electricsheepafrica/africa-mauritius-percentage-water-level-by-year-month-and-reservoir-de3b50ad

Percentage Water Level by Year Month and Reservoir | Africa (MDPA)

20 rows - 1 Africa country/area - 2021-2022 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 20 rows from MDPA, covering Percentage Water Level by Year Month and Reservoir. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Climate and environment datasets help analysts study exposure, resource conditions, environmental pressure, and climate-related trends.

Source-provided context: the data shows percentage water level by year, by month and by reservoir for the year 2021 and 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	20
Countries/areas	1
First period	2021
Last period	2022
Indicators	0
Columns	70
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	20	2021	2022	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.	eab8ad8c-5b0a-4005-a4de-da0fbb19f7c6:tab14:0
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.	TAB14
jan	string	Source column from the original resource.	Feb
d_59_3859502753212	double	Source column from the original resource.	62.549846348517725
d_54_34924078091106	double	Source column from the original resource.	61.71691973969632
d_62_55965292841649	double	Source column from the original resource.	63.28633405639913
d_70_58046115529847	double	Source column from the original resource.	92.79779307743092
d_63_579175704989154	double	Source column from the original resource.	74.90238611713666
d_75_96529284164858	double	Source column from the original resource.	95.88937093275489
d_60	double	Source column from the original resource.	65.0
d_70_72674332234008	double	Source column from the original resource.	67.57805458993175
d_68_17303978370026	double	Source column from the original resource.	63.31788335264581
d_73_34878331402085	double	Source column from the original resource.	70.33603707995366
d_69_36898273321603	double	Source column from the original resource.	97.29457403395637
d_63_15179606025493	double	Source column from the original resource.	72.53765932792584
d_74_43028196214755	double	Source column from the original resource.	100.0
d_23	double	Source column from the original resource.	30.0
d_29_7943376068376	double	Source column from the original resource.	37.37340856481481
d_22_048611111111114	double	Source column from the original resource.	36.28472222222222
d_35_24305555555555	double	Source column from the original resource.	38.54166666666667
d_53_433641975308646	double	Source column from the original resource.	65.40232487922707
d_52_864583333333336	double	Source column from the original resource.	52.951388888888886
d_54_25347222222222	double	Source column from the original resource.	71.875
d_32	double	Source column from the original resource.	48.0
d_79_92405683488487	double	Source column from the original resource.	79.4984076433121
d_75_79617834394904	double	Source column from the original resource.	77.54777070063695
d_82_16560509554141	double	Source column from the original resource.	80.4140127388535
d_76_75748997405046	double	Source column from the original resource.	98.137635004154
d_72_45222929936304	double	Source column from the original resource.	80.09554140127389
d_81_21019108280254	double	Source column from the original resource.	100.0
source_period_start_year	int64	Start year inferred from source metadata.	2021
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.	2021-2022
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.	Percentage water level by year, month and reservoir
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.	c1537c98-cc38-44b3-9d8d-bb9b0f7a05f1
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	eab8ad8c-5b0a-4005-a4de-da0fbb19f7c6
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/c1537c98-cc38-44b3-9d8d-bb9b0f7a05f1/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
sep	string	Source column from the original resource.	``
d_89	double	Source column from the original resource.	``
d_88_03548795944232	double	Source column from the original resource.	``
d_80_60836501901142	double	Source column from the original resource.	``
d_93_15589353612168	double	Source column from the original resource.	``
d_60_67807351077311	double	Source column from the original resource.	``
d_54_94296577946769	double	Source column from the original resource.	``
d_70_15209125475286	double	Source column from the original resource.	``
d_81	double	Source column from the original resource.	``
d_98_6236171854901	double	Source column from the original resource.	``
d_96_98996655518394	double	Source column from the original resource.	``
d_99_66555183946487	double	Source column from the original resource.	``
d_85_84567606306737	double	Source column from the original resource.	``
d_82_94314381270902	double	Source column from the original resource.	``
d_89_29765886287625	double	Source column from the original resource.	``
d_99_12217194570134	double	Source column from the original resource.	``
d_98_78431372549021	double	Source column from the original resource.	``
d_99_68627450980392	double	Source column from the original resource.	``
d_93_79971988795519	double	Source column from the original resource.	``
d_90_0392156862745	double	Source column from the original resource.	``
d_97_05882352941177	double	Source column from the original resource.	``
d_99_24692516155929	double	Source column from the original resource.	``
d_98_03523035230353	double	Source column from the original resource.	``
d_100	double	Source column from the original resource.	``
d_83_57046070460704	double	Source column from the original resource.	``
d_78_5230352303523	double	Source column from the original resource.	``
d_88_6178861788618	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-percentage-water-level-by-year-month-and-reservoir-de3b50ad")
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: Energy_Water_Yr22_060623_Source_file.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T16:30:55Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-percentage-water-level-by-year-month-and-reservoir-de3b50ad

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

@misc{electric_sheep_africa_africa_mauritius_percentage_water_level_by_year_month_and_reservoir_de3b50ad_2022,
  title        = {Percentage Water Level by Year Month and Reservoir | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/percentage-water-level-year-month-and-reservoir},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-percentage-water-level-by-year-month-and-reservoir-de3b50ad}}
}

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.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/percentage-water-level-year-month-and-reservoir