2. Pandas I

The Introduction chapter described the data science lifecycle: we formulate questions, acquire data, explore it, and draw conclusions. Once we have data (a “box of data”), we need to work with it in practice. Pandas is the industry-standard Python library for tabular data. This chapter connects the lifecycle’s “data acquisition” and “understand the data” stages to the tools you will use in every later chapter: DataFrames, Series, and the basics of loading, inspecting, and extracting data.

If you have seen CSCI 8’s datascience library and Tables, pandas DataFrames and Series are the analogous concepts in the wider Python ecosystem. Mastering pandas syntax here sets you up for EDA, visualization, and modeling.

Why tabular data?¶

Data scientists often work with tabular data: data in rows and columns, like a spreadsheet. Each row is typically one observation (e.g., one person, one year, one event); each column is a variable. Pandas gives us DataFrames (the whole table) and Series (a single column). Along with an Index (labels for rows and columns), these structures let us load data from files (e.g., CSV), inspect it, and extract subsets using position (.iloc) or labels (.loc and []).

What you will learn¶

This chapter introduces pandas syntax, operators, and functions: creating or reading DataFrames, understanding structure (rows, columns, index), and data extraction with loc, iloc, and bracket notation. You will also review how Python data structures (arrays, lists, dictionaries) relate to working with tabular data. The goal is to feel comfortable opening a dataset and selecting the rows and columns you need for analysis.