers_3` | `double` | Source column from the original resource. | `` |
+| `volume_sold_thousand_m3_3` | `double` | Source column from the original resource. | `` |
+| `amount_collectible_rs_000_3` | `double` | Source column from the original resource. | `` |
+| `average_sales_price_per_m3_3` | `double` | Source column from the original resource. | `` |
+| `no_of_consumers_4` | `double` | Source column from the original resource. | `` |
+| `volume_sold_thousand_m3_4` | `double` | Source column from the original resource. | `` |
+| `amount_collectible_rs_000_4` | `double` | Source column from the original resource. | `` |
+| `average_sales_price_per_m3_4` | `double` | Source column from the original resource. | `` |
+| `no_of_consumers_5` | `double` | Source column from the original resource. | `` |
+| `volume_sold_thousand_m3_5` | `double` | Source column from the original resource. | `` |
+| `amount_collectible_rs_000_5` | `double` | Source column from the original resource. | `` |
+| `average_sales_price_per_m3_5` | `double` | Source column from the original resource. | `` |
 
 ## Usage
 
@@ -130,29 +152,76 @@ 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
+
+- 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](https://data.govmu.org/dataset/water-sales-tariff-subscriber)
+- **Publisher:** MDPA
+- **Portal:** [https://data.govmu.org](https://data.govmu.org)
+- **Resource:** [Energy_Water_Yr22_060623_Source_File.xlsx](https://data.govmu.org/dataset/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/resource/ff1be866-1f8f-4c16-af99-f14cd51ddfb5/download/energy_water_yr22_060623_source_file.xlsx)
+- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+- **Retrieved/generated:** `2026-08-08T16:30:12Z`
+- **Hugging Face repo:** [electricsheepafrica/africa-mauritius-water-sales-by-tariff-of-subscriber-685f5471](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-water-sales-by-tariff-of-subscriber-685f5471)
+
+## 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
+
+- Analyze seasonal or annual patterns
+- Join with agriculture or health data
+- Map geographic exposure
+- Check missingness before modeling
+- Use `country_iso3` as the safest geography join key when present
+
 ## Citation
 
 ```bibtex
 @misc{electric_sheep_africa_africa_mauritius_water_sales_by_tariff_of_subscriber_685f5471_2022,
-  title        = {Water sales by tariff of subscriber | Africa (Mauritius official open data)},
+  title        = {Water Sales by Tariff of Subscriber | Africa (MDPA)},
   author       = {MDPA},
   year         = {2022},
   url          = {https://data.govmu.org/dataset/water-sales-tariff-subscriber},
-  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-water-sales-by-tariff-of-subscriber-685f5471}}
 }
 ```
@@ -161,20 +230,14 @@ if "indicator_id" in df.columns:
 
 Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/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/9584b934-20df-4dd5-98e7-e3ff15f7aa8e/resource/ff1be866-1f8f-4c16-af99-f14cd51ddfb5/download/energy_water_yr22_060623_source_file.xlsx
+Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/water-sales-tariff-subscriber