implementing np.corrcoef returning NaN values for 2D arrays with missing data in NumPy 1.24.0
I'm collaborating on a project where I'm working on a personal project and I'm working with a 2D NumPy array that contains some missing values, represented as `np.nan`. When I try to compute the correlation coefficients using `np.corrcoef`, I'm getting `NaN` values in the output, which is unexpected. Here’s a snippet of the code: ```python import numpy as np # Sample data with missing values arr = np.array([[1, 2, 3], [4, np.nan, 6], [7, 8, np.nan]]) # Calculate correlation coefficients corr_matrix = np.corrcoef(arr, rowvar=False) print(corr_matrix) ``` In this case, I would expect the correlation coefficients to still be calculated for the remaining values, but instead, I get a matrix filled with `NaN` values. The output looks like this: ``` [[nan nan nan] [nan nan nan] [nan nan nan]] ``` I've tried using `np.nanmean` along with `np.corrcoef` to handle the missing values before calculating the correlation, but it seems to add unnecessary complexity and doesn’t resolve the scenario. Is there a better way to handle this? Also, any suggestions on how to effectively manage missing data in general when working with NumPy arrays would be appreciated. I'm using NumPy version 1.24.0. For context: I'm using Python on macOS. Is there a better approach?