Confusion with np.sum and keepdims when summing along an axis with NaN values in NumPy 1.24.2
I just started working with I'm encountering unexpected behavior when using `np.sum` with the `keepdims=True` argument on a 2D array that contains `NaN` values... I expected that summing along, say, axis 0 would give me a 1D array where the corresponding spots for the `NaN` values would also result in `NaN`. Instead, I’m getting a strange output where some of the sums are valid numbers. Here’s the code I'm using: ```python import numpy as np array = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]]) sum_result = np.sum(array, axis=0, keepdims=True) print(sum_result) ``` The output I get is: ``` [[12. 10. 15.]] ``` I expected the second column to return `NaN` because it has a `NaN` value in the second row. I tried using `np.nansum` instead, which does ignore `NaN`s, but I want to keep the shape of the output array. Why is `np.sum` not behaving as I expected with `keepdims=True`? Is there a way to achieve the desired `NaN` behavior while keeping the dimensions intact? I’m using NumPy version 1.24.2 and have already double-checked my installation. Any insights would be greatly appreciated! Is there a better approach? This is happening in both development and production on Linux. Any suggestions would be helpful. This is part of a larger service I'm building.