Different order of eigenvalues computed with PCA and SVD

Question

I really don't know why, when i computed the eigenvalues with PCA from my dataset i obtain a vector which have values in different order respect of SVD

This is the result

This is the code

Thanks for help!!!

Please provide more context/background information, and please do not post screenshots of your code (paste and format the actual code instead). Also, this question as a whole might be better suited to [Data Science Stack Exchange](https://datascience.stackexchange.com/). — laptou, Dec 23 '18 at 23:04

score 0 · Accepted Answer · answered Dec 23 '18 at 19:12

Your PCA is incomplete when you use np.linalg.eig because after eigen-decomposition you have to reorder the terms so the eigenvalues in the diagonal matrix are in descending order (this is not part of the eigen-decomposition itself). Furthermore, the eig docs do not guarantee any order in your results, whereas the SVD docs explicitly state that your values are returned in descending order.

Different order of eigenvalues computed with PCA and SVD

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