[Numpy-discussion] mixing arrays and matrices: squeeze yes, flattened no?

Travis Oliphant oliphant.travis at ieee.org
Tue Feb 21 11:13:02 EST 2006

Sven Schreiber wrote:

>Hi, sometimes I'm still struggling with peculiarities of numpy-arrays
>vs. numpy-matrices; my latest story goes like this:
>I first slice out a column of a 2d-numpy-array (a = somearray[:,1]). I
>can just manage to understand the resulting shape ( == (112,) ).
>Then I slice a column from a numpy-matrix b = somematrix[:,1] and get
>the expected (112,1) shape.
>Then I do what I thought was the easiest thing in the world, I subtract
>the two vectors: c = a - b
>I was very surprised by the bug that showed up due to the fact that
>c.shape == (112,112) !!
As you know this isn't a bug, but very expected behavior.  I don't see 
this changing any time soon.  Arrays are different than matrices.  
Matrices are always 2-d arrays while arrays can have any number of 

The default relationship between arrays and matrices is that 1-d arrays 
get converted to row-matrices (1,N).   Regardless of which convention is 
chosen somebody will be bitten by that conversion if they think in terms 
of the other default.   I don't see a way around that except to be 
careful when you mix arrays and matrices.

>Next, I try to workaround by b.squeeze(). That seems to work, but why is
>b.squeeze().shape == (1, 112) instead of (112,)?
Again the same reason as before.  A matrix is returned from b.squeeze() 
and there are no 1-d matrices.   Thus, you get a row-vector.   Use .T if 
you want a column vector.

>Then I thought maybe b.flattened() does the job, but then I get an error
>(matrix has no attr flattened). Again, I'm baffled.
The correct spelling is b.flatten()

And again you are going to get a (1,N) matrix out because of how 1d 
arrays are interpreted as matrices.

In short, there is no way to get a 1-d matrix because that doesn't make 
sense.  You can get a 1-d array using


>Second (preliminary) conclusion: I will paranoically use even more
>asmatrix()-conversions in my code to avoid dealing with those
>array-beasts ;-) and get column vectors I can trust...
>Is there a better general advice than to say: "numpy-matrices and
>numpy-arrays are best kept in separated worlds" ?
You can mix arrays and matrices just fine if you remember that 1d arrays 
are equivalent to row-vectors. 


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