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Concatenate multiIndex into single index in Pandas Series

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  • 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)


Output:


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