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 DataFrame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

Using Indexing with [::-1]

The simplest method to reverse DataFrame rows is using slice notation [::-1]

import pandas as pd

# Create a DataFrame
data = {'Rank': [1, 2, 3, 4, 5], 'Points': [100, 87, 80, 70, 50]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Reverse the DataFrame using indexing
print("\nReversed DataFrame:")
print(df[::-1])

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

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

Using reindex() Method

The reindex() method allows you to reorder rows by specifying a new index order

import pandas as pd

data = {'Rank': [1, 2, 3, 4, 5], 'Points': [100, 87, 80, 70, 50]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Reverse using reindex()
reversed_df = df.reindex(index=df.index[::-1])
print("\nReversed DataFrame:")
print(reversed_df)

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

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

Using iloc Property

The iloc property enables integer-location based indexing for selection by position

import pandas as pd

data = {'Rank': [1, 2, 3, 4, 5], 'Points': [100, 87, 80, 70, 50]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Reverse using iloc
reversed_df = df.iloc[::-1]
print("\nReversed DataFrame:")
print(reversed_df)

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

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

Using sort_index() Method

The sort_index() method can reverse row order by setting ascending=False

import pandas as pd

data = {'Rank': [1, 2, 3, 4, 5], 'Points': [100, 87, 80, 70, 50]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Reverse using sort_index()
reversed_df = df.sort_index(ascending=False)
print("\nReversed DataFrame:")
print(reversed_df)

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

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

Using reset_index() to Reset Index

If you want to reverse rows and reset the index to start from 0, combine reversal with reset_index()

import pandas as pd

data = {'Rank': [1, 2, 3, 4, 5], 'Points': [100, 87, 80, 70, 50]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Reverse and reset index
reversed_df = df[::-1].reset_index(drop=True)
print("\nReversed DataFrame with Reset Index:")
print(reversed_df)

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

Reversed DataFrame with Reset Index:
   Rank  Points
0     5      50
1     4      70
2     3      80
3     2      87
4     1     100

Comparison

Conclusion

Use df[::-1] for the simplest row reversal. Use reset_index(drop=True) if you need a fresh sequential index. All methods achieve the same result with slight differences in syntax and index handling.