Having explored numerical computation with NumPy, we now turn our attention to managing and manipulating structured data, a fundamental task in any machine learning project. Real-world data is rarely clean or perfectly formatted for analysis. This chapter introduces the Pandas library, the standard Python tool for data wrangling.

You will learn about the core Pandas data structures, the one-dimensional Series and the two-dimensional DataFrame, which provide powerful and flexible ways to handle tabular data. We will cover essential operations including:

• Loading data from various file formats like CSV, Excel, and SQL databases.
• Selecting subsets of data using indexing methods such as .loc and .iloc.
• Techniques for identifying and handling missing values.
• Methods for cleaning, transforming, and reshaping datasets.
• Performing group-wise analysis and aggregations using groupby.
• Combining data from multiple sources through merging, joining, and concatenation.
• Working effectively with time series data.

By the end of this chapter, you will be equipped to use Pandas to efficiently prepare diverse datasets for analysis and machine learning model building.