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 pddf = 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      b1  10.0  100.02  20.0  200.03  30.0    NaN4  40.0    NaN5  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   id0  30.0   6.0  1.01  30.0  -3.0  1.02  30.0   6.0  1.03  30.0  12.0  1.04  30.0   8.0  6.05  30.0  11.0  2.06  30.0  -5.0  2.07  30.0  -5.0  4.08  30.0   NaN  NaN9  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     c0  10.0   6.0  1.0  30.01   6.0  -3.0  1.0  30.02  -3.0   6.0  1.0  30.03  -2.0  12.0  1.0  30.04   4.0   8.0  6.0  30.05  12.0  11.0  2.0  30.06   3.0  -5.0  2.0  30.07   3.0  -5.0  4.0  30.08   NaN   NaN  NaN  30.09   NaN   NaN  NaN  30.0