Inconsistent behavior with np.linspace when generating large arrays in NumPy 1.24
I need some guidance on Does anyone know how to I'm having a hard time understanding I'm experiencing inconsistent behavior when using `np.linspace` to generate large arrays with specific endpoints... Specifically, when I try to create an array with 1,000,000 samples ranging from 0 to 1, I'm noticing that the last value is not exactly 1 as I expect, but rather something like 0.999999. Here’s the code I used: ```python import numpy as np large_array = np.linspace(0, 1, 1000000) print(large_array[-1]) # Should be 1.0 ``` Instead of getting `1.0`, I get `0.9999999999999999`. I understand that this may be due to floating-point precision issues, but I was hoping that `np.linspace` would handle this correctly, especially since the endpoint is supposed to be included. I’ve also tried using the `endpoint` parameter set to `True` explicitly, but it still gave me the same result. Is there a recommended way to ensure that the last value is exactly 1, or should I just round the values after generation? This behavior seems inconsistent, and I'm not sure if there's a better approach to take. I've also checked my NumPy version, and it's 1.24.2. Any insights or best practices for dealing with this kind of scenario would be appreciated! This issue appeared after updating to Python LTS. I'm coming from a different tech stack and learning Python. What's the correct way to implement this? I've been using Python for about a year now. Any advice would be much appreciated. I recently upgraded to Python LTS.