Cleaning data with NaNs involves identifying their locations and choosing appropriate handling methods. Techniques include filtering NaNs using boolean indexing, imputing missing values with mean or median, and using masks for selective operations. NumPy functions like np.nanmean and np.nanmedian are essential for reliable computations in incomplete datasets.