A simple pandas question:
Is there a drop_duplicates()
functionality to drop every row involved in the duplication?
An equivalent question is the following: Does pandas have a set difference for dataframes?
For example:
In [5]: df1 = pd.DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
In [6]: df2 = pd.DataFrame({'col1':[4,2,5], 'col2':[6,3,5]})
In [7]: df1
Out[7]:
col1 col2
0 1 2
1 2 3
2 3 4
In [8]: df2
Out[8]:
col1 col2
0 4 6
1 2 3
2 5 5
so maybe something like df2.set_diff(df1)
will produce this:
col1 col2
0 4 6
2 5 5
However, I don't want to rely on indexes because in my case, I have to deal with dataframes that have distinct indexes.
By the way, I initially thought about an extension of the current drop_duplicates()
method, but now I realize that the second approach using properties of set theory would be far more useful in general. Both approaches solve my current problem, though.
Thanks!