What is vectorization in NumPy and pandas?
Tests if you know why NumPy operations beat Python loops via contiguous memory and C-level SIMD. A strong answer defines vectorization as array-wide operations without explicit loops, contrasts a ufunc to a for-loop, and cites interpreter overhead removal.
Tests your understanding of how NumPy's contiguous homogeneous arrays enable optimized C-level vectorized operations that bypass Python interpreter overhead. A strong answer defines vectorization as array-wide operations without explicit Python loops, contrasts a NumPy ufunc against a manual for-loop, explains the gain comes from pre-compiled C loops and SIMD instructions on contiguous memory, and extends this pattern to pandas Series and DataFrames. Red flag: equating it with list comprehensions or multithreading.
Read the original → pythonlikeyoumeanit.com
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