![]() Also explained how to use the keep parameter that takes ‘first/last/false’ values, which controls the deletion of duplicate values. In this article I have explained how to drop duplicates based on Index using Index.drop_duplicates() function. Related: Pandas Get List of All Duplicate Rows Conclusion So after applying drop_duplicates(keep=’first’) on Index object idx, all the duplicates in the Index has been dropped by keeping the first occurences. Idx2 = idx.drop_duplicates(keep ='first') Extract the index from a DataFrame using pd.DataFrame.index. # Drop Duplicates Except the First Occurrence DataFrame.duplicated() to remove rows with duplicate indices. By default ‘ first‘ is taken as a value to the keep parameter. Now drop all occurrences of duplicates in the Index except the first occurrence. Drop Duplicates Except the First Occurrence ![]() # Drop all duplicate occurrences of the indexįollowing is the output for the above example, where you see all the duplicates are removed.Ģ. Now, let’s drop all occurrences of duplicate values in the Index by using drop_duplicates() as shown below, I am using keep=False as I wanted to remove all occurance of duplicates. In order to explain this with example, first, lets create an Index which contains duplicates values as show in below. Sometimes you may have duplicates in pandas index and you can drop these using index.drop_duplicates() (dropduplicates). Pandas dropduplicates() returns only the dataframes unique values, optionally only considering certain columns. This is used to store axis labels for all pandas objects. Pandas Index is a immutable sequence used for indexing and alignment. The value ‘ first’ keeps the first occurrence for each set of duplicated entries. The parameter ‘ keep‘ controls which duplicate values should be removed. import Pandas dropduplicates () function removes duplicate rows from the DataFrame. This return Index with duplicate values removed. To drop duplicate columns from pandas DataFrame use df.
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