如何定位ndarray[sliceobject] = ndarray操作的Numpy源码及确认时间复杂度

Let's break this down for you step by step—first the time complexity, then how to track down the relevant source code.

Time Complexity Breakdown #

First off: this operation can't be faster than O(k), where k is the number of elements in the target slice. Here's the breakdown:

• Even with NumPy's optimized C-backed operations, you still need to copy each element (or contiguous block of elements) from the source array to the sliced portion of the target array.
• If your slice only covers a subset of the original array (e.g., arr[10:20] = new_arr where arr has 1000 elements), then k is much smaller than n (the total size of arr), so the time complexity is effectively lower than O(n).
• For continuous slices, NumPy uses low-level memory copy operations (like memcpy) which are extremely efficient, but they still process exactly k elements. For non-continuous slices (e.g., fancy indexing with arr[[1,3,5]] = new_arr), the operation still runs in O(k) time, though with a small constant overhead due to non-contiguous memory access.

So to confirm: if you're only modifying a subset of the array, this operation absolutely runs in less than O(n) time.

Locating the Source Code #

NumPy's core array operations are implemented in C for speed, so you'll need to dig into the C source files rather than pure Python code. Here's how to find the relevant bits:

• Start with the __setitem__ entry point
  The Python-level ndarray.__setitem__ method maps to the C function PyArray_SetItem in numpy/core/src/multiarray/arrayobject.c. For slice assignments, this function delegates to specialized slicing logic instead of handling it directly.

• The assignment core: assignment.c
  Most array assignment logic (including slice assignments) lives in numpy/core/src/multiarray/assignment.c. Look for the array_assign function—it's the main entry point for handling all types of array assignments.

  • Inside array_assign, the code checks if the source and target are contiguous in memory. If they are, it uses fast block copies. If not, it falls back to element-wise copying loops.
  • Slice-specific handling includes validating slice bounds, calculating stride values, and determining the number of elements to copy.

• Helper slicing utilities
  Additional logic for parsing and validating slice objects lives in numpy/core/src/multiarray/slice.c, which handles converting Python slice objects into index ranges and stride calculations.

If you're checking the pure Python wrapper code, you can look at numpy/core/multiarray.py, but keep in mind this is just a thin layer over the C implementation.

内容的提问来源于stack exchange，提问作者Spazz