Convert one row of a pandas dataframe into multiple rows

I want to turn this:

   age  id  val
0   99   1  0.3
1   99   2  0.5
2   99   3  0.1

Into this:

   age  id  val
0   25   1  0.3
1   50   1  0.3
2   75   1  0.3
3   25   2  0.5
4   50   2  0.5
5   75   2  0.5
6   25   3  0.1
7   50   3  0.1
8   75   3  0.1

Context: I have data stored with one value coded for all ages (age = 99). However, the application I am developing for needs the value explicitly stated for every id-age pair (id =1, age = 25,50, and 75). There are simple solutions to this: iterate over id's and append a bunch of dataframes, but I'm looking for something elegant. I'd like to do a many:one merge from my original dataframe to a template containing all the ages, but I would still have to loop over id's to create the template.

Don't know, may be there's more elegant approach, but you can do something like cross join (or cartesian product):

>>> df = pd.DataFrame({'age':[99,99,99],'id':[1,2,3],'val':[0.3,0.5,0.1]})
>>> df
   age  id  val
0   99   1  0.3
1   99   2  0.5
2   99   3  0.1
>>> df2 = pd.DataFrame({'age':[99,99,99],'new_age':[25,50,75]})
>>> df2 = pd.merge(df, df2, on='age')
>>> del df2['age']
>>> df2 = df2.rename(columns={'new_age':'age'})
>>> df2
   id  val      age
0   1  0.3       25
1   1  0.3       50
2   1  0.3       75
3   2  0.5       25
4   2  0.5       50
5   2  0.5       75
6   3  0.1       25
7   3  0.1       50
8   3  0.1       75