Handling overflow issues when performing element-wise operations on large NumPy arrays
I recently switched to I'm currently working with an scenario when performing element-wise operations on large NumPy arrays, specifically with integer overflow. I have two large arrays of data, and I'm trying to compute their element-wise sum and store the result in a new array. However, I keep working with unexpected results due to overflow. Here's a snippet of my code that illustrates the question: ```python import numpy as np # Creating a large array of int32 type arr1 = np.full((10000, 10000), np.int32(2000000000)) arr2 = np.full((10000, 10000), np.int32(3000000000)) # Attempting to compute the element-wise sum result = arr1 + arr2 print(result) ``` When I run this code, I expect to see an array filled with values, but instead, I get an array filled with negative numbers due to overflow. The output is: ```plaintext [[-1610612736 -1610612736 ... -1610612736] [-1610612736 -1610612736 ... -1610612736] ... ] ``` I tried changing the data type of the result array to `np.int64`, thinking that would help: ```python result = np.empty_like(arr1, dtype=np.int64) result = arr1 + arr2 ``` However, I'm still seeing unexpected results. Is there a better way to handle this situation? Should I use a different approach for the element-wise operation to avoid this scenario? I'm using NumPy version 1.21.0. Any insights or best practices would be greatly appreciated! I'm developing on macOS with Python. I'd love to hear your thoughts on this.