I would suggest using the duplicated method on the Pandas Index itself:
df3 = df3[~df3.index.duplicated(keep='first')]
While all the other methods work, .drop_duplicates
is by far the least performant for the provided example. Furthermore, while the groupby method is only slightly less performant, I find the duplicated method to be more readable.
Using the sample data provided:
>>> %timeit df3.reset_index().drop_duplicates(subset="index", keep='first').set_index('index')
1000 loops, best of 3: 1.54 ms per loop
>>> %timeit df3.groupby(df3.index).first()
1000 loops, best of 3: 580 µs per loop
>>> %timeit df3[~df3.index.duplicated(keep='first')]
1000 loops, best of 3: 307 µs per loop
Note that you can keep the last element by changing the keep argument to 'last'
.
It should also be noted that this method works with MultiIndex
as well (using df1 as specified in Paul’s example):
>>> %timeit df1.groupby(level=df1.index.names).last()
1000 loops, best of 3: 771 µs per loop
>>> %timeit df1[~df1.index.duplicated(keep='last')]
1000 loops, best of 3: 365 µs per loop