np.arange with non-integer steps returns unexpected output for large ranges
I'm a bit lost with I need help solving Can someone help me understand I'm sure I'm missing something obvious here, but I'm working with an scenario when using `np.arange` with non-integer steps over a large range. Specifically, I'm trying to generate an array that increments by `0.1` from `0` to `1000`, but it seems like the last element is not what I expected. Here's the code I wrote: ```python import numpy as np start = 0 stop = 1000 step = 0.1 result = np.arange(start, stop, step) print(result[-5:]) # Print the last 5 elements ``` When I run this code, the output shows values like `999.5`, `999.6`, `999.7`, `999.8`, `999.9`, which is fine; however, I was expecting the last value to be exactly `1000`. Upon further examination, I noticed that the last value is actually `999.9` and not approaching `1000` due to floating-point precision. I tried switching to `np.linspace` to see if this would resolve the scenario: ```python result_linspace = np.linspace(start, stop, num=int((stop - start) / step) + 1) print(result_linspace[-5:]) ``` This indeed gives me the output I was expecting, including the `1000` as the last element. However, I want to understand the underlying reason why `np.arange` behaves this way with floating-point numbers. Is there a recommended best practice for using `np.arange` with floating-point numbers? Should I stick to `np.linspace` for such cases, or is there a way to ensure `np.arange` gives the expected last value? I'm using NumPy version 1.24.1, and I want to make sure I write robust code for similar scenarios in the future. For context: I'm using Python on Windows. I'm using Python 3.11 in this project. Could someone point me to the right documentation? I appreciate any insights! I'm working in a Linux environment.