You can use the following basic syntax to insert a row into a a specific index position in a pandas DataFrame:

#insert row in between index position 2 and 3
df.loc[2.5] = value1, value2, value3, value4

#sort index
df = df.sort_index().reset_index(drop=True)

The following example shows how to use this syntax in practice.

Example: Insert Row at Specific Index Position in Pandas

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [18, 22, 19, 14, 14, 11, 20, 28],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A      18        5        11
1    B      22        7         8
2    C      19        7        10
3    D      14        9         6
4    E      14       12         6
5    F      11        9         5
6    G      20        9         9
7    H      28        4        12

We can use the following syntax to insert a row in between index position 2 and 3:

#insert row in between index position 2 and 3
df.loc[2.5] = 'Z', 10, 5, 7

#sort index
df = df.sort_index().reset_index(drop=True)

#view updated DataFrame
print(df)

  team  points  assists  rebounds
0    A      18        5        11
1    B      22        7         8
2    C      19        7        10
3    Z      10        5         7
4    D      14        9         6
5    E      14       12         6
6    F      11        9         5
7    G      20        9         9
8    H      28        4        12

Notice that a row has been inserted in between the previous index position 2 and 3 with the following information:

• team: Z
• points: 10
• assists: 5
• rebounds: 7

By using the sort_index() and reset_index() functions, we were then able to reassign values to the index ranging from 0 to 8.

Note that the new row must contain the same number of values as the number of existing columns.

For example, if we attempted to insert a new row with only three values, we would receive an error:

#attempt to insert row with only three values
df.loc[2.5] = 10, 5, 7

ValueError: cannot set a row with mismatched columns

We receive a because the number of values in the new row does not match the number of existing columns in the DataFrame.

The following tutorials explain how to perform other common operations in pandas: