Is there a more efficient method to iterate over a matrix and perform calculations on specific columns?

I have a matrix with 2 columns (matrix "X" with two features - feature0 and feature1) and variable number of rows. For each sample (row in matrix) I want to compute an expanded row such that each row would be [feature0, feature1, feature0^2, feature1^2, feature0*feature1, 1].

I have written function below which does the job.

def expand(X):

    X_expanded = np.zeros((X.shape[0], 6))

    for i in range(X_expanded.shape[0]):
        for j in range(X_expanded.shape[1]):

            if j <= 1:
                X_expanded[i, j] = X[i, j]
            elif j == 2:
                X_expanded[i, j] = X[i, 0]*X[i, 0]
            elif j == 3:
                X_expanded[i, j] = X[i, 1]*X[i, 1]
            elif j == 4:
                X_expanded[i, j] = X[i, 0]*X[i, 1]            
            elif j == 5:
                X_expanded[i, j] = 1



    return X_expanded

The questions I have, is there a more efficient or "better way" of performing this calculation? Seems cumbersome to me so would welcome any advice. Thanks in advance.