Step 1 formally creates our groupby object. It is useful to display all the public attributes and methods to reveal all the possible functionality as was done in step 2. Each group is uniquely identified by a tuple containing a unique combination of the values in the grouping columns. Pandas allows you to select a specific group as a DataFrame with the get_group method shown in step 5.

It is rare that you will need to iterate through your groups and in general, you should avoid doing so if necessary, as it can be quite slow. Occasionally, you will have no other choice. When iterating through a groupby object, you are given a tuple containing the group name and the DataFrame without the grouping columns. This tuple is unpacked into the variables name and group in the for-loop in step 6.

One interesting thing you can do while iterating through your groups is to display a few of the rows from each group directly in the notebook. To do this, you can either use the print function or the display function from the IPython.display module. Using the print function results in DataFrames that are in plain text without any nice HTML formatting. Using the display function will produce DataFrames in their normal easy-to-read format.