Pandas dataframe remove duplicate rows series#
You may also find useful to know about about df.duplicated()which returns boolean series denoting duplicate rows. Identify duplicate rows with df.duplicated()
This can be achieved by using groupby method. Lets have a look at the Pandas Dataframe which contains duplicates values according to two columns (A and B) and where you want to remove duplicates keeping the row with max value in column C. The second way to drop duplicate rows across multiple columns is to use the df.groupby() method.
Or it can contain multiple columns that you want to use to identify duplicates.ĭf.drop_duplicates(subset=) drop_duplicates() method with subset columns for removing duplicates Your subset can be one column if you want to identify duplicates in this one specific column and keep first ocurance.ĭf.drop_duplicates(subset=) However if you need to drop duplicate rows across specific column(s), use subset. So when you use the df.drop_duplicates() method, those duplicates will be removed. In this Dataframe row 0 is a duplicate of row 5, and row 2 is a duplicate of row 6. Let's create a Dataframe with some duplicate rows. drop_duplicates (inplace=True).Įxample: Delete all duplicate rows from the Dataframe Please note that if you want to modify Dataframe inplace you will need to specify inplace parameter df. drop_duplicates () without specifying parameters. So if you simply need to delete all duplicate rows from the Dataframe you can use df. Pandas drop_duplicates() method returns Dataframe with duplicate rows removed.ĭataFrame.drop_duplicates (subset=None, *, keep='first', inplace=False, ignore_index=False)īy default, this method removes duplicate rows based on all columns. The first and the easiest way to remove duplicate rows in your Pandas Dataframe is to use the drop_duplicates() method.
Pandas dataframe remove duplicate rows how to#
In this blog post, we'll explore how to drop all duplicate rows and how to drop duplicate rows across multiple columns in Python Pandas. This can be a tricky task, but luckily there are a few different ways to go about it. If you're working with data in Python Pandas, you may find yourself needing to drop duplicate rows across multiple columns.
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