Missing Values (NaN)

Real data has gaps. NumPy marks a missing number as NaN ("not a number"). Any normal calculation that touches a NaN becomes NaN, which can silently spoil a result. Use np.isnan to find them, and the nan versions of the aggregates (np.nanmean, np.nansum, np.nanmax) to skip them. Test for NaN with np.isnan, never with ==, because NaN is not equal even to itself.

Examplepython

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Quick check

Why does `np.mean` of an array containing NaN return NaN?

Any arithmetic with NaN gives NaNNaN is zeroThe array is empty

Your turn: write the code

Print the largest reading, ignoring the missing values.

Finish the quick check (and any code exercise) to complete this lesson, or mark it done yourself.

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