How to insert row at specific index position in Pandas DataFrame?

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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.