Issue with np.nanmean returning incorrect results in masked arrays
I'm optimizing some code but I'm working with a masked NumPy array and using `np.nanmean` to calculate the mean while ignoring NaN values..... However, I'm seeing unexpected results compared to what I would calculate manually. I've tried creating both a masked array and a regular array with NaNs, but the output from `np.nanmean` doesn't seem right. Here's a snippet of my code: ```python import numpy as np # Create a 2D array with some NaN values arr = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]]) # Create a masked array, masking NaN values masked_arr = np.ma.masked_invalid(arr) # Using np.nanmean on the regular array mean_without_mask = np.nanmean(arr) print("Mean without mask (should ignore NaNs):", mean_without_mask) # Using np.nanmean on the masked array mean_with_mask = np.nanmean(masked_arr) print("Mean with mask:", mean_with_mask) ``` When I run this code, I get the following output: ``` Mean without mask (should ignore NaNs): 5.0 Mean with mask: 5.0 ``` However, I expected the mean with the masked array to account for the masked values differently. After checking the masked array, it seems like it is correctly masking the NaNs, yet the results are the same. I also confirmed that the structure of my masked array is correct: ```python print(masked_arr) ``` This prints: ``` [[1.0 2.0 --] [4.0 -- 6.0] [7.0 8.0 9.0]] ``` I'm using NumPy version 1.23.1. Am I misunderstanding how `np.nanmean` works with masked arrays? Should I be using a different approach to get the desired mean value while ignoring the masked entries? Any insights would be greatly appreciated! For context: I'm using Python on Ubuntu. The stack includes Python and several other technologies. I'm developing on Ubuntu 20.04 with Python.