Often you might want to remove rows based on duplicate values of one ore more columns.
#PANDAS DROP DUPLICATE ROWS HOW TO#
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) # concatenate two dataframes with concat() function in Pandas Here we specify axis=0 so that concat joins two dataframes by rows. We can join two dataframes using Pandas’ concat function. After concatenating, we will have each row duplicated completely two times. To illustrate how to drop rows that are duplicated completely, let us concatenate the gapminder dataframe with a copy of its own. gapminder data set is well curated one, so there is not any row that is completely duplicated. How to Drop/remove Completely Duplicated Rows?įirst, let us create dataframe with duplicated rows. We can see that it has 1704 rows and 6 columns. We will use gapminder dataset from Carpentries. Sometime, you may have to make a decision if only part of a row is duplicated. We can drop the duplicated row for any downstream analysis. If the whole row is duplicated exactly, the decision is simple.
One of the common data cleaning tasks is to make a decision on how to deal with duplicate rows in a data frame. Pandas drop_duplicates(): remove duplicated rows from a dataframe
We will use Pandas drop_duplicates() function to can delete duplicated rows with multiple examples. In this post, we will learn how to drop duplicate rows in a Pandas dataframe.
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