The need for data structures
Consider that you are working with quarterly gross domestic product (GDP) data for the US. A natural way to think about the data and work with it would be to use it in a table. An example might be viewing the data in spreadsheet software, as shown here:
Figure 2.1 – Tabular data
In Figure 2.1, you see two columns of data. The spreadsheet software has labeled the columns with letters and the rows with numbers. In addition, the column names representing the data (date, GDP) are present in the first row.
The table shown in Figure 2.1 is a data structure. Having this data in two columns makes it easier to understand and work with. However, in the spreadsheet, it's complicated to work with the data as a single object (a table). This is where pandas gives you an edge over the core Python data structures (and over spreadsheets). As you saw in Chapter 1, Introduction to pandas, in pandas you can refer to the entire dataset...
