Python 在新数据上应用系数列表（来自回归模型）

Question

I have a list of coeeficient from sklearn logistic regression model:

[-0.52  0.31  0.059 0.1 ]

Now , I have a new dataframe, for example:

    df =  A  B  C  D
          1  5  2  7
          6  2  1  9

And I want to add a new column - that is the result of applying the list of coeffs on each row. So the values will be:

1*-0.52 + 5*0.31 + 2*0.059 + 7*0.1 = 1.848
6*-0.52 + 2*0.31 + 1*0.059 + 9*0.1 = -1.541

What is the best way to do that?

Thanks!

Answer 1

So we can do numpy reshape

l = [-0.52,  0.31,  0.059, 0.1 ]
s = [1, 5 ,2 ,7 ,6 ,2 ,1 ,9]
np.sum(np.array(s).reshape(-1,4)*l, axis=1)
Out[140]: array([ 1.848, -1.541])

Updated

df['New'] = df.dot(l)
df
Out[145]: 
   A  B  C  D    New
0  1  5  2  7  1.848
1  6  2  1  9 -1.541

Answer 2

This is matrix multiplication. You can do:

df['new_col'] = df @ a

Output:

   A  B  C  D  new_col
0  1  5  2  7    1.848
1  6  2  1  9   -1.541