[Numpy-discussion] recarray slow?
Wed Jul 21 15:22:37 CDT 2010
However: is there an automatic way to convert a named index to a position?
What about looping over tuples of my recarray:
for t in d:
date = t['Date']
I guess that the above does have to lookup 'Date' each time.
But the following does not need the hash lookup for each tuple:
for t in d:
date = t
Should I create a map from dtype.names(), and use that to look up the
index based on the name in advance? (if I really really want to
factorize out the lookup of 'Date']
On Wed, Jul 21, 2010 at 3:47 PM, wheres pythonmonks
> Thank you very much.... better crack open a numpy reference manual
> instead of relying on my python "intuition".
> On Wed, Jul 21, 2010 at 3:44 PM, Pauli Virtanen <email@example.com> wrote:
>> Wed, 21 Jul 2010 15:12:14 -0400, wheres pythonmonks wrote:
>>> I have an recarray -- the first column is date.
>>> I have the following function to compute the number of unique dates in
>>> my data set:
>>> def byName(): return(len(list(set(d['Date'])) ))
>> What this code does is:
>> 1. d['Date']
>> Extract an array slice containing the dates. This is fast.
>> 2. set(d['Date'])
>> Make copies of each array item, and box them into Python objects.
>> This is slow.
>> Insert each of the objects in the set. Also this is somewhat slow.
>> 3. list(set(d['Date']))
>> Get each item in the set, and insert them to a new list.
>> This is somewhat slow, and unnecessary if you only want to
>> 4. len(list(set(d['Date'])))
>> So the slowness arises because the code is copying data around, and
>> boxing it into Python objects.
>> You should try using Numpy functions (these don't re-box the data) to do
>> this. http://docs.scipy.org/doc/numpy/reference/routines.set.html
>> Pauli Virtanen
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