I am using numpy. I have a matrix with 1 column and N rows and I want to get an array from with N elements.
For example, if i have M = matrix([[1], [2], [3], [4]]), I want to get A = array([1,2,3,4]).
To achieve it, I use A = np.array(M.T)[0]. Does anyone know a more elegant way to get the same result?
Thanks!
If you'd like something a bit more readable, you can do this:
A = np.squeeze(np.asarray(M))
Equivalently, you could also do: A = np.asarray(M).reshape(-1), but that's a bit less easy to read.
numpy.matrix is a bit of a contentious issue, but the numpy devs very much agree with you that having both is unpythonic and annoying for a whole host of reasons. However, the amount of old, unmaintained code "in the wild" that uses matrix makes it difficult to fully remove it.
sklearn). In fact there are different sparse matrix types in scipy, which allow efficient access via rows or columns. I imagine this may be an issue for merging the concepts of matrix and array. That said, I'm wondering whether there could be introduced a sparse array type as well and whether there are any plans for doing that. Any clues?
result = M.A1
https://numpy.org/doc/stable/reference/generated/numpy.matrix.A1.html
matrix.A1
1-d base array
You can try the following variant:
result=np.array(M).flatten()
A, = np.array(M.T)
depends what you mean by elegance i suppose but thats what i would do
np.array(M).ravel()
If you care for speed; But if you care for memory:
np.asarray(M).ravel()
Or you could try to avoid some temps with
A = M.view(np.ndarray)
A.shape = -1
First, Mv = numpy.asarray(M.T), which gives you a 4x1 but 2D array.
Then, perform A = Mv[0,:], which gives you what you want. You could put them together, as numpy.asarray(M.T)[0,:].
This will convert the matrix into array
A = np.ravel(M).T
ravel() and flatten() functions from numpy are two techniques that I would try here. I will like to add to the posts made by Joe, Siraj, bubble and Kevad.
Ravel:
A = M.ravel()
print A, A.shape
>>> [1 2 3 4] (4,)
Flatten:
M = np.array([[1], [2], [3], [4]])
A = M.flatten()
print A, A.shape
>>> [1 2 3 4] (4,)
numpy.ravel() is faster, since it is a library level function which does not make any copy of the array. However, any change in array A will carry itself over to the original array M if you are using numpy.ravel().
numpy.flatten() is slower than numpy.ravel(). But if you are using numpy.flatten() to create A, then changes in A will not get carried over to the original array M.
numpy.squeeze() and M.reshape(-1) are slower than numpy.flatten() and numpy.ravel().
%timeit M.ravel()
>>> 1000000 loops, best of 3: 309 ns per loop
%timeit M.flatten()
>>> 1000000 loops, best of 3: 650 ns per loop
%timeit M.reshape(-1)
>>> 1000000 loops, best of 3: 755 ns per loop
%timeit np.squeeze(M)
>>> 1000000 loops, best of 3: 886 ns per loop
Came in a little late, hope this helps someone,
np.array(M.flat)
