Pandas DataFrame: Sorting in each group by one or many columns (5 examples)

Updated: February 24, 2024 By: Guest Contributor Post a comment


Pandas is a fiercely powerful tool for data manipulation and analysis in Python. It offers an incredible set of functionalities, making tasks like data cleaning, transformation, and preparation both efficient and intuitive. Among its rich features, sorting stands out as a fundamental element, particularly when dealing with grouped data. This tutorial dives deep into how to sort within groups by one or many columns, showcasing five practical examples that progress from basic to advanced usage.

The Basics of Sorting Data in Pandas

Before we jump into the examples, it’s important to understand the basics of sorting data in Pandas. The primary method for sorting data is sort_values(), which can sort a DataFrame or Series. For group-wise sorting, groupby() becomes our go-to method, allowing us to partition the data into groups, which can then be sorted individually.

Example 1: Basic Group Sorting by a Single Column

import pandas as pd

df = pd.DataFrame({
    'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [1, 2, 3, 4, 5, 6]

df.groupby('Group').apply(lambda x: x.sort_values('Value')).reset_index(drop=True)


  Group  Value
0     A      1
1     A      2
2     B      3
3     B      4
4     C      5
5     C      6

This example demonstrates the simplest form of group-wise sorting, where each group formed by the ‘Group’ column is sorted by the ‘Value’ column.

Example 2: Sorting Within Groups by Multiple Columns

import pandas as pd

df = pd.DataFrame({
    'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value1': [1, 2, 3, 4, 5, 6],
    'Value2': [6, 5, 4, 3, 2, 1]

df.groupby('Group').apply(lambda x: x.sort_values(by=['Value1', 'Value2'])).reset_index(drop=True)


  Group  Value1  Value2
0     A      1       6
1     A      2       5
2     B      3       4
3     B      4       3
4     C      5       2
5     C      6       1

In this example, each group is sorted by two columns, ‘Value1’ and ‘Value2’. This highlights how Pandas can manage multiple sorting keys, providing flexibility in organizing grouped data.

Example 3: Sorting with Custom Order

Not every sorting scenario sticks to ascending or descending order. Sometimes, a custom sorting order is necessary, especially when categorical data is involved. Pandas caters to this need efficiently.

import pandas as pd
from pandas.api.types import CategoricalDtype

df = pd.DataFrame({
    'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Fruit': ['Banana', 'Apple', 'Apple', 'Banana', 'Cherry', 'Banana']

cat_type = CategoricalDtype(categories=['Apple', 'Banana', 'Cherry'], ordered=True)
df['Fruit'] = df['Fruit'].astype(cat_type)

df.groupby('Group').apply(lambda x: x.sort_values('Fruit')).reset_index(drop=True)


  Group   Fruit
0     A   Apple
1     A Banana
2     B   Apple
3     B Banana
4     C Banana
5     C Cherry

This example demonstrates sorting within groups using a custom order for the ‘Fruit’ column, defined by the sequence ‘Apple’, ‘Banana’, ‘Cherry’. This is particularly useful for handling categories with inherent ordering.

Example 4: Descending and Mixed Sort Order

import pandas as pd

df = pd.DataFrame({
    'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [2, 1, 4, 3, 6, 5],
    'Date': ['2023-01-02', '2023-01-01', '2023-02-02', '2023-02-01', '2023-03-02', '2023-03-01']

df['Date'] = pd.to_datetime(df['Date'])

df.groupby('Group').apply(lambda x: x.sort_values(by=['Value', 'Date'], ascending=[False, True])).reset_index(drop=True)


  Group  Value       Date
0     A      2 2023-01-02
1     A      1 2023-01-01
2     B      4 2023-02-02
3     B      3 2023-02-01
4     C      6 2023-03-02
5     C      5 2023-03-01

This example combines descending sort order for the ‘Value’ column with ascending order for the ‘Date’ column, showcasing the ability to apply mixed sorting criteria within each group.

Example 5: Advanced Sorting with Aggregation

In some cases, sorting within groups necessitates a more nuanced approach, especially when it involves aggregate values. This next example illustrates how to sort groups based on the sum of a particular column.

import pandas as pd

df = pd.DataFrame({
    'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [1, 2, 3, 4, 5, 6]

result = df.groupby('Group')['Value'].sum().sort_values().reset_index()
result = pd.merge(df, result, on='Group').sort_values(by=['Value_y', 'Group'])


  Group  Value_x  Value_y
0     A        1        3
1     A        2        3
2     B        3       7
3     B        4        7
4     C        5       11
5     C        6       11

This sophisticated method involves first calculating the sum of values for each group, then sorting the entire DataFrame based on these aggregated values. It provides a powerful technique for organizing data based on summary statistics.


Sorting data within groups in Pandas can significantly enhance your data analysis, allowing for more insightful and tailored results. By mastering the examples provided, ranging from basic to advanced techniques, you’ll be better equipped to harness the full potential of your data. Merging these practical examples with your own data analysis tasks can lead to richer, more informative insights.