Analyzing 2013 Toulouse Airport Weather Data with Python, pandas, and SciPy
This tutorial demonstrates how to import, clean, and explore 2013 weather observations from Toulouse Airport using Python libraries such as pandas and SciPy, perform consistency checks, visualize temperature trends, assess variable correlations, and fit probability distributions—including normal, log‑normal, and Weibull—to the data.
Conclusion
Compared with the log‑normal distribution, the Kolmogorov‑Smirnov distance for the Weibull fit is slightly larger. Q‑Q plots show that, aside from an extreme outlier, the Weibull distribution matches the high‑wind‑speed tail better. For optimization focused on the most common wind speeds, Weibull is preferable; for safety‑critical scenarios where extreme wind speeds matter, the log‑normal model provides a better prediction of the highest observed speeds.
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