The NumPy library doesn't utilize Python's C layer for its memory management.
Instead, it maintains its own memory space. Consequently, transferring data from the Python environment into NumPy or vice versa is relatively slow.
The process of opening a file and travesing its data within Python relies heavily on the C code behind the scenes, resulting in near-C performance.
However, if one were to write an algorithm along the lines of LeetCode - one that has a time complexity of n*2 - Python's performance will be slower compared to other languages. This difference could range from a factor of one to potentially even a hundred.
Instead, it maintains its own memory space. Consequently, transferring data from the Python environment into NumPy or vice versa is relatively slow.
The process of opening a file and travesing its data within Python relies heavily on the C code behind the scenes, resulting in near-C performance.
However, if one were to write an algorithm along the lines of LeetCode - one that has a time complexity of n*2 - Python's performance will be slower compared to other languages. This difference could range from a factor of one to potentially even a hundred.