What is the fastest method to perform vectorized operations on a NumPy array with np.nan values?

Below is an explanation based on NumPy’s documentation and observed behavior:

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Which Approach Is the Quickest?

When using an array like your arr_nans that already contains np.nan for invalid values, NumPy’s vectorized arithmetic still runs at C speed. That is why your test

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test_nans = timeit.timeit('arr_nans * 5 / 2.123', globals=globals(), number=1000)

is about as fast as the normal array operation. In many cases you can leverage functions such as np.nanmean or np.nansum which already ignore NaNs.

If you need to perform arbitrary operations only on the valid entries, the quickest options are usually:

• Use vectorized operations on the full array: Let operations run even if some positions are np.nan (if the math works normally).
• Use boolean indexing to select valid elements: For example,

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  valid = ~np.isnan(arr_nans)
  result = np.empty_like(arr_nans)
  result[valid] = arr_nans[valid] * 5 / 2.123

  In this way, you only compute on valid data and avoid the overhead of a mask object.

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Why Are Masked Arrays Slower?

The performance drop you observed with:

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masked_arr_nans = np.ma.array(arr_nans, mask=np.isnan(arr_nans))
test_masked_nans = timeit.timeit('masked_arr_nans * 5 / 2.123', globals=globals(), number=1000)

can be explained by the following:

• Overhead of Mask Management: Masked arrays, provided by np.ma, add a layer where every arithmetic operation checks not only the core data but also the associated mask. This extra masking logic introduces additional overhead.
• Python-Level Overhead: Masked arrays are a subclass of ndarray with additional attributes and methods. The overhead isn’t incurred by pure C-level vectorized operations on regular ndarrays.
• Propagation and Finalization of the Mask: After each arithmetic operation, NumPy must compute the new mask (or propagate it), which results in extra work compared to nan-based operations.

Thus, if performance is key and you already encode invalid data as np.nan, it is more efficient to work directly with the underlying ndarray and let NumPy skip the NaNs (or use functions that are designed to ignore NaNs).

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Summary

• Quickest Way: Use vectorized operations on arrays with np.nan or use boolean indexing to extract valid entries.
• Why Masked Arrays Are Slower: They introduce extra overhead from managing the mask during each operation, which slows down performance compared to operations on regular ndarrays.

This approach is based on how NumPy’s masked arrays are implemented and documented in the NumPy manual.