Numpy tricks (again)

A small post about different tricks I use in numpy. Since frequently I work with statistical data and different statistical criterions, frequently I have to compute the order statistics (alternatively, one can ask about which position will get every number in array after array was sorted)
There is rankdata function inside scipy which solves the named problem:

result = rankdata(array)

However, one can easily implement this is numpy (isn't beautiful?):

result = numpy.argsort(numpy.argsort(array))

which is (almost) equivalent code.
See also my previous post on numpy tricks
PS. Once upon a time I will understand why all blogging platforms suggest heavy and ugly layout templates. Even adding background image turns into non-trivial task.

Numpy exercises

When one starts writing in python, the typical reaction is disappointment about how slow it is compared to any compilable language. After a while, you learn numpy and find out it's actually not so bad.

Having spent a month with numpy, I found out that many things can be written in it.
Having spent a year with it, I found out that almost any algorithm may be vectorized, though it's sometimes non-trivial.

I'm still quite disappointed about the most answers at stackoverflow, where people prefer plain python for any nontrivial thing.

For instance, you need to compute statistics of values in array. Well, you can sort array and keep track of initial position. Alternatively, you can do it in numpy-one-liner:

order_statistics = numpy.argsort(numpy.argsort(initial_array))

Don't believe? Check this!
Want to compute mean value over the group of events? Another one-liner:

means = numpy.bincount(group_indices, weights=values) / numpy.bincount(group_indices)

Writing oblivious decision tree in numpy is very simple and can be done really fast.

As a non-trivial problem: will you be able to write application of usual decision tree in pure numpy? For simplicity, you can first consider only trees with equal depth of all leaves.