np.linalg.inv() leads to array full of np.nan-CodePudding

The

I don't understand why that's the case. I've tried to replace all np.nan values in A (although there shouldn't be any) via A[np.isnan(A)] = 0 but the problem persists.

Any suggestions?

CodePudding user response：

The problem is in A.

There could be nan values in the dataframe. The matrix A could be singular, please check if np.linalg.det(A) is not 0

Then I would pass to the function np.linalg.inv a numpy array using pd.DataFrame.to_numpy (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html)

CodePudding user response：

The reason is an np.inf value in at least column 293. The np.inf value can be replaced via A[np.isinf(A)] = 0. If np.inf is replaced with zero, there are no np.nan values in L.