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 moduleimport pandas as pdimport numpy as np# Creating series data for address detailsindex_values = pd.Series([('sravan', 'address1'), ('sravan', 'address2'), ('sudheer', 'address1'), ('sudheer', 'address2')])# assigning values with integersdata = pd.Series(np.arange(1, 5), index=index_values)# display dataprint(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 moduleimport pandas as pd# Creating series data for address detailsindex_values = pd.Series([('sravan', 'address1'), ('sravan', 'address2'), ('sudheer', 'address1'), ('sudheer', 'address2')])# assigning values with integersdata = pd.Series(np.arange(1, 5), index=index_values)# display dataprint(data)# mapping with data using '_' symbol with joindata1 = 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 moduleimport pandas as pd# importing numpy moduleimport 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 integersdata = pd.Series(np.arange(1, 5), index=index_values)# display dataprint(data)# mapping with data using '_' symbol with joindata1 = 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 moduleimport pandas as pd# importing numpy moduleimport 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 integersdata = pd.Series(np.arange(1, 9), index=index_values)# display dataprint(data)# mapping with data using '_' symbol with joindata1 = 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 moduleimport pandas as pd# importing numpy moduleimport 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 integersdata = pd.Series(np.arange(1, 9), index=index_values)# display dataprint(data)# mapping with data using '/' symbol with joindata1 = data.index.map('/'.join)print(data1) |
Output:
