In the context of this exercise, when should I generally use the
reset_index() method in Pandas?
You can always use the method any time you’d like, but there are some common situations in which it would be particularly useful to apply.
One common situation, which is given in the example text of the exercise, is when the dataframe has non-consecutive indices, meaning the indices are not sequential like
0, 1, 2,… and skip some values. This can occur when we create a new dataframe by selecting the rows from another dataframe, causing some of the indices to be out of order. By using
reset_index() it will set the indices in order, starting from 0, and make it easier for us to work with the dataframe.
Another situation you might encounter is when the default index column was changed to some other values other than integers, such as the values of another column in the dataframe. For example, we might have updated the index column of a dataframe for country populations to be the country names, instead of integers. This is possible because in Pandas we have the option to change the index values from the default, integer values, to the values of another column in the dataframe, which can be strings or other value types. If we need to change the first column back to the integer indices, we can use