implementing np.mean on masked arrays leading to unexpected NaN results in NumPy 1.24.3
I'm sure I'm missing something obvious here, but I'm working on a project and hit a roadblock. I've looked through the documentation and I'm still confused about This might be a silly question, but I'm working with an scenario when working with masked arrays in NumPy 1.24.3. I have a 2D masked array where some values are masked (i.e., not considered in computations), but when I try to calculate the mean using `np.mean`, I'm getting unexpected `NaN` results instead of the average of the unmasked values. Here's a snippet of the code I've written: ```python import numpy as np # Creating a 2D masked array data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) masked_data = np.ma.masked_array(data, mask=[[0, 0, 1], [0, 1, 0], [1, 0, 0]]) # Trying to compute the mean mean_value = np.mean(masked_data) print('Mean of masked array:', mean_value) ``` I expected the `mean_value` to give me the average of the values that are not masked, which should be `(1 + 2 + 4 + 6 + 8) / 5 = 4.2`. However, I am getting `nan` as a result. I also tried using `np.ma.mean(masked_data)` instead, but that returns the same result. I checked the documentation and it mentions that `np.mean` should handle masked arrays correctly. I am also aware that the default behavior of `np.mean` does not ignore `NaN` values. Could this be related to how I created the masked array, or is there something else I might be missing? Any insights would be appreciated. I'm working on a application that needs to handle this. What am I doing wrong? For context: I'm using Python on macOS. Any help would be greatly appreciated! This is part of a larger web app I'm building. What's the best practice here? The project is a service built with Python. Thanks for taking the time to read this!