How to implement guide with np.where returning unexpected results for non-boolean conditions
I'm building a feature where I've looked through the documentation and I'm still confused about I'm working with an unexpected behavior while using `np.where` with conditions that aren't strictly boolean. My goal is to replace values in a 1D NumPy array based on a condition that checks if the elements are greater than a certain threshold. Here's the code I've written: ```python import numpy as np data = np.array([1, 2, 3, 4, 5]) threshold = 3 result = np.where(data > threshold, data, 'below') print(result) ``` I expected the output to be an array like `[ 'below', 'below', 'below', 4, 5 ]`, but instead, I got: ``` ['below' 'below' 'below' 4 5] ``` The question seems to be that NumPy is treating the resulting array as a single type, and since the original array is of type `int`, it promotes the entire array to a string type. This is confusing because I assumed that the data type could remain mixed. I've tried explicitly converting the results to an object type using `dtype=object`, but it still returns inconsistent behavior: ```python result = np.where(data > threshold, data.astype(object), 'below') ``` This still results in a homogeneous array, where all elements are strings. Is there a way to handle this such that I maintain the original integer types for elements that meet the condition while replacing the others with a string? Or is there a better approach to achieve this functionality without losing the original integer types? I'm using NumPy version 1.22.0. The stack includes Python and several other technologies.