While Python's interpreted nature means that pure Python loops are slow, the NumPy/SciPy stack side-steps this by vectorizing operations. As a rule of thumb, we expect compiled code (like the routines in NumPy and SciPy) to be at least faster than pure Python code for heavy numerical tasks. However, for extremely high-frequency or latency-sensitive applications, integrating C or Fortran code remains an option.
NumPy introduces the N-dimensional array object ( ndarray ). This object allows Python to store and manipulate massive datasets in contiguous memory blocks. It replaces the custom vector and matrix structures used in old C or Fortran recipes.
C code uses nested for loops. Python ( NumPy ) excels when you replace loops with vectorized operations ( arr1 + arr2 instead of for i in range... ).
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