Summary  
Creating and inspecting one-dimensional labeled data structures by constructing pandas Series from lists or dictionaries and retrieving their metadata (index, dtype, name, size).

General domain of usage  
Data analysis

import pandas as pd  

scores = pd.Series([88, 92, 79, 93], index=["Alice", "Bob", "Carol", "Dan"], name="Exam Scores")  

print(scores)
print(scores.index)
print(scores.dtype)
print(scores.size)

A **Series** is the simplest pandas data structure: a one-dimensional labeled array. You can think of it as a single column of a table, with **values** and an **index** that labels them.

Definition

You can create a Series from common Python data structures, such as dictionaries and lists.

import pandas as pd

scores = pd.Series({"Alice": 88, "Bob": 92, "Carol": 79, "Dan": 93})  
print(scores)

Here, dictionary keys become the index labels.

import pandas as pd

grades = pd.Series([88, 92, 79, 93])  
print(grades)

Pandas automatically generates an index `(0, 1, 2, 3)`.

A Series also carries information about itself:

import pandas as pd

grades = pd.Series([88, 92, 79, 93], name="Exam Scores")  

print(grades.index)
print(grades.dtype)
print(grades.name)
print(grades.size)

- `.index`: index labels;
- `.dtype`: data type of values;
- `.name`: name of the Series;
- `.size`: number of elements.

What are the two main components of a pandas Series?

Which attribute shows the data type of a Series?

Understanding Series

AI in Action

Creating Series

From a Dictionary

From a List

Metadata

1. What are the two main components of a pandas Series?

2. Which attribute shows the data type of a Series?

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