👀 Views: 77 💬 Answers: 1 📅 Created: 2025-06-08

numpy floating-point precision Python

I'm trying to figure out I'm collaborating on a project where I'm experiencing an unexpected behavior when comparing results from `np.arange` and `np.linspace` in NumPy 1.25, particularly when it comes to floating-point precision... I expected both methods to produce similar results under certain conditions, but they are behaving differently, leading to issues in my calculations. Here's a snippet of my code: ```python import numpy as np # Using np.arange start = 0.0 stop = 1.0 step = 0.1 array_arange = np.arange(start, stop, step) # Using np.linspace num_points = 10 array_linspace = np.linspace(start, stop, num_points) print("np.arange:", array_arange) print("np.linspace:", array_linspace) ``` When I run this code, I get: ``` np.arange: [0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9] np.linspace: [0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ] ``` As you can see, `np.arange` stops at `0.9` while `np.linspace` goes all the way to `1.0`. I initially thought that since the step in `np.arange` was `0.1`, it would include the endpoint, but I believe `np.arange` is based on a float comparison which excludes the endpoint. I've tried adjusting the parameters in both functions, but the behavior remains consistent. As a workaround, I'm currently using `np.linspace(start, stop, num_points + 1)` to ensure I get the endpoint included, but I would like to understand if there’s a best practice here, especially for larger datasets where precision matters. Can anyone clarify why this discrepancy occurs and whether there's a recommended approach for scenarios like this? I'd really appreciate any guidance on this. Could someone point me to the right documentation?