![]() – first: Drop duplicates except for the first occurrence , default ‘first’ĭetermines which duplicates (if any) to keep. OptionsĬolumn label or sequence of labels, optionalĬolumn label certain columns for identifying duplicates, by default use all of the columns. Here is the in-detailed description of available options for drop_duplicates() function. Keep option is set to False to remove all the occurrences of duplicate column(s) df.drop_duplicates(subset='column_name', keep=False) Keep option is set to ‘first’ to remove duplicates and keep the first occurrences only df.drop_duplicates(subset='column_name', keep='first') Keep option is set to ‘ last’ to remove duplicates and keep the last occurrences only df.drop_duplicates(subset='column_name', keep='last') List of column name is passed in subset to remove duplicates from multiple columns df.drop_duplicates(subset=) Subset is used to remove duplicates from specific column df.drop_duplicates(subset='column_name') Remove duplicates from entire dataset df.drop_duplicates() The syntax is divided in few parts to explain the functions potential. drop_duplicates() function allows us to remove duplicate values from the entire dataset or from specific column(s) Removing duplicates is a part of data cleaning. ![]() ![]() While working with the dataset at times situation demands for the unique entries only at that time we have to remove duplicate values from the dataset.In this section, we will learn everything about how to drop duplicates using drop_duplicates() function in python pandas.Python pandas drop duplicates case sensitive.Pandas drop duplicates multiple columns.Python pandas drop duplicates keep last.Python Pandas drop duplicates based on column.
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