You can use the following basic syntax to reverse the rows in a pandas DataFrame:

df_reversed = df[::-1]

If you’d like to reverse the rows in the DataFrame and reset the index values, you can use the following syntax:

df_reversed = df[::-1].reset_index(drop=True)

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

Example: How to Reverse a Pandas DataFrame

Suppose we have the following pandas DataFrame that contains information about various basketball players:

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]})

#view DataFrame
print(df)

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

We can use the following syntax to reverse the rows in the DataFrame:

#create new DataFrame with rows reversed
df_reversed = df[::-1]

#view new DataFrame
print(df_reversed)

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

Notice that the order of the rows in the DataFrame have been reversed.

However, each row still contains its original index value.

If you’d like to reverse the rows of the DataFrame and reset the index values, you can use the following syntax:

#create reversed DataFrame and reset index values
df_reversed = df[::-1].reset_index(drop=True)

#view new DataFrame
print(df_reversed)

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

Notice that the order of rows has been reversed and the index values have been reset.

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