Guidance for using Pandas, NumPy, Statistics, and Machine Learning within the SimUCI context.
iterrows). Use vectorized operations for simulation data processing.
[row['a'] + row['b'] for _, row in df.iterrows()]df['a'] + df['b']NaN. Simulation data must be clean.
fillna() or dropna() with valid reasoning documented.pathlib for paths.utils/constants/limits.py if filtering during load.simuci.statsFriedman test for multiple group comparisons.Wilcoxon for paired comparisons.simuci.distribuciones for random variable generation (e.g., norm, expon).sklearn.pipeline.Pipeline for preprocessing + modeling.joblib in models/ directory.random_state (or seed) for stochastic models.