Yes, it is necessary to normalize data before performing PCA. The PCA calculates a new projection of your data set. ... If you normalize your data, all variables have the same standard deviation, thus all variables have the same weight and your PCA calculates relevant axis.

When should I scale before PCA?

The rule of thumb is that if your data is already on a different scale (e.g. every feature is XX per 100 inhabitants), scaling it will remove the information contained in the fact that your features have unequal variances. If the data is on different scales, then you should normalize it before running PCA.

Does standardization affect PCA?

Standardization of features will have an effect on the outcome of a PCA (assuming that the variables are originally not standardized). This is because we are scaling the covariance between every pair of variables by the product of the standard deviations of each pair of variables.