amygdala | RESTful HTTP client for JavaScript powered web applications | REST library

Here's one method to use IQR on each column.

I find it typically very useful to come up with a simple "verb" function that can be easily tested and demonstrated, and then applied as many times as needed.

You need to define the same row names for top and anno:

I still cannot really understand how and where, but you showed that somewhere in the function you were able to build a float64 Series containing for example Left-Amygdala_Volume_mm3 as index and -0.29729 as value. And I assume that at the same time, you have the value of meand for the same index value.

More exactly I will assume:

Here is a solution:

Not a full answer, but it should address some of your concerns:

a) to isolate the first word of a string, leading up to the first .: use Parameter Expansions

If you split Sample_Name by body_site, you get a vector of Sample_Names corresponding to each body_site. Then you just need to intersect this with the names of NE for each body_site, and select the columns resulting from that intersection. The result is a named list of data tables. The names are the body_site values.

EDIT:

def get(self): may have some issues.

First, get may be reserved. Second, you aren't able to send self to that function. Change that to:

def get_service():

EDIT2:

You also need to import logging at the top of any page that uses it. And, you have not imported Flask and its components:

CloudML Engine is largely agnostic to how you write your TensorFlow programs. You provide a Python program, and the service executes it for you, providing it with some environment variables you can use to perform distributed training (if necessary), e.g., task index, etc.

census/tensorflowcore demonstrates how to do things with the "core" TensorFlow library -- how to do everything "from scratch", including using replica_device_setters, MonitoredTrainingSessions, etc.. This may be necessary sometimes for ultimate flexibility, but can be tedious.

Alongside the census/tensorflowcore example, you'll also see a sample called census/estimator. This example is based on a higher level library, which unfortunately is in contrib and therefore does not yet have a fully stable API (expect lots of deprecation warnings, etc.). Expect it to stabilize in a future version of TensorFlow.

That particularly library (known as Estimators) is a higher level API that takes care of a lot of the dirty work for you. It will parse TF_CONFIG for you and setup the replica_device_setter as well as handle the MonitoredTrainingSession and necessary Hooks, while remaining fairly customizable.

This is the same library that the wide and deep example you pointed to is based on and they are fully supported on the service.