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:
# 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.
# 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.
# 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.
# 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.
# 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:
