4.3.1. Series: One-Dimensional Labeled Array

A Series is like a column of data, similar to a Python list, but with labels (called index) for each value.

A Series in Pandas is a one-dimensional labeled array that can hold any data type. It is similar to a list in Python but has an additional feature—each data point (value) can be identified with an index label. This allows for more meaningful access and manipulation of the data.

Think of a Series like a contact list on your phone, where each name (label) corresponds to a specific phone number (value). Instead of just having numbers like in a classic phone directory, you can quickly find the information you want by looking up the name.

import pandas as pd
s = pd.Series([10, 20, 30, 40])
print(s)

In this code, we first import the Pandas library and then create a new Series named 's' that contains four integer values. The print(s) statement displays the Series to the console. Pandas assigns default index labels to each value automatically, which can be seen when we print the Series.

You can think of creating a Series like filling out a form where each row represents a different person's age (the values), and their positions in the document act like the index (0 for the first person, 1 for the second, and so on).

Output:

0    10
1    20
2    30
3    40
dtype: int64

This output shows how the Series is presented. The left-hand side of the output displays the index (0, 1, 2, 3), while the right-hand side shows the corresponding values (10, 20, 30, 40). The dtype: int64 indicates that the data type of the values in the Series is a 64-bit integer.

Imagine your Series is like a list of students' grades in a class. The index represents each student's position in the list (like their roll number), and the values represent their grades. When you look at the list, it's easy to see which student has which grade at a glance.

--