Sorting rows in pandas DataFrame

Sorting rows in a Pandas DataFrame means rearranging rows based on the values of one or more columns. Pandas provides the sort_values() method to do this efficiently.

In this example, we sort movies by their release year in ascending order.

Python

import pandas as pd  

# Create a simple DataFrame  
movies = pd.DataFrame({ 'Movie': ['The Godfather', 'Inception', 'Titanic'],  
                        'Year': [1972, 2010, 1997] })  

result = movies.sort_values(by='Year')  
print(result)

Output

           Movie  Year
0  The Godfather  1972
2        Titanic  1997
1      Inception  2010

Explanation:

• sort_values(by='Year') sorts rows using the values in the Year column.
• Movies are arranged from the oldest (1972) to the most recent (2010).

Syntax

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, na_position='last')

Parameters:

• by: column label(s) to sort by.
• axis: 0 for rows (default), 1 for columns.
• ascending: True = smallest first, False = largest first.
• na_position: 'first' puts NaN first, 'last' puts them last.

Return Value: Returns a DataFrame with rows sorted by the given column(s).

Examples

Example 1: In this example, we sort student scores in Science from highest to lowest.

Python

import pandas as pd  
df = pd.DataFrame({ 'Name': ['Simon', 'Marsh', 'Alex', 'Selena'],  
                    'Science': [7, 9, 4, 7] })  
result = df.sort_values(by='Science', ascending=False)  
print(result)

Output

     Name  Science
1   Marsh        9
0   Simon        7
3  Selena        7
2    Alex        4

Explanation:

• ascending=False sorts Science marks from highest to lowest.
• Marsh with 9 comes first, Alex with 4 comes last.

Example 2: This code sorts students first by Maths, then by English, both in ascending order.

Python

import pandas as pd
df = pd.DataFrame({ 'Name': ['Simon', 'Marsh', 'Gaurav', 'Alex'],  
                    'Maths': [8, 5, 6, 9],  
                    'English': [7, 4, 7, 6] })  
result = df.sort_values(by=['Maths', 'English'])  
print(result)

Output

     Name  Maths  English
1   Marsh      5        4
2  Gaurav      6        7
0   Simon      8        7
3    Alex      9        6

Explanation:

• Sorting happens by Maths first.
• If two rows have the same Maths score, they are sorted by English.

Example 3: In this example, we sort by Science but display missing values (NaN) first.

Python

import pandas as pd
df = pd.DataFrame({ 'Name': ['Simon', 'Marsh', 'Alex', 'Selena'],  
                    'Science': [7, None, 4, 9] })  
result = df.sort_values(by='Science', na_position='first')  
print(result)

Output

     Name  Science
1   Marsh      NaN
2    Alex      4.0
0   Simon      7.0
3  Selena      9.0

Explanation:

• na_position='first' places NaN values before numeric values.
• Marsh with missing Science score comes first.

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