data = np.array([1, 2, np.nan, 4, 5])

# Check for NaN
has_nan = np.isnan(data)  # [False False True False False]

# Remove NaN
clean_data = data[~np.isnan(data)]  # [1. 2. 4. 5.]

# NaN-aware functions
mean_ignore_nan = np.nanmean(data)  # 3.0