👀 Views: 1 💬 Answers: 1 📅 Created: 2025-06-10

numpy mean array Python

I'm wondering if anyone has experience with I'm encountering unexpected results when using `np.mean` on a 3D NumPy array with the `axis` parameter. My intention is to calculate the mean across the second axis, but the output seems incorrect and does not match my manual calculations. Here's the code I've been using: ```python import numpy as np # Creating a 3D array with random values array_3d = np.random.rand(4, 3, 5) # Calculating the mean across the second axis (axis=1) mean_result = np.mean(array_3d, axis=1) print(mean_result) ``` I expected the shape of `mean_result` to be `(4, 5)` since I'm averaging the values across the three elements of the second dimension. However, the output shape is `(4, 5)` but the values seem significantly off. It looks like the mean calculation is taking into account different data than I intended. I verified that the input array has the expected values and dimensions: ```python print(array_3d.shape) # Should output (4, 3, 5) print(array_3d) # Check the actual content of the array ``` I also tried different `axis` values and even used `np.mean(array_3d.flatten())`, but I still get results that don't seem to make sense compared to my expectations. Does anyone have insight into why this might be happening? Is there something specific about how `np.mean` handles 3D arrays that I might be overlooking? Any help would be appreciated!