How to Get Index of Series in Pandas

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  • Post category:Pandas
  • Post last modified:October 5, 2023

Pandas Series.index attribute is used to get the index labels of the given Series object. A pandas Series holds labeled data and these labels are called indexes, by using these labels we can access series elements and we can do manipulations on our data.

In some situations, we need to get all labels and values separately, using this attribute we can get only index labels. In this article, I will explain how to get index values/labels in pandas Series with examples.

1. Quick Examples of Getting Series Index

If you are in hurry below are some quick examples of how to get the pandas series index.

# Below are the quick examples

# Example 1 : Create pandas series
courses = pd.Series(['Java', 'Spark', 'PySpark','Pandas','NumPy', 'Python'])

# Example 2 : Get the indices of Series
courses.index = ['Course1', 'Course2', 'Course3', 'Course4', 'Course5', 'Course6']

2. Syntax of Pandas Series.index

Following is the syntax of the Series.index.

# Syntax of series.index

3. Create Pandas Series

Pandas Series is a one-dimensional, Index-labeled data structure that is available only in the Pandas library. It can store all the datatypes such as strings, integer, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.

Note : Series data structure is same as the NumPy array data structure but only one difference that is arrays indices are integers and starts with 0, whereas in series, the index can be anything even strings. The labels do not need to be unique but they must be of hashable type.

Now, let’s create a series,

# Create the Series
import pandas as pd
courses = pd.Series(['Java', 'Spark', 'PySpark','Pandas','NumPy', 'Python'])

Yields below output. When you create a series without an index, pandas create a default index with an incremental sequence number starting from 0.

# Output:
0       Java
1      Spark
2    PySpark
3     Pandas
4      NumPy
5     Python
dtype: object

4. Get the Index in Pandas Series

In Series, labels are called indices, and holding the data is called values. If we use Series.index attribute we can get the labels. Let’s apply this attribute to the above Series, it will return the indices of the series. When you have a default index it returns the RangeIndex.

# Get the default indices of Series.

# Output:
# RangeIndex(start=0, stop=6, step=1)
#<class 'pandas.core.indexes.range.RangeIndex'>

From the above, we got the default indices in the form of a range from 0 to 6.

5. Get the Custom Index in Series

We can also create the Series with customized index labels for, that we need to pass the index as a list of values into pd.series() function.

# Create pandas Series
courses = pd.Series(['Java', 'Spark', 'PySpark','Pandas','NumPy', 'Python'], index = ['Course1', 'Course2', 'Course3', 'Course4', 'Course5', 'Course6'])

Yields below output.

# Output:
Course1       Java
Course2      Spark
Course3    PySpark
Course4     Pandas
Course5      NumPy
Course6     Python
dtype: object

As we can see in the output, the Series.index attribute has successfully set the index labels for the given Series object.

Now, this time we can get the customized indices of the Series individually for, that we need to print only index values.

# Get the custom index

# Output:
# Index(['Course1', 'Course2', 'Course3', 'Course4', 'Course5', 'Course6'], dtype='object')
<class 'pandas.core.indexes.base.Index'>


In this article, I have explained the pandas.series.index attribute is used to get the Series index. also, covered what you get when you have a default index and custom index.

Happy learning !!



Vijetha is an experienced technical writer with a strong command of various programming languages. She has had the opportunity to work extensively with a diverse range of technologies, including Python, Pandas, NumPy, and R. Throughout her career, Vijetha has consistently exhibited a remarkable ability to comprehend intricate technical details and adeptly translate them into accessible and understandable materials.

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