It has been published that slicing at the second index can be done in the multi-indexed pandas series:
import numpy as np import pandas as pd buckets = np.repeat(range(3), [3,5,7]) sequence = np.hstack(map(range,[3,5,7])) s = pd.Series(np.random.randn(len(sequence)), index=pd.MultiIndex.from_tuples(zip(buckets, sequence))) print s 0 0 0.021362 1 0.917947 2 -0.956313 1 0 -0.242659 1 0.398657 2 0.455909 3 0.200061 4 -1.273537 2 0 0.747849 1 -0.012899 2 1.026659 3 -0.256648 4 0.799381 5 0.064147 6 0.491336
Then, to get the first three rows for the first index = 1, you simply say:
s[1].ix[range(3)] 0 -0.242659 1 0.398657 2 0.455909
This works great for one-dimensional series, but not for DataFrames:
buckets = np.repeat(range(3), [3,5,7]) sequence = np.hstack(map(range,[3,5,7])) d = pd.DataFrame(np.random.randn(len(sequence),2), index=pd.MultiIndex.from_tuples(zip(buckets, sequence))) print d 0 1 0 0 1.217659 0.312286 1 0.559782 0.686448 2 -0.143116 1.146196 1 0 -0.195582 0.298426 1 1.504944 -0.205834 2 0.018644 -0.979848 3 -0.387756 0.739513 4 0.719952 -0.996502 2 0 0.065863 0.481190 1 -1.309163 0.881319 2 0.545382 2.048734 3 0.506498 0.451335 4 0.872743 -0.070985 5 -1.160473 1.082550 6 0.331796 -0.366597 d[1].ix[range(3)] 0 0 0.312286 1 0.686448 2 1.146196 Name: 1
It gives you the “1st” data column and the first three rows, regardless of the first level of the index. How can you get the first three rows for the first index = 1 for a multi-indexed DataFrame?
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