implementing np.where when using masked arrays in NumPy 1.23
I'm experimenting with I'm stuck on something that should probably be simple. I'm experiencing unexpected behavior with `np.where` when applied to a masked array in NumPy 1.23. I have a masked array containing both valid and invalid (masked) entries, and I want to replace the masked entries with a specific value while keeping the valid entries unchanged. Here’s the code I’m working with: ```python import numpy as np # Create a masked array data = np.array([1, 2, 3, 4, 5]) masked_data = np.ma.masked_array(data, mask=[0, 1, 0, 0, 1]) # Try to replace masked values with -1 result = np.where(masked_data.mask, -1, masked_data) print(result) ``` I expected the output to be `array([-1, 2, -1, 4, -1])`, but instead, I’m getting `array([-1, 2, 3, 4, -1])`. It seems like the `np.where` function is treating the valid entries differently than I anticipated. I also tried using `masked_data.filled(-1)` before applying `np.where`, but that didn’t produce the desired outcome either, since it replaced all masked values without keeping the original entries intact. Is there a different approach to achieve my goal without losing the valid entries or doing unnecessary copies of the array? Any insights would be greatly appreciated! How would you solve this? I'm on Windows 11 using the latest version of Python. Am I approaching this the right way? This is for a REST API running on CentOS. Thanks, I really appreciate it!