Gapminder.drop_duplicates(subset="continent", keep="last") We can also keep the last occurrence of a column value by using the argument “keep=last”. We would expect that we will have just one row from each continent value and by default drop_duplicates() keeps the first row it sees with a continent value and drops all other rows as duplicates. Gapminder.drop_duplicates(subset = "continent") # drop duplicates based on value of a column Let us drop duplicate rows using the original gapminder data frame and use subset argument with “continent” Pandas drop_duplicates function has an argument to specify which columns we need to use to identify duplicates.įor example, to remove duplicate rows using the column ‘continent’, we can use the argument “subset” and specify the column name we want to identify duplicate. Often you might want to remove rows based on duplicate values of one ore more columns. How to Drop/remove Partially Duplicated Rows based on Select Columns?īy default drop_duplicates function uses all the columns to detect if a row is a duplicate or not. Gapminder_duplicated.drop_duplicates().shape # verify if all duplicated rows are dropped We can verify that we have dropped the duplicate rows by checking the shape of the data frame. # remove duplicated rows using drop_duplicates() By default, drop_duplicates() function removes completely duplicated rows, i.e. Pandas function drop_duplicates() can delete duplicated rows. Basically, every row in the original data frame is duplicated. Our new Pandas dataframe with duplicated rows has double the number of rows as the original gapminder dataframe. Gapminder_duplicated = pd.concat(,axis=0) We can see the outputs in the above output block, and the value “None” is the output from the drop_duplicates() method.# concatenate two dataframes with concat() function in Pandas ![]() The Pandas series is as follows − East Johnīy setting inplace=True, we have successfully updated the original series object with deleted rows. Result = series.drop_duplicates(inplace=True)īy setting the True value to the inplace parameter, we can modify our original series object with deleted rows and the method returns None as its output. # delete duplicate values with inplace=True Example 2įor the same example, we have changed the inplace parameter value from default False to True. Here the original series object does not affect by this method instead it returns a new series object. The drop_duplicate method returns a new series object with deleted rows. The Pandas series is given below − East John ![]() Index=)Īfter creating the series object we applied the drop_duplicate() method without changing the default parameters. # create pandas series with duplicate values In this following example, we have created a pandas series with a list of strings and we assigned the index labels also by defining index parameters. Also, we can change it to last and False occurrences. The default behavior of this parameter is “first” which means it drops the duplicate values except for the first occurrence. The other important parameter in the drop_duplicates() method is “Keep”. Instead, it will return a new one.īy using the inplace parameter, we can update the changes into the original series object by setting “inplace=True”. This method returns a series with deleted duplicate rows, and it won’t alter the original series object. ![]() To remove duplicate values from a pandas series object, we can use the drop_duplicate() method. In the process of analysing the data, deleting duplicate values is a commonly used data cleaning task. The main advantage of using the pandas package is analysing the data for Data Science and Machine Learning applications.
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