implementing np.where returning unexpected results for boolean masking in NumPy 1.25
I'm having trouble with I've been researching this but I'm working with unexpected behavior when using `np.where` for boolean masking in my NumPy arrays. Specifically, I have a 2D array and I'm trying to extract values based on a condition, but the output is not matching my expectations. Here's the code snippet I used: ```python import numpy as np # Create a sample 2D array array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Define a condition to find values greater than 5 condition = array > 5 # Use np.where to get indices of elements satisfying the condition indices = np.where(condition) # Extract values using the indices result = array[indices] print("Extracted values:", result) ``` This should return the values greater than 5, but instead, I get the output: ``` Extracted values: [6 7 8 9] ``` Which is technically correct, but I am surprised by the format of the output. I was expecting `np.where` to return a 1D array of values directly without needing to index the original array. Additionally, I noticed that if I try to reshape the result: ```python reshaped_result = result.reshape(-1, 1) ``` I get a `ValueError: want to reshape array of size 4 into shape (4,1)` when I expect it to create a 2D column vector. Could anyone clarify whether this is the expected behavior of `np.where`, and how to properly handle the extraction of values directly as a 1D array? Am I missing something in my understanding or usage of these functions? Thanks in advance! My team is using Python for this microservice. Is this even possible? The stack includes Python and several other technologies.