The data in your sample can often contain duplicate rows. This is just a reality of dealing with data that is collected automatically, or even a situation created when manually collecting data. In these situations, it is often considered best to error on the side of having duplicates instead of missing data, especially if the data can be considered to be idempotent. However, duplicate data can increase the size of the dataset, and if it is not idempotent, then it would not be appropriate to process the duplicates.
Pandas provides the .duplicates() method to facilitate finding duplicate data. This method returns a Boolean Series, where each entry represents whether or not the row is a duplicate. A True value represents that the specific row has appeared earlier in the DataFrame object, with all the column values identical.
The following demonstrates this...
