Following the multi-indexing documentation code, I do the following:
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo'], ['one', 'two', 'one', 'two', 'one', 'two']] tuples = list(zip(*arrays)) index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) df2 = pd.DataFrame(np.random.randn(3, 6), index=['A', 'B', 'C'], columns=index)
The result is a data frame that looks like this:
first bar baz foo second one two one two one two A -0.398965 -1.103247 -0.530605 0.758178 1.462003 2.175783 B -0.356856 0.839281 0.429112 -0.217230 -2.409163 -0.725177 C -2.114794 2.035790 0.059812 -2.197898 -0.975623 -1.246470
My problem is that in my release (in an HTML table) I would like to group based on the index of the second level, not the first. An expression of something similar:
second one two first bar baz foo bar baz foo A -0.398965 -0.530605 1.462003 -1.103247 0.758178 2.175783 B -0.356856 0.429112 -2.409163 0.839281 -0.217230 -0.725177 C -2.114794 0.059812 -0.975623 2.035790 -2.197898 -1.246470
Is there an easy way to swap and rearrange column indexes?
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