Concatenate multiIndex into single index in Pandas Series

Last Updated : 14 Sep, 2021

In this article, we will see how to concatenate multi-index to a single index in Pandas Series. Multi-index refers to having more than one index with the same name.

Create a sample series:

Python3

# importing pandas module
import pandas as pd
import numpy as np

# Creating series data for address details
index_values = pd.Series([('sravan', 'address1'),
                          ('sravan', 'address2'),
                          ('sudheer', 'address1'),
                          ('sudheer', 'address2')])

# assigning values with integers
data = pd.Series(np.arange(1, 5),
                 index=index_values)

# display data
print(data)

Output:

Joining two or more data is known as concatenation. Here we are going to concatenate the index using map function.

Syntax:

map(fun, iter)

fun: function
iter: iterations.

Below are various examples that depict how to concatenate multi-index into a single index in Series:

Example 1:

This code explains the joining of addresses into one based on multi-index.

Python3

# importing pandas module
import pandas as pd

# Creating series data for address details
index_values = pd.Series([('sravan', 'address1'),
                          ('sravan', 'address2'),
                          ('sudheer', 'address1'),
                          ('sudheer', 'address2')])

# assigning values with integers
data = pd.Series(np.arange(1, 5), index=index_values)

# display data
print(data)

# mapping with data using '_' symbol with join
data1 = data.index.map('_'.join)

print(data1)

Output:

Example 2:

This code is an example for all the given same name, but different values passed in a tuple.

Python3

# importing pandas module
import pandas as pd

# importing numpy module
import numpy as np

# Creating series data for address details with same name.
index_values = pd.Series([('sravan', 'address1'), 
                          ('sravan', 'address2'), 
                          ('sravan', 'address3'), 
                          ('sravan', 'address4')])

# assigning values with integers
data = pd.Series(np.arange(1, 5), 
                 index=index_values)

# display data
print(data)

# mapping with data using '_' symbol with join
data1 = data.index.map('_'.join)

print(data1)

Output:

Example 3:

This code gives a demo on multiple users given in nested list data structure.

Python3

# importing pandas module
import pandas as pd

# importing numpy module
import numpy as np

# Creating series data for address details 
# with same name with nested lists.
index_values = pd.Series([['sravan', 'address1'], 
                          ['sravan', 'address2'], 
                          ['sravan', 'address3'], 
                          ['sravan', 'address4'], 
                          ['vani', 'address5'], 
                          ['vani', 'address6'], 
                          ['vani', 'address7'], 
                          ['vani', 'address8']])

# assigning values with integers
data = pd.Series(np.arange(1, 9), 
                 index=index_values)

# display data
print(data)

# mapping with data using '_' symbol with join
data1 = data.index.map('_'.join)

print(data1)

Output:

Example 4:

This code explains the college data with respect to address passed in a nested list separated by '/' operator.

Python3

# importing pandas module
import pandas as pd

# importing numpy module
import numpy as np

# Creating series data for address details w.r.t 
# college names  with same name with nested lists.
index_values = pd.Series([['sravan', 'address1', 'vignan'], 
                          ['sravan', 'address2', 'vignan'], 
                          ['sravan', 'address3', 'vignan'], 
                          ['sravan', 'address4', 'vignan'], 
                          ['vani', 'address5', 'vignan lara'], 
                          ['vani', 'address6', 'vignan lara'], 
                          ['vani', 'address7', 'vignan lara'], 
                          ['vani', 'address8', 'vignan lara']])

# assigning values with integers
data = pd.Series(np.arange(1, 9), 
                 index=index_values)

# display data
print(data)

# mapping with data using '/' symbol with join
data1 = data.index.map('/'.join)

print(data1)