There's no really easy way forward here. And there's no generally agreed upon "best approach". It all depends on your data, and how much you know about each variable, and how much time and effort are you willing to put into the handling of missings.
If you understand ALL of the variables, you can do intelligent things like impute values for the missing in each variable. The imputation for X1 could very well be performed differently than the imputation for X2.
On the other hand, if you need to just handle all variables in bulk, you could assign the mean to each variable, but this has its own drawbacks.
Another approach is to create dummy variables for each continuous variable, where if X1 is missing, you assign it a value of the mean, and assign DUMMY1 to have a 1 to indicate missing, and zero elsewhere.
Yet other people bin each variable into 8–10 bins, with missing being an additional and separate bin.
And since I have appointed myself the PCA spelling police, I point out that it is "Principal Components", not "Principle Components".
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