concept axis in category dask

This is an excerpt from Manning's book Data Science with Python and Dask. Login to get full access to this book.

Like a spreadsheet or a database table, DataFrames are organized into rows and columns. However, we have a few additional terms to be aware of when working with DataFrames: indexes and axes. Figure 3.3 displays the anatomy of a DataFrame.

Figure 3.3 A Dask representation of the structured data example from figure 3.2

The example in figure 3.3 shows a DataFrame representation of the structured data from figure 3.2. Notice the additional labels on the diagram: rows are referred to as “axis 0” and columns are referred to as “axis 1.” This is important to remember when working with DataFrame operations that reshape the data. DataFrame operations default to working along axis 0, so unless you explicitly specify otherwise, Dask will perform operations row-wise.

So far, we haven’t done much with the NYC Parking Ticket dataset beyond choosing appropriate datatypes for each column and reading the data into Dask. Now that the data is loaded and ready for us to start exploring, a good place for us to ease into our exploration is by learning how to navigate the DataFrame’s index and axes. Let’s start with something simple: selecting and filtering columns.