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A set is basically a collection of unique items. Return object._getattribute_(self, name)ĪttributeError: 'DataFrame' object has no attribute 'unique' Python setĪnother way to get unique values is by using s set, a data structure in Python. unique() on a pandas Dataframe, we’ll get an error message because this method doesn’t exist on Dataframe! > df.unique()įile "C:\Program Files\Python38\lib\site-packages\pandas\core\generic.py", line 5274, in _getattr_
#Excel remove duplicates series
unique() on a pandas Series object, it returns a list of unique elements from that column > df.unique()Īrray(, unique() method however, pandas Dataframe doesn’t have this method.
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In other words, a Dataframe consists of various Series. A pandas Series is a column in that table/sheet. A pandas Dataframe is a table or a sheet. pandas Series vs pandas Dataframeįor Excel users, it is easy to remember their difference. I mean, we could, but there are better ways to find unique values. In this case, we wouldn’t use the drop_duplicate(). Sometimes we want to find unique values in a list of a dataframe column. > dfĥ Mary Jane CANADA Toronto F 30 Finding unique values in a list or data table column If we specify inplace=True, the original df will be replaced with the new dataframe with duplicates removed. It’s not changed! That’s because we left inplace argument blank, which by default is False. Record #1 and 3 got dropped because they were the first duplicated values from that column. Now pandas will check for duplicates in the “User Name” column, and drop them accordingly. In the 2nd round, we passed in a column name “User Name”, also we told pandas to keep the last duplicates. > df.drop_duplicates('User Name', keep='last') The first duplicated value remained as a result. The only wholly duplicated record was record #5, which got dropped. In the above code, we chose to not pass any argument, which means we check on all columns for duplicates.
#Excel remove duplicates how to
We’ll see how to handle both situations with different techniques. The two most common scenarios are: removing duplicates from the entire table or finding unique values from a column. Line 3 and 4 contain the same User Name, but different Country and City Remove duplicatesĭepending on what you are trying to achieve, we can use different approaches to remove duplicates.Line 1 and 5 contain exact same information.import pandas as pdĪ quick observation of the above small table: If you are not familiar with using Python to work with Excel files, check out here for a Python vs. First thing, let’s load the spreadsheet into Python.
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#Excel remove duplicates download
You can download this sample Excel spreadsheet to follow along. This tutorial is part of the “Integrate Python with Excel” series, you can find the table of content here for easier navigation. So today, we’ll explore how to use Python to remove duplicates from a data table. However, when the datasets are too big, or there are formulas in the spreadsheet, this can sometimes be slow.
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In Excel, we can “easily” remove duplicates from a table by clicking on the “Remove Duplicates” button from the Data tab.