Dear All
I'm a like matlab user (more specifically a Scilab one) for years, and because I've to deal with huge ascii files (with dozens of millions of lines), I decided to have a look to Python and Numpy, including vectorization topics.
Obviously I've been influenced by my current feedbacks.
I've a basic question concerning the current code: why it is necessary to transpose the column vector (still in the right format in my mind)? does it make sens?
Thanks
Paul
####################################
import numpy as np ## np = raccourci
## works with a row vector vect0 = np.random.rand(5); print vect0; print("\n") mat = np.zeros((5,4),dtype=float) mat[:,0]=np.transpose(vect0); print mat ## works while the vector is still in column i.e. in a right format, isn't it? vect0 = np.random.rand(5,1); print vect0; print("\n") mat = np.zeros((5,4),dtype=float) mat[:,0]=np.transpose(vect0); print mat ## does not work vect0 = np.random.rand(5,1); print vect0; print("\n") mat = np.zeros((5,4),dtype=float) mat[:,0]=np(vect0); print mat _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.python.org/mailman/listinfo/numpy-discussion |
There are a couple of interesting observations here. In your first bit, you have:
(or I prefer vect0.T). Did you happen to notice that this works too:
The transpose or the original work as well. Unlike Scilab, python’s arrays can be literally 1-dimensional. Not 5x1 but just 5, which doesn’t have a transpose, because it doesn’t have a 2nd dimension.
you can see that in vect0.shape
so np.random.rand(5) doesn’t make a row-vector but a length 5 array, which is different than np.random.rand(5,1) or np.random.rand(1,5). Thus, you have to make sure the shapes all work.
in your second example, with the column vector, you can also slice along the 2nd dimension without transposing like:
mat[:,0] seems to have shape of (5,) which is just length-5 array, so setting it equal to 1xN or Nx1 arrays seems to cause some issues.
- Brian On Jul 3, 2017, 15:57 -0400, [hidden email], wrote:
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Hi Brian First of all thanks for the answer, the explanations and the advices; as mentioned I've to think differently in order to code with efficiency. 1.: yes 'mat[:,0]=vect0' works fine and I understand why :-) 2. more generally, I've read some tutorials or presentations saying that Numpy is faster than the native Python; regarding the (huge) size of my matrices, vectorization, Numpy (and others) + optimzation of calls are the basics Thanks Paul Le 2017-07-04 01:04, Brian Blais a écrit :
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On Tue, Jul 4, 2017 at 1:32 AM, <[hidden email]> wrote:
Note that NumPy is not the best for large text files, for instance, pandas is faster. Also note that the custom here is bottom posting. And welcome to Python ;) <snip> Chuck _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.python.org/mailman/listinfo/numpy-discussion |
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