Python Pandas - Sorting a MultiIndex

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:

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:

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:

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'])