Good morning -
Got a question for a mlab module guru.
After some experimentation (and judicious peeking at the source code), I
think I've got the hang of writing custom functions to pass into these
modules - basically, anything that accepts a list of values sliced from
a single column on the structured array and returns a single list seems
to work well. In functional programming terms, rec_summarize appears
similar to "map", rec_groupby appears similar to "reduce".
Now - what if I want to derive a calculation from multiple statistics in
the original dataset - eg. create a new column on the array which is
derived from 2 (or up to n) other fields in a custom function which I
pass into the process?
For example, conditional counts/summaries (count transactions and sum
the sales on all orders that weighed > 5K lbs).
Is there a way to do this within numpy or mlab without going all the way
out to python and creating a list comprehension?
Thanks.
John
------------------------------------------------------------------------------
All of the data generated in your IT infrastructure is seriously valuable.
Why? It contains a definitive record of application performance, security
threats, fraudulent activity, and more. Splunk takes this data and makes
sense of it. IT sense. And common sense.
http://p.sf.net/sfu/splunk-d2d-c2
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
