Pandas dataframe computations

Question

I am trying compute a metric with panda dataframes. In particular, I get a results object

prediction = results.predict(start=1,end=len(test),exog=test)

The actual values are in a dataframe given by

test['actual']. 

I need to compute two things:

1. How can I compute the sum of squares of errors? So basically, I would be doing an element by element subtraction and then summing the squares of these.

2. How can I compute the sum of squares of the predicted minus the mean of the actual values? So it would be

   (x1-mean_actual)^2 + (x2-mean_actual)^2...+(xn-mean_actual)^2
   

Answer 1

First one would be

((prediction - test['actual']) ** 2).sum()

Second one would be:

((prediction - test['actual'].mean()) ** 2).sum()