split data frame based on integer index

Question

In pandas how do I split Series/dataframe into two Series/DataFrames where odd rows in one Series, even rows in different? Right now I am using

rng = range(0, n, 2)
odd_rows = df.iloc[rng]

This is pretty slow.

Answer 1

Use slice:

In [11]: s = pd.Series([1,2,3,4])

In [12]: s.iloc[::2]  # even
Out[12]:
0    1
2    3
dtype: int64

In [13]: s.iloc[1::2]  # odd
Out[13]:
1    2
3    4
dtype: int64

Answer 2

Here's some comparisions

In [100]: df = DataFrame(randn(100000,10))

simple method (but I think range makes this slow), but will work regardless of the index (e.g. does not have to be a numeric index)

In [96]: %timeit df.iloc[range(0,len(df),2)]
10 loops, best of 3: 21.2 ms per loop

The following require an Int64Index that is range based (which is easy to get, just reset_index()).

In [107]: %timeit df.iloc[(df.index % 2).astype(bool)]
100 loops, best of 3: 5.67 ms per loop

In [108]: %timeit df.loc[(df.index % 2).astype(bool)]
100 loops, best of 3: 5.48 ms per loop

make sure to give it index positions

In [98]: %timeit df.take(df.index % 2)
100 loops, best of 3: 3.06 ms per loop

same as above but no conversions on negative indicies

In [99]: %timeit df.take(df.index % 2,convert=False)
100 loops, best of 3: 2.44 ms per loop

This winner is @AndyHayden soln; this only works on a single dtype

In [118]: %timeit DataFrame(df.values[::2],index=df.index[::2])
10000 loops, best of 3: 63.5 us per loop