Remove Duplicate Rows in place source_df. So, rows 1 and 2 are removed from the output. The columns ‘A’ and ‘B’ are used to identify duplicate rows. Drop duplicate rows in pandas python dropduplicates () Delete or Drop duplicate rows in pandas python using dropduplicate () function Drop the duplicate rows in pandas by retaining last occurrence Delete or Drop duplicate in pandas by a specific column name Delete All Duplicate Rows from DataFrame. Result_df = source_df.drop_duplicates(subset=) By default, all the columns are used to find the duplicate rows. df.sortvalues('var2', ascendingFalse).dropduplicates('var1').sortindex() Method 2: Remove Duplicates in Multiple Columns and Keep. subset: column label or sequence of labels to consider for identifying duplicate rows. You can use the following methods to remove duplicates in a pandas DataFrame but keep the row that contains the max value in a particular column: Method 1: Remove Duplicates in One Column and Keep Row with Max.Its syntax is: drop_duplicates(self, subset=None, keep="first", inplace=False)
#PANDAS REMOVE DUPLICATE ROWS CODE#
The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.Pandas drop_duplicates() function removes duplicate rows from the DataFrame. With this, we come to the end of this tutorial. In the above example, none of the duplicates are retained.įor more on the pandas dataframe drop_duplicates() function refer to its official documentation. import pandas as pdĭf_unique = df.drop_duplicates(keep=False) If you do not want to retain any of the duplicate rows pass keep=False to the drop_duplicates() function. Remove duplicates and do not retain any occurrences In the above example, we retain the last duplicate instead of the first one. import pandas as pdĭf_unique = df.drop_duplicates(keep='last') If you want to retain the last duplicate row instead of the first one pass keep='last' to the drop_duplicates() function. Remove duplicates and retain the last occurrence With this criteria, rows with index 1, 2, and 3 are now duplicates with the returned dataframe only retaining the first row. In the above example, we identify the duplicates based on just the columns Pet and Color by passing them as a list to the drop_duplicates() function.
import pandas as pdĭf_unique = df.drop_duplicates(subset=) You can also instruct the drop_duplicates() function to identify the duplicates based on only certain columns by passing them as a list to the subset argument. Drop duplicate rows based on certain columns If you want the returned dataframe to have a continuous index pass ignore_index=True to the drop_duplicates() function or reset the index of the returned dataframe. As a result, the dataframe returned does not have a continuous index. On applying the drop_duplicates() function, the first row is retained and the remaining duplicate rows are dropped. In the above example, you can see that the rows with index 1 and 2 have the same values for all the three columns. # create a sample dataframe with duplicate rows It then, drops the duplicate rows and just keeps their first occurrence. Drop duplicate rows based on all columnsīy default, the drop_duplicates() function identifies the duplicates taking all the columns into consideration. Let’s look at some of the use-cases of the drop_duplicates() function through examples – 1. To modify the dataframe in-place pass the argument inplace=True. You can change this behavior through the parameter keep which takes in 'first', 'last', or False. It drops the duplicates except for the first occurrence by default. It returns a dataframe with the duplicate rows removed. The following is its syntax: df.drop_duplicates() It also gives you the flexibility to identify duplicates based on certain columns through the subset parameter. The pandas dataframe drop_duplicates() function can be used to remove duplicate rows from a dataframe. Pandas DataFrame.dropduplicates() will remove any duplicate rows (or duplicate subset of rows) from your DataFrame.
#PANDAS REMOVE DUPLICATE ROWS HOW TO#
In this tutorial, we’ll look at how to drop duplicates from a pandas dataframe through some examples. Knowing how to remove such rows quickly can be quite handy. While working with data there can be situations where your dataframe has duplicate rows.
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