added historical file & validation rule in the code

[niveditasing]

Added Historical data to prevent data loss, the script now merges the fresh output with a historical data file to fill in any missing gaps. If a record exists in both files, the script follows a validation rule that prioritizes the output generated data, to ensures we keep a complete, up-to-date data without losing older historical records.

[gemini-code-assist]

Summary of Changes

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This pull request introduces a robust data management strategy by incorporating historical GDP data into the existing processing workflow. The primary goal is to ensure the completeness and accuracy of the dataset by merging new information with an archive of past records, while intelligently resolving conflicts to maintain the most current data. This enhancement safeguards against data loss and provides a more comprehensive view of regional statistics.

Highlights

• Historical Data Integration: A new historical data file (historical_deleted.csv) has been added to prevent data loss and preserve older records.
• Data Merging Logic: The data processing script now includes a mechanism to merge fresh output with the historical data, ensuring a complete dataset.
• Validation Rule Implementation: A validation rule has been established where fresh output is prioritized as the source of truth for any overlapping records between the new and historical data.

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[gemini-code-assist]

Code Review

This pull request adds historical GDP data for NUTS regions in Switzerland and Romania by introducing the historical_deleted.csv file. The import_data.py script is updated to include a new merge_historical_data function that integrates this historical data, prioritizing new data over historical data in case of overlaps. A critical bug was identified in the merge_historical_data function where the groupby(...).first() pattern with as_index=False will raise an AttributeError; using drop_duplicates(subset=['geo', 'time'], keep='first') is suggested as a more robust solution.

[pulkeet-wq]

LGTM