| # Datacard for Mauritius Jobs And Development Indicators (1960-2024) |
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| This dataset contains a time-series of key jobs and development indicators for Mauritius, spanning from 1960 to 2024. The data has been aggregated from multiple sources, cleaned, and processed into a single, analysis-ready CSV file. |
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| The raw data was sourced from **The World Bank** data portal. The original files were provided in Excel (.xls) format. |
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| - **Temporal Coverage**: 1960-2024 |
| - **Geographic Coverage**: Mauritius |
| - **Format**: Comma-Separated Values (CSV) |
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| ## Data Points (Features) |
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| The dataset includes the following jobs and development indicators, with 'Year' serving as the primary date column: |
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| 1. `employment_to_population_ratio_15_total_modeled_ilo_estimate_`: Employment to population ratio, 15+, total (%) (modeled ILO estimate) |
| 2. `gdp_per_person_employed_constant_2021_ppp_`: GDP per person employed (constant 2021 PPP $) |
| 3. `labor_force_participation_rate_total_of_total_population_ages_15_modeled_ilo_estimate_`: Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate) |
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| ## Data Preparation & Missing Data Handling |
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| The raw data was processed using a Python script to transform it into a clean, structured format. The key steps were: |
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| 1. **Filtering**: The data was filtered to include only records for 'Mauritius'. |
| 2. **Reshaping**: The original wide-format data (years as columns) was melted into a long format. |
| 3. **Merging**: Data from all indicator files was merged into a single DataFrame on 'Year'. |
| 4. **Handling Missing Data**: Missing values (`NaN`) were filled using a two-step strategy: linear interpolation followed by a back-fill to handle any remaining gaps at the start of the series. |
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