advanced patterns when using np.where with a boolean mask and NaN values in NumPy
Hey everyone, I'm running into an issue that's driving me crazy. I tried several approaches but none seem to work. I'm working with unexpected behavior when using `np.where` with a boolean mask that includes `NaN` values. Specifically, I'm trying to replace values in an array based on a condition, but the presence of `NaN` in the mask seems to be leading to incorrect output. Here’s a simplified version of my code: ```python import numpy as np # Sample data arr = np.array([1, 2, np.nan, 4, 5]) mask = np.array([True, False, np.nan, True, False]) # Attempting to replace values using np.where result = np.where(mask, arr, 0) print(result) ``` I expected the output to replace values where `mask` is `True` with the corresponding values from `arr`, and the rest should be `0`. However, I'm getting: ``` array([ 1., 0., 0., 4., 0.]) ``` It seems like the `NaN` in the mask is being treated as `False`, but I need to find any documentation that clarifies this behavior. I've tried explicitly converting the `NaN` values in the mask to `False` before using `np.where`: ```python mask_cleaned = np.nan_to_num(mask, nan=False) result = np.where(mask_cleaned, arr, 0) print(result) ``` This gave me the expected output of `array([ 1., 0., 0., 4., 0.])`, but it feels hacky. Is there a more straightforward or cleaner way to handle this situation? I’m using NumPy version 1.23.2, and I want to ensure my solution is robust, especially when dealing with larger datasets that may contain `NaN` values. Any insights would be appreciated! Am I missing something obvious?