Reverse the Rows of a Pandas Data Frame?

We will see here how to reverse the rows of a Pandas Dataframe. Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

Reverse the rows of a Pandas Data Frame using indexing

Example

In this example, we will reverse the rows of a dataframe using [::-1] −

import pandas as pd

# Create a Dictionary
dct = {'Rank':[1,2,3,4,5], 'Points':[100,87, 80,70, 50]}

# Create a DataFrame from Dictionary elements using pandas.dataframe()
df = pd.DataFrame(dct)
print("DataFrame = \n",df)

# Reverse the DataFrame using indexing
print("\nReverse the DataFrame = \n",df[::-1])

Output

DataFrame = 
    Rank  Points
0     1     100
1     2      87
2     3      80
3     4      70
4     5      50

Reverse the DataFrame = 
    Rank  Points
4     5      50
3     4      70
2     3      80
1     2      87
0     1     100

Reverse the rows of a Pandas Data Frame using reindex()

Example

In this example, we will reverse the rows of a dataframe using the reindex() −

import pandas as pd

# Create a Dictionary
dct = {'Rank':[1,2,3,4,5], 'Points':[100,87, 80,70, 50]}

# Create a DataFrame from Dictionary elements using pandas.dataframe()
df = pd.DataFrame(dct)
print("DataFrame = \n",df)

# Reverse the DataFrame using reindex()
print("\nReverse the DataFrame = \n",df.reindex(index=df.index[::-1]))

Output

DataFrame = 
    Rank  Points
0     1     100
1     2      87
2     3      80
3     4      70
4     5      50

Reverse the DataFrame = 
    Rank  Points
4     5      50
3     4      70
2     3      80
1     2      87
0     1     100

Reverse the rows of a Pandas Data Frame using iloc

Example

In this example, we will reverse the rows of a dataframe using the iloc −

import pandas as pd

# Create a Dictionary
dct = {'Rank':[1,2,3,4,5], 'Points':[100,87, 80,70, 50]}

# Create a DataFrame from Dictionary elements using pandas.dataframe()
df = pd.DataFrame(dct)
print("DataFrame = \n",df)

# Reverse the DataFrame using iloc
print("\nReverse the DataFrame = \n",df.iloc[::-1])

Output

DataFrame = 
    Rank  Points
0     1     100
1     2      87
2     3      80
3     4      70
4     5      50

Reverse the DataFrame = 
    Rank  Points
4     5      50
3     4      70
2     3      80
1     2      87
0     1     100

Reverse the rows of a Pandas Data Frame using sort_index()

Example

In this example, we will reverse the rows of a dataframe using the sort_index() method. In the parameter, we can set the order i.e. ascending False or True −

import pandas as pd

# Create a Dictionary
dct = {'Rank':[1,2,3,4,5], 'Points':[100,87, 80,70, 50]}

# Create a DataFrame from Dictionary elements using pandas.dataframe()
df = pd.DataFrame(dct)
print("DataFrame = \n",df)

# Reverse the DataFrame using sort_index()
print("\nReverse the DataFrame =   \n",df.sort_index(ascending=False))

Output

DataFrame = 
    Rank  Points
0     1     100
1     2      87
2     3      80
3     4      70
4     5      50

Reverse the DataFrame = 
    Rank  Points
4     5      50
3     4      70
2     3      80
1     2      87
0     1     100

Reverse the rows of a Pandas Data Frame using reset_index()

Example

Here, we will see another way to reverse the rows of a DataFrame. This also resets the index after reversing the DataFrame. Let us see the example −

import pandas as pd

# Create a Dictionary
dct = {'Rank':[1,2,3,4,5], 'Points':[100,87, 80,70, 50]}

# Create a DataFrame from Dictionary elements using pandas.dataframe()
df = pd.DataFrame(dct)
print("DataFrame = \n",df)

# Reverse the DataFrame using reset_index()
print("\nReverse the DataFrame = \n",df[::-1].reset_index())

Output

DataFrame = 
    Rank  Points
0     1     100
1     2      87
2     3      80
3     4      70
4     5      50

Reverse the DataFrame = 
    index  Rank  Points
0      4     5      50
1      3     4      70
2      2     3      80
3      1     2      87
4      0     1     100