Beware of This Unexpected Behaviour of NumPy Methods

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If a NumPy array contains NaNs, NumPy's aggregate functions (np.mean, np.min, np.max, etc.) return NaN.

But this may not be desired at times.

One solution is to replace the NaN entries with a default value (0).

However, NumPy also provides nan-insensitive methods, such as np.nansum, np.nanmin, etc.

As a result, the output isn't influenced by the presence of NaNs.

👉 Over to you: What are some other unexpected behaviors of NumPy that you are aware of?

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