Python Pandas - Sort within groups

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Sorting within groups is useful for analyzing data by specific categories, which is allowing us to manage and process subsets of data independently within a DataFrame. Pandas provides the groupby() function to split your data into groups based on certain criteria.

This is useful for performing operations like aggregation, transformation, filtration and sorting data within the groups.

In this tutorial, we will learn how to use the groupby() function in Pandas to sort data within groups.

GroupBy Sort

By default, when we use groupby() on a DataFrame, the group keys are automatically sorted in ascending order. If we need to maintain the original order of keys as they appear in the DataFrame, we can set the sort parameter to False.

Setting sort=False can also improve the speed of operation of the groupby() method, as no sorting of the group keys is required.

Example: Default Sorting Behavior of the GroupBy

Here is the basic example of demonstrating the default sorting behavior of the groupby() method.

import pandas as pd

# Create a sample DataFrame
data = {"Group": ["B", "B", "A", "A", "C", "B", "A"], "Values": [1, 2, 3, 4, 6, 5, 7]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Grouping and summing with default sorting
grouped_default = df.groupby("Group").sum()
print("\nGrouped and summed with default sorting:")
print(grouped_default)

Following is the output of the above code −

Original DataFrame:

	Group	Values
0	B	1
1	B	2
2	A	3
3	A	4
4	C	6
5	B	5
6	A	7

Grouped and summed with default sorting:

	Values
Group
A	14
B	8
C	6

Example: Disabling GroupBy Sorting

To disable the default sorting behavior of the Pandas grouping operation, we can pass the sort=False to the groupby() method. Which will maintain the original DataFrame order even after grouping the data.

import pandas as pd

# Create a sample DataFrame
data = {"Group": ["B", "B", "A", "A", "C", "B", "A"], "Values": [1, 2, 3, 4, 6, 5, 7]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Group by 'Group' column without sorting
result = df.groupby("Group", sort=False).sum()
print("\nGrouped and summed without sorting:")
print(result)

Following is the output of the above code −

Original DataFrame:

	Group	Values
0	B	1
1	B	2
2	A	3
3	A	4
4	C	6
5	B	5
6	A	7

Grouped and summed without sorting:

	Values
Group
B	8
A	14
C	6

GroupBy Sort Index

You can also sort the resulting DataFrame by the index after performing a grouping operation. This is particularly useful when you want to order the aggregated results explicitly.

Example

The following example demonstrates how to sort the grouped data by their index in descending order using the sort_index(ascending=False) method.

import pandas as pd

# Create a sample DataFrame
data = {"Group": ["B", "B", "A", "A", "C", "B", "A"], "Values": [1, 2, 3, 4, 6, 5, 7]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Grouping and summing with default sorting
grouped_default = df.groupby("Group").sum()

# Sort the grouped result by index in descending order
result = grouped_default.sort_index(ascending=False)
print("\nGrouped result sorted by index (descending):")
print(result)

Following is the output of the above code −

Original DataFrame:

	Group	Values
0	B	1
1	B	2
2	A	3
3	A	4
4	C	6
5	B	5
6	A	7

Grouped result sorted by index (descending):

	Values
Group
C	6
B	8
A	14

Sorting Groups by Size

Sorting the groups based on their sizes (i.e., the number of elements of each group) can be done by using the size() and sort_values() methods. This operation provides insights into which groups have more data points.

Example

The following example demonstrates how to sort grouped data based on its size.

import pandas as pd

# Create a sample DataFrame
data = {"Group": ["B", "B", "A", "A", "C", "B", "A"], "Values": [1, 2, 3, 4, 6, 5, 7]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Group the data and count the sizes of each group
group_sizes = df.groupby("Group").size()

# Sort the sizes in descending order
sorted_by_size = group_sizes.sort_values(ascending=False)

print("\nGroup sizes sorted by size (descending):")
print(sorted_by_size)

Following is the output of the above code −

Original DataFrame:

	Group	Values
0	B	1
1	B	2
2	A	3
3	A	4
4	C	6
5	B	5
6	A	7

Group sizes sorted by size (descending): Group A 3 B 3 C 1 dtype: int64

Sorting Groups by Another Column

You can also sort the groups based on the values in another column after aggregation.

Example

The following example sorts the grouped data based on the values in another column after aggregation.

import pandas as pd

# Create a sample DataFrame
data = {"Group": ["B", "B", "A", "A", "C", "B", "A"], "Values": [1, 2, 3, 4, 6, 5, 7]}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Group the data and sum, then sort by 'Values' column in ascending order
sorted_by_values = df.groupby("Group").sum().sort_values(by="Values", ascending=False)
print("\nGrouped result sorted by 'Values' (descending):")
print(sorted_by_values)

Following is the output of the above code −

Original DataFrame:

	Group	Values
0	B	1
1	B	2
2	A	3
3	A	4
4	C	6
5	B	5
6	A	7

Grouped result sorted by 'Values' (descending):

	Values
Group
A	14
B	8
C	6