⚡ Bolt: Replace iterrows with itertuples for faster DataFrame iteration

[alinelena]

💡 What:
Replaced iterrows() with itertuples() and to_dict('records') in Pandas DataFrame iterations across multiple calculation scripts (calc_elasticity.py, gscdb138.py, calc_MPCONF196.py, calc_solvMPCONF196.py).

🎯 Why:
Pandas iterrows() is a known performance bottleneck in Python data processing because it wraps each row in a pd.Series, generating massive overhead in loops. By switching to itertuples() (which returns named tuples or standard tuples) and to_dict('records') (which returns native Python dictionaries), we bypass the Series overhead completely without sacrificing readability.

📊 Impact:
Iterating over rows becomes significantly faster (often 10x-50x quicker depending on dataframe size and contents). This directly accelerates the setup and processing stages in benchmark tests and analyses.

🔬 Measurement:
To verify, one can run benchmarking scripts using timeit comparing df.iterrows() vs df.itertuples() vs df.to_dict('records') on sample dataframes. Also, existing calculations like MPCONF196 will noticeably complete their setup/iteration loops in less time.

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PR created automatically by Jules for task 14578147934914453641 started by @alinelena

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