Understanding LinAlgError Singular Matrix

Python programming language provides us with various libraries to deal with several numeric, vectorized data and perform operations. Using them prevents us from doing computationally expensive tasks and makes our work easier. One such library is Numpy. It is used to perform mathematical operations on array and matrices. Some of them are cross-multiplication, dot-product, inverse e.t.c. While operating these matrices sometimes, we get errors like LinAlgError Singular Matrix. This article will try to understand the error and find suitable solutions for it.

So, LinAlgError is raised by linear algebra class (named linalg) when some linear algebra function prevents the correct execution of other program parts. The singular matrix is the sub-error raised when we perform incorrect operations on the singular matrix.

What is LinAlgError Singular Matrix Error?

LinAlgError Singular Matrix Error

So, in the above image, you can see that the interpreter threw a LinAlgError: Singular matrix. It means that the error occurred because of some linear algebra operation that is computationally incorrect. Talking more descriptively, we can say that some operations on a singular matrix are because of some operations. The matrix may not support those operations.

Why do I get “LinAlgError Singular Matrix” Error?

The reason to get the error lies because we are doing those operations, which is not computationally possible. In the above case, the reason for the error is we want to inverse the matrix whose determinant is zero. Let’s see that.

Why do I get "LinAlgError Singular Matrix" Error

And when we work on matrices, there are several constraints and rules that we need to follow. Inverting a singular matrix is one of them. Inverting a singular matrix is practically impossible, and hence while applying the inverse function.

Solution to “LinAlgError Singular Matrix” Error

Now, the only solution to these errors is that you should avoid being in such scenarios. You should check the singularity of any matrix before applying any inverse operations on them. Moreover, it would be best never to forget the constraints on matrices while performing any operations on them.

This solution works in several scenarios where we get LinAlgError Singular Matrix-like building machine learning algorithms such as Logistic regression or deep learning model.

LinAlgError Singular Matrix In Pandas

Now, more often, when we work on some ML or DL project, we use more than one library simultaneously. Pandas are one of the significant tools in them. While performing some operations on matrix data, we might get LinAlgError Singular Matrix. Now, the reason for the error is the same as above, and we need to apply the same solution there. We choose to work on those data, which lets us avoid getting matrix data.


Q1) Does Logit endog requires the y variable to be 0?

The endog y variable needs to be zero, one. However, in other cases, it is possible that the Hessian is not positive definite when we evaluate it far away from the optimum, for example, at bad starting values. Switching to an optimizer that does not use the Hessian often succeeds in those cases. For example, scipy’s ‘bfgs’ is a good optimizer that works in many cases.


Today, in this article, we learned about LinAlgError. We now understand the meaning of error and inspect the scenarios in which the error may occur.

I hope this article has helped you. Thank You.

Notify of
Inline Feedbacks
View all comments