How to drop duplicates based on two or more subsets criteria in Pandas data-frame

Lets say this is my data-frame

df = pd.DataFrame({ 'bio' : ['1', '1', '1', '4'],
                'center' : ['one', 'one', 'two', 'three'],
                'outcome' : ['f','t','f','f'] })

It looks like this …

  bio center outcome
0   1    one       f
1   1    one       t
2   1    two       f
3   4  three       f

I want to drop row 1 because it has the same bio & center as row 0.
I want to keep row 2 because it has the same bio but different center then row 0.

Something like this won’t work based on drop_duplicates input structure but it’s what I am trying to do

df.drop_duplicates(subset = 'bio' & subset = 'center' )

Any suggestions ?

edit : changed df a bit to fit example by correct answer

Answers:

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Method 1

Your syntax is wrong. Here’s the correct way:

df.drop_duplicates(subset=['bio', 'center', 'outcome'])

Or in this specific case, just simply:

df.drop_duplicates()

Both return the following:

  bio center outcome
0   1    one       f
2   1    two       f
3   4  three       f

Take a look at the df.drop_duplicates documentation for syntax details. subset should be a sequence of column labels.

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