Why does PCA require centered data?

Centering ensures variance is measured relative to the mean.

PCA depends on covariance computation, which assumes that data is centered at zero. Without centering, the first principal component may capture the mean offset instead of meaningful variance structure. Centering shifts data so that covariance reflects true relationships between features.