["python","pandas","dataframe"]
set difference for pandas

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!
from pandas import DataFrame df1 = DataFrame({'col1':[1,2,3], 'col2':[2,3,4]}) df2 = DataFrame({'col1':[4,2,5], 'col2':[6,3,5]}) print(df2[~df2.isin(df1).all(1)]) print(df2[(df2!=df1)].dropna(how='all')) print(df2[~(df2==df1)].dropna(how='all'))