Attributes of Series

A Series object in Pandas is more than just a labelled array — it carries several built-in properties, called attributes, that let you inspect or label the data directly. You access an attribute by writing the Series name, a dot, and the attribute name (e.g., seriesName.attribute). The textbook uses the Series seriesCapCntry (which maps countries to their capitals) to demonstrate each one.

The name attribute

Every Series can have a name — a label for the entire data column. Initially, a Series has no name. You assign one using series.name = 'some string'. Once set, the name appears in the output when you print the Series, right after the data, as Name: Capitals, dtype: object.

seriesCapCntry.name = 'Capitals'
print(seriesCapCntry)

Output:

India        NewDelhi
USA       WashingtonDC
UK              London
France           Paris
Name: Capitals, dtype: object

The index.name attribute

The index itself can also be given a name. This is separate from the Series name. You set it with series.index.name = 'some string'. When printed, the index name appears as a header above the index labels.

seriesCapCntry.index.name = 'Countries'
print(seriesCapCntry)

Output:

Countries
India        NewDelhi
USA       WashingtonDC
UK              London
France           Paris
Name: Capitals, dtype: object

Notice that Countries now sits above the index column, while Capitals remains the name of the data column.

The values attribute

values returns a NumPy array containing only the data values — the index labels are stripped away. This is useful when you need to work with the raw data without labels.

print(seriesCapCntry.values)

Output:

['NewDelhi' 'WashingtonDC' 'London' 'Paris']

The result is an array of strings (dtype object), not a Series.

The size attribute

size gives the total number of elements in the Series, including any missing values (NaN). It is an integer.

print(seriesCapCntry.size)

Output:

4

The empty attribute

empty returns a Boolean — True if the Series has zero elements, False otherwise. This is a quick check before performing operations that require data.

print(seriesCapCntry.empty)   # False, because it has 4 elements

# Create an empty Series
seriesEmpt = pd.Series()
print(seriesEmpt.empty)       # True

Activity 2.3 (from the textbook)

The textbook includes a small activity to test your understanding. Given this code:

import pandas as pd
import numpy as np …