0   1   2
0   1   2   3
1   4   5   6
2  10  11  12
3   7   8   9

As we can see the row containing [10, 11, 12] got inserted after the row with index 1. We can use the following Python code to do the same:

import pandas

list1 = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
df = pandas.DataFrame(list1)
print("df before adding row: \n", df)

df.loc[1.5] = [10, 11, 12]
df = df.sort_index().reset_index(drop=True)
print("df after adding row: \n", df)

Here, we want to add a row after row index 1. So, we are first adding the row using df.loc[1.5]. After that, we are sorting the row indexes and resetting the indexes of the DataFrame. Please note that drop=True parameter ensures that the old indexes are not added to the DataFrame as a column.

The output of the above program will be:

df before adding row: 
   0  1  2
0  1  2  3
1  4  5  6
2  7  8  9
df after adding row: 
    0   1   2
0   1   2   3
1   4   5   6
2  10  11  12
3   7   8   9