Strange behavior when expanding dimensions of a NumPy array with NaN values
After trying multiple solutions online, I still can't figure this out. I've hit a wall trying to I'm refactoring my project and I'm working with unexpected behavior when trying to expand the dimensions of a NumPy array that contains NaN values. I'm using NumPy version 1.21.0, and I have the following 1D array: ```python import numpy as np array = np.array([1, 2, np.nan, 4]) ``` When I try to expand its dimensions using `np.newaxis`, my expectation was to get a 2D array. However, when I do: ```python expanded = array[np.newaxis, :] print(expanded) ``` I get the following output: ``` [[ 1. 2. nan 4.]] ``` This works as expected, but when I try to perform an operation like summing along the new axis: ```python result = np.sum(expanded, axis=0) print(result) ``` I end up with: ``` [nan nan nan nan] ``` I expected the sum to ignore the NaN value and just return `7`. I read that using `np.nansum()` could help, but when I tried: ```python result_nansum = np.nansum(expanded, axis=0) print(result_nansum) ``` I still received an behavior: ``` ValueError: want to perform reduce with flexible type ``` The `expanded` array seems to have a flexible dtype, possibly due to the presence of NaN. I've tried explicitly converting the array to a float type when creating it: ```python array = np.array([1, 2, np.nan, 4], dtype=float) ``` But the scenario continues. What is the right way to handle this? How can I achieve the summation correctly while taking NaN values into account? For context: I'm using Python on macOS. I'd love to hear your thoughts on this. I've been using Python for about a year now.