PCA ? after the transformed data, are they still same with original data, (if maintain same dimensional)
As you can see from your pics there is a change.
From an algebra point of view, PCA is a base change. The interest of the algo is how the target base is designed: it is designed as to explain variance in the dataset. Dimension reduction is achieved trough removing some base vector with the least associated variance. In other words : PCA is a base change to an interesting base in term of variance explained, which might be followed by a projection on a sub-base.
When you remove the dimension reduction component you still have a base change. Which in the general case amount to some rotation, translastion, scaling, but no significant change in the underlying data. It seems to be what happen in your second graph : a rotation by 45° plus some scaling.