different types of Data Structures in Pandas?

In Pandas, there are two main data structures: Series and DataFrame.

1. Series: A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating-point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. A Series can be created using the pd.Series() function.

Example:

import pandas as pd
data = pd.Series([1, 2, 3, 4])
print(data)

Output:

0    1
1    2
2    3
3    4
dtype: int64

1. DataFrame: A data frame is a 2-dimensional labeled data structure with columns of potentially different types. It is like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used Pandas object. A data frame can be created using the pd.DataFrame() function.

Example:

import pandas as pd
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [25, 30, 35, 40]}
df = pd.DataFrame(data)
print(df)

Output:

       name  age
0     Alice   25
1       Bob   30
2   Charlie   35
3     David   40

In addition to these two main data structures, Pandas also provides other data structures such as Panel, Panel4D, and PanelND, which are less commonly used.