Pandas Series Index Attribute (original) (raw)

Last Updated : 26 Mar, 2025

Pandas Series is a **one-dimensional labeled array capable of holding any data type (integers, strings, floats, etc.), with each element having an associated label known as its **index. The Series.index attribute in Pandas allows users to get or set the index labels of a Series object, enhancing data accessibility and retrieval efficiency. Example:

Python `

import pandas as pd

data = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])

Accessing the index

print("Original Index:", data.index)

Modifying the index

data.index = ['w', 'x', 'y', 'z'] print("Modified Series:\n", data)

`

Output

Original Index: Index(['a', 'b', 'c', 'd'], dtype='object') Modified Series: w 10 x 20 y 30 z 40 dtype: int64

**Explanation: This code creates a Pandas Series with custom index labels (‘a’, ‘b’, ‘c’, ‘d’) and retrieves the index using data.index. It then updates the index to (‘w’, ‘x’, ‘y’, ‘z’).

Syntax

Series.index # Access index labels

Series.index = new_index # Modify index labels

**Parameter: This method does not take any parameter.

**Returns: Index labels of the Series.

**Functionality:

• Retrieves the current index labels of the Series.
• Can be used to set new index labels.
• Supports both unique and duplicate index labels.
• Useful for locating elements efficiently within a Series.

Examples of Pandas Series Index() Attribute

**Example 1. Assigning Duplicate Index Labels

**Pandas allows assigning duplicate index labels, which can be useful in cases where multiple elements share the same category.

Python `

import pandas as pd

series = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon'])

Creating the row axis labels

series.index = ['City 1', 'City 1', 'City 3', 'City 3'] print(series)

`

Output

City 1 New York City 1 Chicago City 3 Toronto City 3 Lisbon dtype: object

**Explanation: Even with duplicate labels (**‘City 1’ and ‘City 3’ appearing twice), Pandas maintains the Series structure and ensures data integrity.

**Example 2. Retrieving Index Labels

The **Series.index attribute can also be used to retrieve the current index labels of a Series.

Python `

import pandas as pd

Date = ['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018'] idx_name = ['Day 1', 'Day 2', 'Day 3', 'Day 4']

sr = pd.Series(data = Date,index = idx_name) print(sr.index)

`

Output

Index(['Day 1', 'Day 2', 'Day 3', 'Day 4'], dtype='object')

**Explanation: The index labels (‘Day 1’ to ‘Day 4’) are assigned to a Series and retrieved using series.index.

**Example 3. Resetting Index to Default

If needed, we can reset the index to default integer values.

Python `

import pandas as pd

Date = ['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018'] idx_name = ['Day 1', 'Day 2', 'Day 3', 'Day 4']

sr = pd.Series(data = Date, # Series Data index = idx_name # Index )

Resetting index to default

sr.reset_index(drop=True, inplace=True) print(sr)

`

Output

0 1/1/2018 1 2/1/2018 2 3/1/2018 3 4/1/2018 dtype: object

**Explanation: reset_index(drop=True, inplace=True) removes the custom index and replaces it with the default integer index while modifying the Series in place.