How to create a multi-index in Pandas

Question There are two questions that look similar but they're not the same question: here and here. They both call a method of `GroupBy`, such as `count()` or `aggregate()`, which I know returns a `DataFrame`. What I'm asking is how to convert the `GroupBy` (class `pandas.core.groupby.DataFrameGroupBy`) object itself into a `DataFrame`. I'll illustrate below. Example Construct an example `DataFrame` as follows. ``` data_list = [] for name in ["sasha", "asa"]: for take in ["one", "two"]: row = {"name": name, "take": take, "score": numpy.random.rand(), "ping": numpy.random.randint(10, 100)} data_list.append(row) data = pandas.DataFrame(data_list) ``` The above `DataFrame` should look like the following (with different numbers obviously). ``` name ping score take 0 sasha 72 0.923263 one 1 sasha 14 0.724720 two 2 asa 76 0.774320 one 3 asa 71 0.128721 two ``` What I want to do is to group by the columns "name" and "take" (in that order), so that I can get a `DataFrame` indexed by the multiindex constructed from the columns "name" and "take", like below. ``` score ping name take sasha one 0.923263 72 two 0.724720 14 asa one 0.774320 76 two 0.128721 71 ``` How do I achieve that? If I do `grouped = data.groupby(["name", "take"])`, then `grouped` is a `pandas.core.groupby.DataFrameGroupBy` instance. What is the correct way of doing this?