Pandas dataframe creating multiple rows at once via .loc

Admittedly, this is a very late answer, but I have had to deal with a similar problem and think my solution might be helpful to others as well.

After recreating your data, it is basically a two-step approach:

1. Recreate data:

   import pandas as pd
   df = pd.DataFrame({'a':[10, 20], 'b':[100,200]}, index='1 2'.split())
   df.loc[3, 'a'] = 30
   

2. Extend the df.index using .reindex:

   idx = list(df.index)
   new_rows = list(map(str, range(4, 6)))  # easier extensible than new_rows = ["4", "5"]
   idx.extend(new_rows)
   df = df.reindex(index=idx)
   

3. Set the values using .loc:

   df.loc[new_rows, "a"] = [40, 50]
   

   giving you

   >>> df
         a      b
   1  10.0  100.0
   2  20.0  200.0
   3  30.0    NaN
   4  40.0    NaN
   5  50.0    NaN
   

Example data

>>> data = pd.DataFrame({
    'a': [10, 6, -3, -2, 4, 12, 3, 3], 
    'b': [6, -3, 6, 12, 8, 11, -5, -5], 
    'id': [1, 1, 1, 1, 6, 2, 2, 4]})

Case 1 Note that range can be altered to whatever it is that you desire.

>>> for i in range(10):
...     data.loc[i, 'a'] = 30
... 
>>> data
      a     b   id
0  30.0   6.0  1.0
1  30.0  -3.0  1.0
2  30.0   6.0  1.0
3  30.0  12.0  1.0
4  30.0   8.0  6.0
5  30.0  11.0  2.0
6  30.0  -5.0  2.0
7  30.0  -5.0  4.0
8  30.0   NaN  NaN
9  30.0   NaN  NaN

Case 2 Here we are adding a new column to a data frame that had 8 rows to begin with. As we extend our new column c to be of length 10 the other columns are extended with NaN.

>>> for i in range(10):
...     data.loc[i, 'c'] = 30
... 
>>> data
      a     b   id     c
0  10.0   6.0  1.0  30.0
1   6.0  -3.0  1.0  30.0
2  -3.0   6.0  1.0  30.0
3  -2.0  12.0  1.0  30.0
4   4.0   8.0  6.0  30.0
5  12.0  11.0  2.0  30.0
6   3.0  -5.0  2.0  30.0
7   3.0  -5.0  4.0  30.0
8   NaN   NaN  NaN  30.0
9   NaN   NaN  NaN  30.0