Why does my nested for loop over a NumPy array give unexpected results in Python 3.10?
I need some guidance on I'm optimizing some code but I'm sure I'm missing something obvious here, but I have a NumPy array where I need to perform some computations on each element. I used a nested for loop to iterate through the array, but the results are not what I expected. Here’s the code I'm using: ```python import numpy as np arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) results = [] for i in range(arr.shape[0]): row_results = [] for j in range(arr.shape[1]): if arr[i][j] % 2 == 0: row_results.append(arr[i][j] ** 2) else: row_results.append(arr[i][j]) results.append(row_results) print(results) ``` I expect the output to be `[[1, 4, 3], [16, 25, 36], [7, 64, 9]]`, but I'm getting `[[1, 4, 3], [4, 25, 36], [7, 64, 9]]`. It seems like the second row is not getting processed correctly. I added print statements inside the inner loop to debug, and it shows that the condition is being evaluated, but the output is still off. I’ve also checked that I’m using NumPy version 1.21.0, and I’m running Python 3.10.0. What am I missing? Could there be an scenario with how I'm indexing the array, or is there a better way to achieve this using NumPy's built-in functions? For context: I'm using Python on Windows. Am I missing something obvious? My development environment is Ubuntu 22.04. Thanks for your help in advance!