As you can observe, the input dataframe contains some duplicate rows.

In the above example, we have an input dataframe containing the Class, Roll, Name, Marks, and Grades of some students. You can observe this in the following example. After execution, it returns a dataframe containing all the unique rows. To drop duplicate rows from a pandas dataframe, you can invoke the drop_duplicates() method on the dataframe. Drop Duplicate Rows From a Pandas Dataframe If you want to refresh the index and assign the ordered index from 0 to (length of dataframe)-1, you can set ignore_index to True.Īfter execution, the drop_duplicates() method returns a dataframe if the inplace parameter is set to False. When rows are dropped from a dataframe, the order of the indices becomes irregular.To alter the original dataframe, you can set inplace to True. When inplace is set to False, which is its default value, the original dataframe isn’t changed and the drop_duplicates() method returns the modified dataframe after execution. The inplace parameter is used to decide if we get a new dataframe after the drop operation or if we want to modify the original dataframe.If we need to drop all the rows having duplicates, we can set the keep parameter to False. If we want to drop all the duplicate rows except the last occurrence, we can set the keep parameter to “last”. If we want to drop all the duplicate rows except the first occurrence, we can set the keep parameter to “first” which is its default value. The keep parameter is used to decide whether we want to keep one of the duplicate rows in the output dataframe.If you want to compare rows by two or more columns, you can pass the list of column names to the subset parameter. If you want to compare two rows by only a single column, you can pass the column name to the subset parameter as the input argument. Due to this, values from all the columns are used from rows for comparison. By default, the subset parameter is set to None. The subset parameter is used to compare two rows to determine duplicate rows.

Pandas drop duplicate rows