Handling integer division precision in Python 2.7 when using NumPy
I'm a bit lost with I'm a bit lost with I've looked through the documentation and I'm still confused about I'm working on a data analysis project using Python 2.7 and NumPy, and I've encountered an unexpected scenario with integer division that affects the precision of my calculations... When I perform division between two integers, I'm getting results that are rounded down instead of providing a float result. For example, I have the following code: ```python import numpy as np def calculate_ratio(a, b): return a / b result = calculate_ratio(5, 2) print(result) # Expected: 2.5, Actual: 2 ``` I'm aware that in Python 2.7, dividing two integers performs floor division, which isn't what I want for my calculations. I tried converting one of the integers to a float before the division: ```python result = calculate_ratio(float(5), 2) print(result) # Expected: 2.5, Actual: 2.5 ``` This works for the specific case, but when I extend this function to work with NumPy arrays, like so: ```python def calculate_ratio(a, b): return a / b array_a = np.array([5, 10, 15]) array_b = np.array([2, 4, 3]) results = calculate_ratio(array_a, array_b) print(results) # Expected: [2.5, 2.5, 5.0], Actual: [2, 2, 5] ``` The output is still rounding down due to the integer division, even though I expected NumPy to handle this better. I've looked into using `np.float64` to force a float conversion on the arrays: ```python array_a = np.array([5, 10, 15], dtype=np.float64) array_b = np.array([2, 4, 3], dtype=np.float64) results = calculate_ratio(array_a, array_b) print(results) # Expected: [2.5, 2.5, 5.0], Actual: [5. 5. 5.] ``` Now, the results are completely incorrect. I feel exploring trying to ensure that my division performs as expected regardless of the input types. Does anyone have an effective way to handle integer division precision with NumPy arrays in Python 2.7? Any insights on best practices for this would be greatly appreciated! Any ideas what could be causing this? This is part of a larger web app I'm building. Any ideas what could be causing this? Has anyone else encountered this?