advanced patterns with np.corrcoef when computing correlation on large datasets
I'm writing unit tests and I tried several approaches but none seem to work. I'm experiencing unexpected results when using `np.corrcoef` on a large dataset (around 1,000,000 rows) in NumPy 1.25. When I try to compute the correlation coefficients between multiple columns of my data, the results seem to be off, showing correlation values that don't make sense given the data. For example: ```python import numpy as np data = np.random.rand(1000000, 10) # 1,000,000 rows, 10 columns correlation_matrix = np.corrcoef(data, rowvar=False) print(correlation_matrix) ``` The output has several correlation values near 1 or -1, which I didn't expect. I tried reducing the dataset size by sampling 10,000 rows, and the results seem more reasonable. Could this be an scenario with how `np.corrcoef` handles large datasets? I also checked the data types and ensured they are all `float64`, which is what I expected. I read somewhere that large datasets might cause numerical instability or floating-point precision issues, but I'm not sure how to confirm if that's the case. Is there a best practice for using `np.corrcoef` with large datasets, or should I consider using an alternative method to compute correlations? Any insights or similar experiences would be greatly appreciated! My development environment is Ubuntu. What's the best practice here? The project is a service built with Python. Any pointers in the right direction?