Now that the column names are indicative of the data they contain, we can check what types of data they hold. We should have formed an intuition as to what the data types should be after looking at the first few rows when we inspected the dataframe with the head() method previously. With type conversion, we aim to reconcile what the current data types are with what we believe they should be; we will be changing how our data is represented.
Note that, sometimes, we may have data that we believe should be a certain type, such as a date, but it is stored as a string; this could be for a very valid reason—data could be missing. In the case of missing data encoded as text (for example, ?), pandas will store it as a string when ...
