Handling duplicate data
The data in your sample can often contain duplicate rows. This is just a reality of dealing with data collected automatically, or even a situation created in manually collecting data. Often, it is considered best to err 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.
To facilitate finding duplicate data, pandas provides a .duplicates() method that 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 ...
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