Further reading
Victor Powell and Lewis Lehe provide a fantastic interactive, visual explanation of PCA at http://setosa.io/ev/principal-component-analysis/, this is ideal for readers who are new to the core concepts of PCA or who are not quite getting it.
For a lengthier and more mathematically-involved treatment of PCA, touching on underlying matrix transformations, Jonathon Shlens from Google research provides a clear and thorough explanation at http://arxiv.org/abs/1404.1100.
For a thorough worked example that translates Jonathon's description into clear Python code, consider Sebastian Raschka's demonstration using the Iris dataset at http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html.
Finally, consider the sklearn documentation for ...
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