Re: Difference between array( [1,0,1] ) and array( [ [1,0,1] ] )
Markos, I can explain the difference from a non-numpy point of view - I hope you will be able to see how this difference affects what you are trying to do in numpy. vector_1 is an np.array consisting of a three-element list, with the three elements being 1, 0 and 1. vector_2 is an np.array consisting (at the top level) of a one-element list, with that element (at this top level) being a three-element list, with these three elements (at the lower level) being 1, 0 and 1. Stephen. On Fri, Jun 21, 2019 at 7:29 AM Markos wrote: > > Hi, > > I'm studying Numpy and I don't understand the difference between > > >>> vector_1 = np.array( [ 1,0,1 ] ) > > with 1 bracket and > > >>> vector_2 = np.array( [ [ 1,0,1 ] ] ) > > with 2 brackets > > The shape of vector_1 is: > > >>> vector_1.shape > (3,) > > But the shape of vector_2 is: > > >>> vector_2.shape > (1, 3) > > The transpose on vector_1 don't work: > > >>> vector_1.T > array([1, 0, 1]) > > But the transpose method in vector_2 works fine: > > >>> vector_2.T > array([[1], > [0], > [1]]) > > > I thought that both vectors would be treated as an matrix of 1 row and 3 > columns. > > Why this difference? > > Any tip? > > Thank you, > Markos > -- > https://mail.python.org/mailman/listinfo/python-list > -- https://mail.python.org/mailman/listinfo/python-list
Re: Difference between array( [1,0,1] ) and array( [ [1,0,1] ] )
Every array in numpy has a number of dimensions, "np.array" is a function that can create an array numpy given a list. when you write vector_1 = np.array([1,2,1]) you are passing a list of number to thet function array that will create a 1D array. As you are showing: vector_1.shape will return a tuple with the sizes of each dimension of the array that is: (3,) Note the comma thta indicate that is a tuple. While if you write: vector_2 = np.array([[1,2,3]]) You are passing a list of list to the function array that will instruct it to crete a 2D array, even though the size of the first dimension is 1: vector_2.shape (1,3) It is still a tuple as you can see. Try: vector_3 = np.array([[1,2,3],[4,5,6]]) And you'll see that i'll return a 2D array with a shape: vector_3.shape (2,3) As the external list has 2 elements that is two sublists each with 3 elements. The vector_2 case is just when the external list has only 1 element. I hope it is more clear now. Cherrs, Il giorno venerdì 21 giugno 2019 08:29:36 UTC+2, Markos ha scritto: > Hi, > > I'm studying Numpy and I don't understand the difference between > > >>> vector_1 = np.array( [ 1,0,1 ] ) > > with 1 bracket and > > >>> vector_2 = np.array( [ [ 1,0,1 ] ] ) > > with 2 brackets > > The shape of vector_1 is: > > >>> vector_1.shape > (3,) > > But the shape of vector_2 is: > > >>> vector_2.shape > (1, 3) > > The transpose on vector_1 don't work: > > >>> vector_1.T > array([1, 0, 1]) > > But the transpose method in vector_2 works fine: > > >>> vector_2.T > array([[1], > [0], > [1]]) > > > I thought that both vectors would be treated as an matrix of 1 row and 3 > columns. > > Why this difference? > > Any tip? > > Thank you, > Markos -- https://mail.python.org/mailman/listinfo/python-list
Re: Difference between array( [1,0,1] ) and array( [ [1,0,1] ] )
Keep also in mind that numpy is quite different from Matlab. In Matlab every vaiable is a matrix of at least 2 dimensions. This is not the case of numpy (and is not the case in Fortran too). every array can have a different number of dimensions. The transposition of an array with just 1 dimension is not really meaningful. On the other hand most of the time is not needed. For example le have a matrix: a = np.array([[1,2],[1,1]]) and an array: b = np.array([0.1,0.2]) You can left multiply or right multiply it to this matrix without any need to transpose it (as you have to do in Matlab): a @ b array([0.5,0.3]) b @ a array([0.3,0.4]) Cheers, -- https://mail.python.org/mailman/listinfo/python-list
pip vs python -m pip?
64-bit Python 3.6.8 running on Windows with a virtual environment activated. "pip -v" reports 19.0.3 "python -m pip" reports 19.1.1 Is this behavior by design or a bug? My takeaway is that its better to run "python -m pip ..." vs "pip ..." when running pip related tasks. Thoughts? Malcolm -- https://mail.python.org/mailman/listinfo/python-list
Re: pip vs python -m pip?
you must be picking up pip from a different python installl (or virtualenv) than you are picking up python. Check your %PATH% On Fri, Jun 21, 2019 at 6:29 AM Malcolm Greene wrote: > 64-bit Python 3.6.8 running on Windows with a virtual environment > activated. > > "pip -v" reports 19.0.3 > "python -m pip" reports 19.1.1 > > Is this behavior by design or a bug? > > My takeaway is that its better to run "python -m pip ..." vs "pip ..." > when running pip related tasks. > > Thoughts? > > Malcolm > -- > https://mail.python.org/mailman/listinfo/python-list > -- https://mail.python.org/mailman/listinfo/python-list
How to force "python -m site" ENABLE_USER_SITE to false?
Any suggestions on how one can force the "python -m site" ENABLE_USER_SITE value to false? Is it possible to globally force this setting - across all users - when installing a system wide version of Python ... or via a command line option when starting a Python session? Motivation: When ENABLE_USER_SITE is true, packages can get accidentally installed in user specific Python\Python3XX\site-packages folder, overriding system packages and ... apparently (at least under Windows) ... virtual environment packages as well. Thank you, Malcolm -- https://mail.python.org/mailman/listinfo/python-list
Re: pip vs python -m pip?
> you must be picking up pip from a different python install (or virtualenv) > than you are picking up python. > Check your %PATH% That was our first guess. Only one version of Python installed on the system (we install on an empty, freshly serviced pack Windows VM). Only one version of python*.exe found via Explorer. This behavior observed across multiple Windows 2016 Enterprise servers and Windows 10 Professional desktops. Malcolm -- https://mail.python.org/mailman/listinfo/python-list
Re: pip vs python -m pip?
On 21/06/2019 15.27, Malcolm Greene wrote: > 64-bit Python 3.6.8 running on Windows with a virtual environment activated. > > "pip -v" reports 19.0.3 > "python -m pip" reports 19.1.1 > > Is this behavior by design or a bug? If the pip and python executables you're calling both live in the virtual environment, then it might be a bug > My takeaway is that its better to run "python -m pip ..." vs "pip ..." when > running pip related tasks. It's a good rule of thumb. When you have multiple versions of Python installed side-by-side, there can be a danger that you call the wrong 'pip' by accident. I don't think this should be a concern in a virtual environment though Thomas -- https://mail.python.org/mailman/listinfo/python-list
Re: pip vs python -m pip?
On Fri, Jun 21, 2019 at 11:55 PM Malcolm Greene wrote: > > > you must be picking up pip from a different python install (or virtualenv) > > than you are picking up python. > > Check your %PATH% > > That was our first guess. Only one version of Python installed on the system > (we install on an empty, freshly serviced pack Windows VM). Only one version > of python*.exe found via Explorer. > > This behavior observed across multiple Windows 2016 Enterprise servers and > Windows 10 Professional desktops. > Well, I'm pretty confident this isn't an actually desired behaviour, so to answer your original question, I'd say "bug" is the more accurate choice. That said, though... there are a LOT of ways that the Windows path can fail to pick up the correct 'pip'. Normally activating a venv should let you use "pip" to mean the right thing just as "python" does, but maybe something's cached? Are you doing this in cmd.exe, powershell, bash, or some other shell? ChrisA -- https://mail.python.org/mailman/listinfo/python-list
Re: How to force "python -m site" ENABLE_USER_SITE to false?
On Jun 21, 2019, at 8:49 AM, Malcolm Greene wrote: > > Any suggestions on how one can force the "python -m site" ENABLE_USER_SITE > value to false? > > Is it possible to globally force this setting - across all users - when > installing a system wide version of Python ... or via a command line option > when starting a Python session? >From StackOverflow: >https://stackoverflow.com/questions/25584276/how-to-disable-site-enable-user-site-for-an-environment -- Ed Leafe -- https://mail.python.org/mailman/listinfo/python-list
Re: How to force "python -m site" ENABLE_USER_SITE to false?
> From: Ed Leafe > StackOverflow: > https://stackoverflow.com/questions/25584276/how-to-disable-site-enable-user-site-for-an-environment Thanks Ed! My takeaway from the above article and our path going forward is to explictly force ENABLE_USER_SITE to false via Python's "-s" switch. Malcolm -- https://mail.python.org/mailman/listinfo/python-list
Re: pip vs python -m pip?
> From: Chris Angelico > Are you doing this in cmd.exe, powershell, bash, or some other shell? Same result via cmd.exe and PowerShell (ps1). > There are a LOT of ways that the Windows path can fail to pick up the correct > 'pip'. Normally activating a venv should let you use "pip" to mean the right > thing just as "python" does, but maybe something's cached? I think you're on to something. Running pip as a package (python -m pip) will force the use of the virtual env copy of pip. Running pip as an application vs package may use the system version of pip. Malcolm -- https://mail.python.org/mailman/listinfo/python-list
Re: pip vs python -m pip?
Malcolm> Running pip as a package (python -m pip) will force the use of the virtual env copy of pip. Running pip as an application vs package may use the system version of pip. I believe it is for just this reason that the recommended spelling these days is "python -m pip". Skip -- https://mail.python.org/mailman/listinfo/python-list
Re: Difference between array( [1,0,1] ) and array( [ [1,0,1] ] )
On 2019-06-21 02:39, Markos wrote: Hi, I'm studying Numpy and I don't understand the difference between vector_1 = np.array( [ 1,0,1 ] ) with 1 bracket and vector_2 = np.array( [ [ 1,0,1 ] ] ) with 2 brackets The shape of vector_1 is: vector_1.shape (3,) But the shape of vector_2 is: vector_2.shape (1, 3) The transpose on vector_1 don't work: vector_1.T array([1, 0, 1]) But the transpose method in vector_2 works fine: vector_2.T array([[1], [0], [1]]) I thought that both vectors would be treated as an matrix of 1 row and 3 columns. Why this difference? By similar reasoning, why not 3-dimensional (layer, row, column)? Or 4-dimensional? -- https://mail.python.org/mailman/listinfo/python-list
