README.md

---
license: cc-by-sa-4.0
language:
- en
task_categories:
- tabular-regression
- time-series-forecasting
multilinguality: multilingual
size_categories:
- n<1K
tags:
- "tabular"
- "africa"
- "open-data"
- "official-statistics"
- "mauritius"
- "mdpa"
- "health"
- "health-and-sports"
- "immunisation"
- "polio"
- "vaccine"
- "tetanus"
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-00000-of-00001.parquet
pretty_name: "Immunisations Reported by Government Services | Africa (MDPA)"
---

# Immunisations Reported by Government Services | Africa (MDPA)

**140 rows** - **1 Africa country/area** - **2018-2022** - **1 indicator** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![rows](https://img.shields.io/badge/rows-140-blue)
![countries](https://img.shields.io/badge/countries-1-green)
![period](https://img.shields.io/badge/period-2018--2022-orange)
![indicators](https://img.shields.io/badge/indicators-1-purple)
![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey)

## TL;DR

This dataset contains **140 rows** from **MDPA**, covering **Immunisations Reported by Government Services**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

## What This Dataset Measures

Health datasets help analysts monitor disease burden, service delivery, population health outcomes, and public-health program performance.

Source-provided context: Data shows number of Immunisations Reported by Government Services for the year 2018 to 2022

## 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 | 140 |
| Countries/areas | 1 |
| First period | 2018 |
| Last period | 2022 |
| Indicators | 1 |
| Columns | 20 |
| Source format | CSV |

## Geographic Coverage

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

| Area | Rows | First year | Last year | Name |
|------|-----:|-----------:|----------:|------|
| `MU` | 140 | 2018 | 2022 | `Mauritius` |

## Indicators, Variables, Or Resource Contents

- `immunisations-reported-by-government-services-8a9bb36a` - Immunisations Reported by Government Services(source_units_unspecified)

## Schema

| Column | Type | Description | Example |
|--------|------|-------------|---------|
| `indicator_id` | `string` | Stable source or Electric Sheep Africa indicator identifier. | `immunisations-reported-by-government-services-8a9bb36a` |
| `indicator_name` | `string` | Human-readable indicator name. | `Immunisations Reported by Government Services` |
| `country_iso3` | `string` | ISO3 country or area code. | `MU` |
| `country_name` | `string` | Country or area name. | `Mauritius` |
| `year` | `int64` | Observation year. | `2018` |
| `value` | `double` | Numeric observation value. | `10310.0` |
| `unit` | `string` | Measurement unit, when supplied by the source. | `source_units_unspecified` |
| `dimension_disesase_immunized_against` | `string` | Source dimension retained during long-form normalization. | `tuberculosis` |
| `dimension_dose` | `string` | Source dimension retained during long-form normalization. | `` |
| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2018` |
| `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. | `2018-2022` |
| `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. | `Immunisations Reported by Government Services` |
| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `immunisations-reported-by-govt-services_2018_2022.csv` |
| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `f0915803-f07c-4279-bb5f-d9ca746b39c0` |
| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `f3be027c-c16d-4a07-984f-e9cdb0f1818a` |
| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://data.govmu.org/dataset/f0915803-f07c-4279-bb5f-d9ca746b39c0/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

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-immunisations-reported-by-government-services-5692556c")
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

- 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/immunisations-reported-government-services)
- **Publisher:** MDPA
- **Portal:** [https://data.govmu.org](https://data.govmu.org)
- **Resource:** [immunisations-reported-by-govt-services_2018_2022.csv](https://data.govmu.org/dataset/f0915803-f07c-4279-bb5f-d9ca746b39c0/resource/f3be027c-c16d-4a07-984f-e9cdb0f1818a/download/immunisations-reported-by-govt-services_2018_2022.csv)
- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
- **Retrieved/generated:** `2026-08-08T17:08:52Z`
- **Hugging Face repo:** [electricsheepafrica/africa-mauritius-immunisations-reported-by-government-services-5692556c](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-immunisations-reported-by-government-services-5692556c)

## 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

- Compare health outcomes across geographies
- Track changes over time
- Join with population or facility 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_immunisations_reported_by_government_services_5692556c_2022,
  title        = {Immunisations Reported by Government Services | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/immunisations-reported-government-services},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-immunisations-reported-by-government-services-5692556c}}
}
```

## License

Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/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/immunisations-reported-government-services