How to efficiently compute the weighted average of a 2D NumPy array with NaN values?
I've searched everywhere and can't find a clear answer... I've looked through the documentation and I'm still confused about I tried several approaches but none seem to work... I'm trying to compute the weighted average of a 2D NumPy array that contains `NaN` values. The array represents some data over time, where each row corresponds to a different time point, and each column corresponds to different measurements. The weights are stored in another array of the same shape, but they also contain `NaN` values. I want to ensure that my calculation correctly ignores the `NaN` values. Here’s a simplified example of what I’m working with: ```python import numpy as np data = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]]) weights = np.array([[0.1, 0.2, np.nan], [0.3, 0.4, 0.5], [0.6, 0.7, 0.8]]) ``` I want to calculate the weighted average across the rows, excluding any `NaN` values in both `data` and `weights`. However, I'm struggling with how to approach this without resorting to loops, as I expect this to be computationally expensive given larger datasets. I tried using `np.nansum` to aggregate the values while ignoring `NaN`, but I’m not sure how to properly incorporate the weights in this context. Here’s what I’ve attempted so far: ```python weighted_sum = np.nansum(data * weights, axis=1) weights_sum = np.nansum(weights, axis=1) weighted_average = weighted_sum / weights_sum ``` However, this gives me a result of `nan` for some rows where all corresponding weights are `NaN`. I need a way to handle this gracefully, ideally returning a valid average or a defined fallback value in those cases. Is there a better approach to handle this scenario in NumPy? Any suggestions on how to improve my computation or handle the `NaN` values more efficiently would be greatly appreciated. Thanks in advance! For context: I'm using Python on Linux. What's the best practice here? This issue appeared after updating to Python 3.10. I'd really appreciate any guidance on this. I'm working in a Debian environment. What are your experiences with this?