Python Pandas - Sorting a MultiIndex

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Sorting MultiIndex in Pandas is used to efficiently organize the hierarchical datasets. In Pandas MultiIndex is also known as a hierarchical index and it has multiple levels of index in Pandas data structures such as, DataFrame or Series objects. Each level in a MultiIndexed object can be sorted independently to apply the efficient slicing, indexing, filtering, and retrieving operations on your data.

Below are the key methods to sort MultiIndexed objects in Pandas −

• sort_index(): Sort object by labels.

• sortlevel(): Used for sorting the MultiIndexed object at a specific level.

• sort_values(): Used to get the sorted copy if the DataFrame.

In this tutorial, we will learn how to sort a MultiIndexed objects in Pandas using these methods with different approaches.

Sorting MultiIndex Using sort_index()

The Pandas DataFrame.sort_index() method is used to sort a MultiIndex by all levels. Sorting a MultiIndex object can be useful for efficient indexing and slicing of the data.

Example

Here is the basic example of using the df.sort_index() method is to sort a MultiIndex by all levels. This sorts the data according to both levels of the MultiIndex.

 import pandas as pd # Create a MultiIndex object index = pd.MultiIndex.from_tuples([('A', 'one'), ('A', 'two'), ('A', 'three'),('B', 'one'), ('B', 'two'), ('B', 'three')], names=["level0", "level1"]) # Create a DataFrame data = [[1, 2], [3, 4], [1, 1], [5, 6], [7, 8], [2, 2]] df = pd.DataFrame(data, index=index, columns=['X', 'Y']) # Display the input DataFrame print('Original MultiIndexed DataFrame:\n',df) # Sort MultiIndex with default levels sorted_df = df.sort_index() print("Resultant DataFrame:") print(sorted_df) 

Following is the output of the above code −

 Original MultiIndexed DataFrame: 

		X	Y
level1	level2
A	one	1	2
	two	3	4
	three	1	1
B	one	5	6
	two	7	8
	three	2	2

Resultant DataFrame:

		X	Y
level1	level2
A	one	1	2
	three	1	1
	two	3	4
B	one	5	6
	three	2	2
	two	7	8

Sorting MultiIndex by Specific Level

If you want to sort by a specific level of the MultiIndex, you can use the level parameter of the df.sort_index() method.

Example

Following is the example of sorting a MultiIndex by its the first level (ie., level=0).

 import pandas as pd # Create a MultiIndex object index = pd.MultiIndex.from_tuples([('C', 'one'), ('C', 'two'),('B', 'one'), ('B', 'two')]) # Create a DataFrame data = [[1, 2], [3, 4], [5, 6], [7, 8]] df = pd.DataFrame(data, index=index, columns=['X', 'Y']) # Display the input DataFrame print('Original MultiIndexed DataFrame:\n',df) # Sort MultiIndex by the first level sorted_df = df.sort_index(level=0) print("Resultant DataFrame:") print(sorted_df) 

Following is the output of the above code −

 Original MultiIndexed DataFrame: 

		X	Y
C	one	1	2
	two	3	4
B	one	5	6
	two	7	8

Resultant DataFrame:

		X	Y
B	one	5	6
	two	7	8
C	one	1	2
	two	3	4

Sorting MultiIndex by Level Names

Similar to the above approach you can also sort the MultiIndex by level names instead of the numerical index using the df.sort_index() method with level parameter.

Example

This example sorts the MultiIndex by using the level name specified to the level parameter of the set_names() method.

 import pandas as pd # Create a MultiIndex object index = pd.MultiIndex.from_tuples([('D', 'z'), ('D', 'x'), ('D', 'y'),('B', 't'), ('B', 's'), ('B', 'v')], names=["level0", "level1"]) # Create a DataFrame data = [[1, 2], [3, 4], [1, 1], [5, 6], [7, 8], [2, 2]] df = pd.DataFrame(data, index=index, columns=['X', 'Y']) # Display the input DataFrame print('Original MultiIndexed DataFrame:\n',df) # Sort by the level name sorted_df = df.sort_index(level='level1') print("Resultant DataFrame:") print(sorted_df) 

Following is the output of the above code −

 Original MultiIndexed DataFrame: 

		X	Y
level1	level2
D	z	1	2
	x	3	4
	y	1	1
B	t	5	6
	s	7	8
	v	2	2

Resultant DataFrame:

		X	Y
level1	level2
B	s	7	8
	t	5	6
	v	2	2
D	x	3	4
	y	1	1
	z	1	2

Sorting MultiIndex at Specific Levels with sortlevel()

By using the MultiIndex.sortlevel() method you can also sort a MultiIndex at a specific level.

Example

Following is the example of sorting the MultiIndex object by using the MultiIndex.sortlevel() method.

 import pandas as pd # Create arrays arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob']] # The from_arrays() is used to create a MultiIndex multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student')) # display the MultiIndex print("The Multi-index...\n",multiIndex) # get the levels in MultiIndex print("\nThe levels in Multi-index...\n",multiIndex.levels) # Sort MultiIndex # The specific level to sort is set as a parameter i.e. level 1 here print("\nSort MultiIndex at the requested level...\n",multiIndex.sortlevel(1)) 

Following is the output of the above code −

 The Multi-index... MultiIndex([(2, 'Peter'), (4, 'Chris'), (3, 'Andy'), (1, 'Jacob')], names=['ranks', 'student']) The levels in Multi-index... [[1, 2, 3, 4], ['Andy', 'Chris', 'Jacob', 'Peter']] Sort MultiIndex at the requested level... (MultiIndex([(3, 'Andy'), (4, 'Chris'), (1, 'Jacob'), (2, 'Peter')], names=['ranks', 'student']), array([2, 1, 3, 0])) 

Sorting MultiIndex Using sort_values()

The sort_values() method sorts the index object and returns the copy of the index.

Example

The following example demonstrates how to sort the MultiIndex object using the sort_values() method.

 import pandas as pd # Create arrays arrays = [[2, 4, 3, 1], ['Peter', 'Chris', 'Andy', 'Jacob']] # The from_arrays() is used to create a MultiIndex multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student')) # display the MultiIndex print("The Multi-index...\n",multiIndex) # Sort MultiIndex using the sort_values() method print("\nSort MultiIndex...\n",multiIndex.sort_values()) 

Following is the output of the above code −

 The Multi-index... MultiIndex([(2, 'Peter'), (4, 'Chris'), (3, 'Andy'), (1, 'Jacob')], names=['ranks', 'student']) Sort MultiIndex... MultiIndex([(1, 'Jacob'), (2, 'Peter'), (3, 'Andy'), (4, 'Chris')], names=['ranks', 'student'])