On Monday, September 22, 2014 1:12:23 PM UTC-4, Chris Angelico wrote: > On Tue, Sep 23, 2014 at 2:57 AM, LJ <luisjoseno...@gmail.com> wrote: > > > Quick question here. In the code I am working on I am apparently doing a > > lot of dictionary lookups and those are taking a lot of time. > > > I looked at the possibility of changing the structure and I found about the > > numpy structured arrays. > > > The concrete question is: what would be the difference between using one or > > the other in terms of performance? meaning looping and performing checks an > > operations on them? > > > > The lookups themselves almost certainly aren't actually taking the > > time, unless you have some kind of crazy hash collision happening, > > which is so statistically unlikely that it would have to have come > > from malice. What's the code doing? What are you trying to accomplish? > > It's much more likely that you have an algorithmic flaw than a data > > type inefficiency. > > > > ChrisA
Thank you for your reply. Basically what is happening is the following: I have a network in which the nodes are defined as dictionaries using the NetworkX package. Inside each node (each dictionary) I defined a dictionary of dictionaries holding attributes corresponding to different ways in which the node can be reached (this dictionaries I refer to as labels). At some point in my algorithm I am looping through some subset of nodes and through the labels in each node and I perform some "joining" checks with the labels of each node in another subset of nodes. To clarify I check for a feasibility condition in a pairwise manner for every label in one node and every label of another. This nested loop is taking time. I wonder if the fact of defining the labels using numpy instead of dictionaries could imply a faster way to perform this loop. I hope I was clear. Thanks. -- https://mail.python.org/mailman/listinfo/python-list
