Pandas expanding a dataframe's length and populating every nth row

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I have a dataframe with two columns that I'm interested in which looks like this:

    frame   requests
0   0   214388438.0
1   1   194980303.0
2   2   179475934.0
3   3   165196540.0
4   4   154815540.0
5   5   123650671.0
6   6   119089045.0

This goes on for many rows. So what I'm looking for is a way to keep the value on the requests column then add rows filled with 0 and repeat the process for a certain amount of rows. frame column would also have to be updated accordingly.

Now what I need is to adjust this dataframe so that I have in the following manner:

        frame   requests
    0   0   214388438.0
    1   0             0
    2   0             0
    3   0             0
    4   0             0
    5   0             0
    6   0             0
   ....................
   47   0             0
   48   1   194980303.0
   49   1             0
   ....................

The thing is the dataframe is only 48 rows long but it could be longer like 200 or more so it would have to take that into account.

pandas python
1 Answers

Use Index.repeat with DataFrame.loc and then duplicated values set to 0:

N = 3
df = df.loc[df.index.repeat(N)]
df['requests'] = df['requests'].mask(df.index.duplicated(), 0)
df = df.reset_index(drop=True)
print (df)
    frame     requests
0       0  214388438.0
1       0          0.0
2       0          0.0
3       1  194980303.0
4       1          0.0
5       1          0.0
6       2  179475934.0
7       2          0.0
8       2          0.0
9       3  165196540.0
10      3          0.0
11      3          0.0
12      4  154815540.0
13      4          0.0
14      4          0.0
15      5  123650671.0
16      5          0.0
17      5          0.0
18      6  119089045.0
19      6          0.0
20      6          0.0
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