Data frames and series are fundamental data structures used in data analysis, particularly with the Pandas library in Python. A DataFrame is a two-dimensional, tabular data structure that consists of rows and columns. Each column can hold different data types, making it versatile for storing various types of data. DataFrames are particularly useful for organizing and manipulating datasets, allowing for complex data analysis and operations.

On the other hand, a Series is a one-dimensional array-like structure that can hold any data type. It is often considered a single column of a DataFrame. A Series can be created from various data sources, such as lists, NumPy arrays, or dictionaries, and it can also have an associated index for easy data retrieval.

Both DataFrames and Series are essential for performing data manipulation, analysis, and visualization tasks in data science projects, as they provide a structured way to handle and analyze data efficiently ¹⁴.