Using the Pandas drop_duplicates function, you can drop duplicate rows from your dataframe. The function is a simple matter of iterating over each column of the dataframe, and removing the duplicates. The function also keeps track of the first time a particular row occurs. As a result, your dataframe will be free of duplicates, and you will have the power to sort your data in a more efficient manner. You can even use it to extract rows from the original dataframe, as long as you cast the dataframe as a str.
The most arduous task is to come up with the proper list structure for your data. You can use a tuple for your list, which is similar to a list but is a little more efficient. Using a tuple allows you to keep track of the first time a row occurs. This is a much more efficient way of handling duplicates.
The other is the list comprehension, which is a good way to keep track of the first time a particular item appears. In addition to being more efficient, this method also allows you to generate one list once. The function is a little more powerful than its cousin, as it can also keep track of the first time a row appears multiple times. This can be especially useful in the event that you have a dataframe with many rows. You can even use it to check the membership of your list, which is faster than checking the membership of your dataframe.
There are actually three ways to do this. The first uses a list, which is the simplest way to do it, but will only work for lists in pandas dataframes. The second uses a dictionary, which is also a good way to keep track of the stipulations of the first time a particular row appears. Finally, there is the python oblique mention of the drop_duplicates function, which allows you to drop duplicate rows in your dataframe without requiring you to actually store them in pandas dataframes. This method is also the easiest to use, as all you need to do is cast your dataframe as a str. You will not be able to use the list comprehension to keep track of the first time a single row appears multiple times, but it can work for you if you only have a few rows.
The best way to learn all of these is to actually use drop_duplicates on your dataframe. This is a simple trick that will save you many headaches in the future, and allow you to extract rows from your dataframe that you might have missed. As with any other Pandas feature, you can also learn the best way to use it by reading the help file. It might be a little difficult to find a sample example of the list comprehension, but it is a very easy function to learn.