![]() ![]() You can, of course, also combine this with the keep parameter to determine which duplicates to keep. For example, if you want to find duplicates based on the species column, you can do the following. If you want to find duplicates based on a single column, you can use the subset parameter. ![]() In the default example, duplicated() is looking at the entire row to determine if it is a duplicate. It also considers the first row to be unique, so the first row will always be False, since it doesn’t become a duplicate until the next occurrence is encountered.įind duplicates based on a single column with subset Note that this just returns a series by default, with the numbers of the rows as the index.īy default, duplicated() considers the entire row to be a duplicate if all the values in the row are the same. The default behavior is to return True if the row is a duplicate of a previous row. This method returns a boolean series indicating whether a row is a duplicate. Use duplicated() to return a boolean series indicating whether a row is a duplicateįirst, we’ll look at the duplicated() method. You will also need to import the pandas package as pd to make it easier to reference later on.ĭata = df = pd. The drop means removing the data from the given dataframe and the duplicate means same data occurred more than once. To get started, you will need to open a new Jupyter Notebook and import the pandas package. We’ll handle everything from rows that are completely duplicated (exact duplicates), to rows that include duplicate values in just one column (duplicate keys), and those that include duplicate values in multiple columns (partial duplicates). In this post, you will learn how to identify duplicate values using the duplicated() method and how to remove them using the drop_duplicates() method.
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