Pandas Series Index Attribute

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:

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)

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.

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)

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.

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)

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.

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)

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.