# Get the eigenvalues when you know the explained variances of a PCA plot

I'm performing a PCA using the sklearn.decomposition.pca function.

It appears to work as it should. Acording to this question, I can get the eigenvalues like this:.

The eigenvalues represent the variance in the direction of the eigenvector. So you can get them through the pca.explained_variance_ attribute:

eigenvalues = pca.explained_variance_

If this is correct, the eigenvalues for my first few components seem to be way to high.

eigenvalues = pca.explained_variance_
eigenvalues # returns  [1188.482427    760.26572144  581.29434167  325.56710676  267.10095401
219.49301802  155.1603308   107.8855256    76.17770897   64.09568959]


For every scree-plot I've seen and for example, when you google screeplot on google, the eigenvalues are usually much, much lower. Can eigenvalues be this high?

sklearn doesn't scale your data: