Create new pandas dataframe from rows with values in one column

I am trying to create a new data frame using only the rows from a another data frame (called "div" here) containing values in column 3, labeled "annum".

Then I would like to create a new column, labeled "growth", within the new data frame containing those values divided by the next row and 1 subtracted from the quotient. I am close but am not sure how to get what I need.

Thank you in advance for any help you may be able to provide.

def g_rate():

  div= dh()
  df= pd.DataFrame()

  for count in range(0,len(div)):
    if count == 0:

        df[count] = [div.iloc[0, 2]]

    if count >0:
        if count <10:
            
            row = count*3+count+3
            df[count] = [div.iloc[row, 2]]

  print(df)

The output:

Enter ticker: C
    0     1     2     3     4     5     6     7     8     9
0 NaN  2.04  2.04  1.92  1.54  0.96  0.42  0.16  0.04  0.04

The desired output:

   Year  C_dividends  annum  growth
0  2022         0.51   2.04   0          #annum row 0/annum row 1
1  2021         0.51   2.04   0          #annum row 1/annum row 2
2  2020         0.51   2.04   0.0625     #annum row 2/annum row 3 and so on
3  2019         0.45   1.92   0.246753
4  2018         0.32   1.54   0.604167
5  2017         0.16   0.96   1.285714
6  2016         0.05   0.42   1.625
7  2015         0.01   0.16   3
8  2014         0.01   0.04   0
9  2013         0.01   0.04   NaN #or 0, as it is the last row

The div index:

           Date  C_dividends  annum
0   04 Nov 2022         0.51    NaN
1   29 Jul 2022         0.51    NaN
2   29 Apr 2022         0.51    NaN
3   04 Feb 2022         0.51   2.04
4   29 Oct 2021         0.51    NaN
5   30 Jul 2021         0.51    NaN
6   30 Apr 2021         0.51    NaN
7   29 Jan 2021         0.51   2.04
8   30 Oct 2020         0.51    NaN
9   31 Jul 2020         0.51    NaN
10  01 May 2020         0.51    NaN
11  31 Jan 2020         0.51   2.04
12  01 Nov 2019         0.51    NaN
13  02 Aug 2019         0.51    NaN
14  03 May 2019         0.45    NaN
15  01 Feb 2019         0.45   1.92
16  02 Nov 2018         0.45    NaN
17  03 Aug 2018         0.45    NaN
18  04 May 2018         0.32    NaN
19  02 Feb 2018         0.32   1.54
20  03 Nov 2017         0.32    NaN
21  03 Aug 2017         0.32    NaN
22  27 Apr 2017         0.16    NaN
23  02 Feb 2017         0.16   0.96
24  03 Nov 2016         0.16    NaN
25  28 Jul 2016         0.16    NaN
26  28 Apr 2016         0.05    NaN
27  28 Jan 2016         0.05   0.42
28  29 Oct 2015         0.05    NaN
29  30 Jul 2015         0.05    NaN
30  07 May 2015         0.05    NaN
31  29 Jan 2015         0.01   0.16
32  30 Oct 2014         0.01    NaN
33  31 Jul 2014         0.01    NaN
34  01 May 2014         0.01    NaN
35  30 Jan 2014         0.01   0.04
36  31 Oct 2013         0.01    NaN
37  01 Aug 2013         0.01    NaN
38  02 May 2013         0.01    NaN
39  31 Jan 2013         0.01   0.04