Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Index.to_series() function create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index. It is possible to set a new index label for the newly created Series by passing the list of new index labels.
Syntax: Index.to_series(index=None, name=None)
Parameters :
index : index of resulting Series. If None, defaults to original index
name : name of resulting Series. If None, defaults to name of original indexReturns : Series : dtype will be based on the type of the Index values.
Example #1: Use Index.to_series() function to convert the index into a Series.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Harry', 'Mike', 'Arther', 'Nick'], name ='Student') # Print the Index print(idx) |
Output :
Let’s convert the index into a Series.
# convert the index into a series idx.to_series() |
Output :
The function has converted the index into a series. By default, the function has created the index of the series using the values of the original Index.
Example #2: Use Index.to_series() function to convert the index into a series such that the series created uses new index value.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Alice', 'Bob', 'Rachel', 'Tyler', 'Louis'], name ='Winners') # Print the Index print(idx) |
Output :
Let’s convert the index into a series.
# convert the index into a series idx.to_series(index =['Student 1', 'Student 2', 'Student 3', 'Student 4', 'Student 5']) |
Output :
The function has converted the index into a series. We have passed a list of index labels to be used for the newly created Series.
