iterate over numpy arrays to fill pandas dataframe

I have two numpy arrays and dataframe as given below

val = np.array([0.501,0.32])
values = np.arange(24).reshape((2,3,4))
input_df = pd.DataFrame(columns=['colname_' + str(i) for i in range(4)])
row_id_list = [20,40]

I would like to

a) Create a new dataframe (dummy) with 3 columns such as ROW_ID, FEATURE NAME, Contribution

b) values for dummy dataframe should be populated using np.array above and column names from input_df`

c) Under the Feature Name column use the input_df column names

b) Populate the val[0] as contribution in dummy dataframe and also use each element from values[0][1] to populate it in contribution column.

I tried the below code with the help of this post

   pd.DataFrame({
  "Feature Name": ["Base value"] + [f"{col}" for col in df.columns.tolist()],
  "Contribution": (val[:1].tolist()) + list(values[0][1])
  #                   ^^^^
})

I was trying something like below

for i in range(len(input_df )):
    print("explainer value is")
    print(val[:1].tolist())
    print("the values are")
    print(values[0][i].tolist())

While this does for a single row, I would like to do this for 2000 rows. Is there any efficient way to do this?