Table Talk: Meet pandas

Raw data rarely comes in a form that’s easy to analyze. It’s messy, inconsistent, and often unstructured—full of missing values, mixed types, and confusing layouts. To make sense of this chaos, we must first convert it into a format that computers and humans can easily work with.

That’s where pandas come in. By converting raw data into structured objects like Series and DataFrames, pandas allows us to organize, clean, and manipulate data efficiently. This transformation is the first step to extracting insights and answering complex questions.

In this lesson, we’ll dive into the fundamental building blocks of pandas: Series and DataFrames. Think of these as the language and workspace where all data cleaning, transformation, and analysis happen.

What is a Series?

A Series is a one-dimensional array-like structure with labels, also called indexes, for each data element. We can think of it as a single column of data in a spreadsheet, where each cell has a label attached to it.