Hi, I need to know about the relative speed (i.e., which one is faster) of the followings: 1. list and numpy array, tuples and numpy array 2. list of tuples and numpy matrix (first one is rectangular) 3. random.randint() and numpy.random.random_integers() Thank you.
On 08/29/2013 09:33 AM, Anubhab Baksi wrote:
Hi, I need to know about the relative speed (i.e., which one is faster) of the followings: 1. list and numpy array, tuples and numpy array 2. list of tuples and numpy matrix (first one is rectangular) 3. random.randint() and numpy.random.random_integers()
African or European? It really depends on what you're doing with it. The ipython %timeit magic is pretty useful for answering that question. Note that the answer may change dramatically based on the size of the data set. -- Jonathan Niehof ISR-3 Space Data Systems Los Alamos National Laboratory MS-D466 Los Alamos, NM 87545 Phone: 505-667-9595 email: jniehof@lanl.gov Correspondence / Technical data or Software Publicly Available
On Thu, Aug 29, 2013 at 3:41 PM, Jonathan T. Niehof <jniehof@lanl.gov>wrote:
On 08/29/2013 09:33 AM, Anubhab Baksi wrote:
Hi, I need to know about the relative speed (i.e., which one is faster) of the followings: 1. list and numpy array, tuples and numpy array 2. list of tuples and numpy matrix (first one is rectangular) 3. random.randint() and numpy.random.random_integers()
Hi Anubhab, if you have a reasonably large amount of data (say O(100)), always try to use numpy arrays and not lists or tuples - it'll be faster. I'd recommend not to use numpy.matrix, it's speed will be similar to numpy arrays but it has some peculiarities that you'd rather not deal with. For the random numbers I'm not sure without checking, just timing it in ipython with %timeit is indeed the way to go. Cheers, Ralf
African or European?
Why on earth would you ask that?
It really depends on what you're doing with it. The ipython %timeit magic is pretty useful for answering that question. Note that the answer may change dramatically based on the size of the data set.
-- Jonathan Niehof ISR-3 Space Data Systems Los Alamos National Laboratory MS-D466 Los Alamos, NM 87545
Phone: 505-667-9595 email: jniehof@lanl.gov
Correspondence / Technical data or Software Publicly Available _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Thu, Aug 29, 2013 at 6:10 PM, Eric Moore <ewm@redtetrahedron.org> wrote:
African or European?
Why on earth would you ask that?
Its a Monty Python and the Holy Grail reference.
Thanks. I had read that quite differently, and I'm sure I'm not the only one. Some context would have helped.... Ralf
On 8/29/2013 3:48 PM, Ralf Gommers wrote:
Some context would have helped.
http://www.youtube.com/watch?v=y2R3FvS4xr4 fwiw, Alan
On 08/29/2013 01:48 PM, Ralf Gommers wrote:
Thanks. I had read that quite differently, and I'm sure I'm not the only one. Some context would have helped....
My apologies--that was a rather obtuse reference. In my oddly-wired brain it struck me as a fairly similar, suboptimally-posed question: all data structures sit in memory at the same speed, it's a question of the operations. And as you pointed out, most of the time for non-trivial datasets the numpy operations will be faster. (I'm daunted by the notion of trying to do linear algebra on lists of tuples, assuming that's the relevant set of operations given the comparison to the matrix class.) -- Jonathan Niehof ISR-3 Space Data Systems Los Alamos National Laboratory MS-D466 Los Alamos, NM 87545 Phone: 505-667-9595 email: jniehof@lanl.gov Correspondence / Technical data or Software Publicly Available
And as you pointed out, most of the time for non-trivial datasets the numpy operations will be faster. (I'm daunted by the notion of trying to do linear algebra on lists of tuples, assuming that's the relevant set of operations given the comparison to the matrix class.)
Note the important and pretty common exception of building up a list one element (or row of elements) at a time. Here, python lists usually rule, unless the final size is known in advance.
On Aug 29, 2013 4:11 PM, "Jonathan T. Niehof" <jniehof@lanl.gov> wrote:
On 08/29/2013 01:48 PM, Ralf Gommers wrote:
Thanks. I had read that quite differently, and I'm sure I'm not the only one. Some context would have helped....
My apologies--that was a rather obtuse reference.
Just for future reference, the language and the community is full of references like these. IDLE, is named for Eric Idle, one of the members of Monty Python, while Guido's title of BDFL is a reference to a sketch. But I am sure you'd never expected that... :-p Cheers! Ben Root
Thanks all, my client actually wants the output at a minimum time. On Thu, Aug 29, 2013 at 11:30 PM, Ralf Gommers <ralf.gommers@gmail.com>wrote:
if you have a reasonably large amount of data (say O(100)),
I need to deal with nearly 2**19 or 2**20 arrays of length about 250 each.
On Thu, Aug 29, 2013 at 11:30 PM, Ralf Gommers <ralf.gommers@gmail.com>wrote:
On Thu, Aug 29, 2013 at 3:41 PM, Jonathan T. Niehof <jniehof@lanl.gov>wrote:
On 08/29/2013 09:33 AM, Anubhab Baksi wrote:
Hi, I need to know about the relative speed (i.e., which one is faster) of the followings: 1. list and numpy array, tuples and numpy array 2. list of tuples and numpy matrix (first one is rectangular) 3. random.randint() and numpy.random.random_integers()
Hi Anubhab, if you have a reasonably large amount of data (say O(100)), always try to use numpy arrays and not lists or tuples - it'll be faster. I'd recommend not to use numpy.matrix, it's speed will be similar to numpy arrays but it has some peculiarities that you'd rather not deal with. For the random numbers I'm not sure without checking, just timing it in ipython with %timeit is indeed the way to go.
Cheers, Ralf
African or European?
Why on earth would you ask that?
It really depends on what you're doing with it. The ipython %timeit magic is pretty useful for answering that question. Note that the answer may change dramatically based on the size of the data set.
-- Jonathan Niehof ISR-3 Space Data Systems Los Alamos National Laboratory MS-D466 Los Alamos, NM 87545
Phone: 505-667-9595 email: jniehof@lanl.gov
Correspondence / Technical data or Software Publicly Available _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Fri, Aug 30, 2013 at 6:20 AM, Anubhab Baksi <anubhab91@gmail.com> wrote:
I need to deal with nearly 2**19 or 2**20 arrays of length about 250 each.
As mentioned elsewhere in this thread: what does "deal" mean. You may be better off with something like: http://kwant-project.org/tinyarray/ Stéfan
participants (8)
-
Alan G Isaac -
Anubhab Baksi -
Benjamin Root -
Eric Moore -
Jonathan T. Niehof -
Ralf Gommers -
Stéfan van der Walt -
Zachary Pincus