IndexError when using np.ix_ for advanced indexing with boolean masks
Quick question that's been bugging me - I'm trying to perform advanced indexing using NumPy's `np.ix_` function alongside boolean masks, but I'm working with an `IndexError`. I have a 2D array and a boolean array, and I want to select specific rows and columns. Here's the code I've written: ```python import numpy as np # Sample 2D array array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Boolean mask for rows row_mask = np.array([True, False, True]) # Attempting to use np.ix_ to select rows and specific columns try: selected = array[np.ix_(row_mask, [0, 2])] print(selected) except IndexError as e: print(f"IndexError: {e}") ``` When I run this code, I get an `IndexError: index 2 is out of bounds for axis 1 with size 3`. I assumed that `np.ix_` would handle the boolean array correctly, but it seems I'm misunderstanding how it works. Can anyone explain why this is happening and how I can correctly apply the boolean mask to select specific rows and columns without triggering this behavior? I also checked the dimensions of `array` and confirmed it's (3, 3), which made me think it should work. Any insights would be appreciated! I'm working on a web app that needs to handle this. Am I missing something obvious? I'm working on a application that needs to handle this. How would you solve this?