Having established a foundation with NumPy for numerical computation, we now focus on Pandas, a library optimized for working with structured data in Python. NumPy arrays are powerful for numerical tasks, but data analysis often involves tabular data with labels, mixed data types, and missing values. Pandas provides high-performance, easy-to-use data structures and data analysis tools specifically for these scenarios.

This chapter introduces the core components of Pandas:

• The fundamental data structures: the one-dimensional Series and the two-dimensional DataFrame.
• Methods for creating Series and DataFrames from various sources, including Python lists, dictionaries, and NumPy arrays.
• Essential techniques for inspecting DataFrames to understand their structure, contents, and data types using functions like head(), info(), and describe().

By the end of this chapter, you will be able to create and examine the basic building blocks used for most data manipulation tasks in Pandas. A practical session will reinforce these concepts.